BIOMINERALIZATION AND BIOSORPTION INVOLVING BACTERIA:
METAL PHOSPHATE PRECIPITATION AND MERCURY ADSORPTION EXPERIMENTS
A Dissertation
Submitted to the Graduate School
of the University of Notre Dame
in Partial Fulfillment of the Requirements
for the Degree of
Doctor of Philosophy
by
Sarrah M. Dunham-Cheatham
Jeremy B. Fein, Director
Graduate Program in Civil and Environmental Engineering and Earth Sciences
Notre Dame, Indiana
August, 2012
© Copyright 2012
Sarrah M. Dunham-Cheatham
BIOMINERALIZATION AND BIOSORPTION INVOLVING BACTERIA:
METAL PHOSPHATE PRECIPITATION AND MERCURY ADSORPTION EXPERIMENTS
Abstract
by
Sarrah M. Dunham-Cheatham
The research conducted in these chapters focused on the transport and fate of a range
of metals in the presence of bacteria. In Chapter 2, I investigated the effects of bacteria on the
precipitation of metal phosphates and discovered 2 phenomena, passive cell wall mineralization
and the decreased size of precipitated minerals due to the presence of bacteria. In Chapters 3
and 4, I investigated the effects of 2 ligands (chloride in Chapter 3, fulvic acid in Chapter 4) on
the adsorption behavior on mercury to bacterial cells. I learned from these studies that the
presence of ligands can have a range of effects on the adsorption behavior of mercury to
bacterial cells.
In all of my investigations, I used thermodynamic models to calculate stability constants
for several metal-bacteria complexes formed in my experiments. These stability constants can
be used to better predict the behavior of metals in metal-bacteria-ligand systems, which is
potentially beneficial to several applications (e.g. developing effective remediation strategies).
CONTENTS
Figures ...................................................................................................................................... iv
Tables ..................................................................................................................................... viii
Acknowledgments ......................................................................................................................ix
Chapter 1: Introduction............................................................................................................... 1
Chapter 2: The Effects of Non-Metabolizing Bacterial Cells on the Precipitation of Uranium, Lead
and Calcium Phosphates ................................................................................................... 8
2.1 Abstract .................................................................................................................... 8
2.2 Introduction ............................................................................................................. 9
2.3 Methods ................................................................................................................. 12
2.3.1 General approach .................................................................................... 12
2.3.2 Experimental methods ............................................................................ 13
2.3.3 Analytical methods .................................................................................. 19
2.3.4 Thermodynamic modeling ....................................................................... 24
2.4 Results & Discussion ............................................................................................... 29
2.4.1 Uranium system ...................................................................................... 29
2.4.2 Lead system ............................................................................................ 51
2.4.3 Calcium system ....................................................................................... 55
2.5 Conclusions ............................................................................................................ 60
2.6 Acknowledgements ................................................................................................ 61
Chapter 3: The Effects of Chloride on the Adsorption of Mercury onto Three Bacterial Species . 62
3.1 Abstract .................................................................................................................. 62
3.2 Introduction ........................................................................................................... 63
3.3 Methods ................................................................................................................. 65
3.3.1 Experimental Methods ............................................................................ 65
3.3.2 Analytical Methods: Inductively-Coupled Plasma – Optical Emission
Spectroscopy (ICP-OES) ....................................................................... 68
3.3.3 Thermodynamic Modeling....................................................................... 68
3.4 Results & Discussion ............................................................................................... 70
3.4.1 Potentiometric Titrations ........................................................................ 70
3.4.2 Adsorption Experiments .......................................................................... 76
3.4.3 Thermodynamic Modeling....................................................................... 77
3.5 Conclusions ............................................................................................................ 87
3.6 Acknowledgements ................................................................................................ 88
ii
Chapter 4: The Effect of Natural Organic Matter on the Adsorption of Mercury to Bacterial
Cells ................................................................................................................................ 89
4.1 Abstract .................................................................................................................. 89
4.2 Introduction ........................................................................................................... 90
4.3 Methods ................................................................................................................. 91
4.3.1 Experimental Methods ............................................................................ 91
4.3.2 Analytical Methods: Inductively Coupled Plasma – Optical Emission
Spectroscopy (ICP-OES) ....................................................................... 94
4.3.3 Thermodynamic Modeling....................................................................... 94
4.4 Results .................................................................................................................... 96
4.5 Discussion............................................................................................................... 99
4.6 Conclusions .......................................................................................................... 104
4.7 Acknowledgements .............................................................................................. 105
Chapter 5: Conclusions............................................................................................................ 106
Bibliography............................................................................................................................ 110
iii
FIGURES
Figure 1: XRD diffractogram from the isolated biotic precipitate used in the solubility
experiment. The upper diffractogram is from the reference HUP mineral. .................... 18
Figure 2: Elemental map of biotic U11 sample. P is shown in red, U is shown in green. The scale
bar is 500 nm. ............................................................................................................... 30
Figure 3: TEM bright field images for U system. (A) Abiotic U5 control; (B) Biotic U5 experiment;
(C) Abiotic U11 control; (D) Biotic U11 experiment. All scale bars are 200 nm. The
bacteria in (B) and (D) is B. subtilis. ............................................................................... 31
Figure 4: TEM bright field images for U system. (A) Biotic U10 experiment; (B) close up of area in
the black box in image A to illustrate the texture of the biogenic U nanoparticulate
precipitate; (C) Biotic U10 experiment; (D) close up of area located in black box in image
C; (E) Biotic U10 experiment; (F) close up of area located in the black box in image F. The
bacteria in all micrographs is B. subtilis. ........................................................................ 33
Figure 5: TEM bright field image of uranyl phosphate biomineralization in biotic (A) U5 and (B)
U11 samples, showing texture and prevalence of minerals within the S. oneidensis cell
walls. The scale bars represent (A) 200 nm, and (B) 100 nm.......................................... 34
Figure 6: XRD patterns from analysis of run products from U system experiments. ................... 36
Figure 7: k3-weighted EXAFS spectra of the biotic and abiotic samples plotted with the HUP
standard. Except for Biotic U5, which exhibits an adsorption spectrum, all spectra have
the small features around k = 10 Å-1, a signature feature of the autunite group. .......... 37
Figure 8: (A) Magnitude of U L3-edge EXAFS spectra after Fourier transformation for the abiotic
samples overlaid by the HUP standard; (B) Magnitude of U L3-edge EXAFS spectra after
Fourier transformation for the biotic samples overlaid by the HUP standard. Spectra
shown were collected in transmission mode. ............................................................... 39
Figure 9: EXAFS data and fit in the magnitude of the Fourier transformed EXAFS spectrum. ...... 40
Figure 10: Changes in the aqueous concentrations of U and P in the U experiments. (A) B.
subtilis; (B) S. oneidensis. All experiments were performed in triplicate (symbols
represent the mean). Error bars represent one standard deviation (note that some error
bars are smaller than the symbol). Each arrow connects the starting condition (arrow
tail, asterisks) to the final U and P concentrations in the abiotic control or biotic
experiments (arrow head, squares and circles). The numerals “1”, “2”, and “3” represent
iv
saturation state conditions discussed in detail in the text and are presented here for
reference...................................................................................................................... 45
Figure 11: Aqueous chemistry results for the bacterial exudate experiment (shown as hollow
triangles) compared to aqueous chemistry results for the U system (as shown in Figure
10A). Each arrow connects the starting condition (arrow tail, asterisks) to the final U and
P concentrations in the abiotic control or biotic experiments (arrow head, squares and
circles). The numerals “1”, “2”, and “3” represent saturation state conditions discussed
in detail in the text and are presented here for reference. ............................................ 48
Figure 12: Measured U and P concentrations from the solubility experiments involving biogenic
hydrogen uranyl phosphate (HUP) precipitates. Model P concentrations were fixed at
the average experimental value, and the model U line is the calculated U concentration
in equilibrium with macroscopic HUP, using the Ksp value reported by Gorman-Lewis et
al. (2009). ..................................................................................................................... 50
Figure 13: TEM bright field images for Pb system: (A) Biotic Pb4 experiment (scale bar is 200
nm); (B) Biotic Pb8 (scale bar is 100 nm). ...................................................................... 51
Figure 14: XRD patterns for biotic samples from the Pb system. The lower pattern is a reference
for Pb3(PO4)2. ................................................................................................................ 52
Figure 15: Changes in the aqueous concentrations of Pb and P in the Pb experiments with B.
subtilis. All experiments were performed in duplicate. Error bars represent one standard
deviation (note that some error bars are smaller than the symbol). Each arrow connects
the starting condition (arrow tail, asterisks) to the final Pb and P concentrations in the
abiotic control or biotic experiments (arrow head, squares and circles). The numerals
“1” and “2” represent saturation state conditions discussed in detail in the text and are
presented here for reference. ...................................................................................... 54
Figure 16: TEM bright field images for Ca system: (A) Abiotic Ca7 control; (B) Biotic Ca7
experiment; (C) Abiotic Ca11 control; (D) Biotic Ca11 experiment. All scale bars are 100
nm................................................................................................................................ 56
Figure 17: XRD data from run-products of Ca experiments. ....................................................... 57
Figure 18: Changes in the aqueous concentrations of Ca and P in the Ca experiments with B.
subtilis. All experiments were performed in duplicate. Error bars represent one standard
deviation (note that some error bars are smaller than the symbol). Each arrow connects
the starting condition (arrow tail, asterisks) to the final Ca and P concentrations in the
abiotic control or biotic experiments (arrow head, squares and circles). The numerals
“1” and “2” represent saturation state conditions discussed in detail in the text and are
presented here for reference. ...................................................................................... 59
Figure 19: Four replicate forward potentiometric titration of 100 gm L-1 G. sulfurreducens in 0.1
M NaClO4. .................................................................................................................... 73
v
Figure 20: Best fit 4-site model results (smooth curve) for one representative potentiometric
titration of G. sulfurreducens (data points). .................................................................. 74
Figure 21: Hg adsorption onto bacterial species normalized per gram of bacteria. The initial
molality of Hg in the adsorption experiments is 7.41 x 10-5. .......................................... 77
Figure 22: Hg adsorption onto bacterial species, normalized per gram of bacteria, in the
presence of chloride. The solid black curve represents the model fit for B. subtilis, the
dashed black line represents the model fit for S. oneidensis, and the solid grey line
represents the model fit for G. sulfurreducens. The initial molality of Hg in the
adsorption experiments is 7.41 x 10-5 and the initial molality of Cl is 1.00 x 10-3. ........... 78
Figure 23: Aqueous Hg speciation in the (A) absence and (B) presence of chloride under the
experimental Hg and chloride concentration conditions. Only species with calculated
concentrations above 0.01 x 10-5 M are shown. ............................................................ 79
Figure 24: Comparison of model fits (curves) to B. subtilis experimental data (solid squares) for
the adsorption of Hg according to Reaction(s): (5) only (dashed grey curve); (5) and (6)
(dotted black curve); and (5), (6), and (7) (solid black curve). ........................................ 83
Figure 25: Comparison of model fits (curves) to G. sulfurreducens experimental data (solid
squares) for the adsorption of Hg according to Reaction(s): (5) only (dashed grey curve);
(5) and (6) (dotted black curve); (5), (6), and (7) (long dashed grey curve); and (5), (6),
(7), and (8) (solid black curve). Using only Reactions (5) through (7), as was used for the
B. subtilis modeling, results in a model fit that poorly constrains the data at high pH,
indicating that another reaction is necessary to account for the observed Hg adsorption.
It is likely that Hg(OH)20 is involved in the high pH adsorption, as it is the dominant
aqueous Hg species at high pH. Adding Hg(OH)20 onto R-A41- (Reaction (8)) yields a model
fit that fits the data well across the entire pH range. .................................................... 84
Figure 26: Comparison of model fits (curves) to S. oneidensis experimental data (solid squares)
for the adsorption of Hg according to Reaction(s): (5) only (dashed grey curve); (5) and
(6) (dotted black curve); (5), (6), and (7) (long dashed grey curve); (5), (6), (7), and (8)
(solid grey curve); and (5), (6), (7), (8), and (9) (solid black curve). Using only Reactions
(5) through (8), the model does not constrain the high pH data well, thus an additional
surface species is necessary. It is likely that Hg(OH)20 is involved in the high pH
adsorption because it is the dominant aqueous Hg species under the high pH conditions
where we see a misfit between the data and the model predictions. Models invoking
Hg(OH)20 adsorption onto R-A31- or onto R-A41- do not improve the model fit, as these
reactions cause less HgCl(OH)0 to adsorb onto these sites due to site mass balance
constraints. However, a model that involves Hg(OH)20 adsorption onto R-A21- (solid black
curve) yields an excellent fit to the data across the pH range studied. .......................... 85
Figure 27: Aqueous chemistry results for Hg isotherms in the absence and presence of FA at pH
4 (A, B, C), pH 6 (D, E, F), and pH 8 (G, H, I). Plots A, D, and G present the results for the
FA-free controls, plots B, E, and H present the results for the 25 mg L-1 FA experiments,
and plots C, F, and I present the results of the 50 mg L-1 FA experiments. B. subtilis is
represented by the black-outlined, grey-filled squares, S. oneidensis is represented by
vi
the solid black diamonds, and G. sulfurreducens is represented by the hollow circles. The
black line on each plot represents 100% Hg adsorption under each experimental
condition. ..................................................................................................................... 98
Figure 28: Representative model fits for S. oneidensis at pH 6 under 0 mg L-1 FA (grey squares
and grey curve) and 50 mg L-1 FA (solid black diamonds and black curve) conditions. The
dotted line represents 100% Hg adsorption under each experimental condition......... 102
vii
TABLES
Table 1 Starting conditions for precipitation experiments (Uranium system) ............................. 20
Table 2 Starting conditions for precipitation experiments (Lead system) ................................... 21
Table 3 Starting conditions for precipitation experiments (Calcium system) .............................. 21
Table 4 System of equations used for saturation state and solubility calculations for uranium
system.......................................................................................................................... 26
Table 5 System of equations used for saturation state calculations for lead system .................. 27
Table 6 System of equations used for saturation state calculations for calcium system ............. 28
Table 7 Fitting paths and corresponding parameters used for XAS analysis ............................... 41
Table 8 Parameters for major fitting paths used in the fitting of XAS data ................................. 42
Table 9 Hg reactions used to construct SCMs ............................................................................ 72
Table 10 Site concentrations and pKa values used for SCMs ...................................................... 75
Table 11 Calculated stability constants (log K) for Hg adsorption onto bacteria ......................... 86
Table 12 Hg reactions used in the speciation modeling ............................................................. 97
Table 13 Calculated log stability constant values for Reactions (12) – (15) ............................... 101
viii
ACKNOWLEDGMENTS
I would like to acknowledge and thank my advisor, Dr. Jeremy B. Fein, without whom
this work would not have been possible. I would also like to acknowledge all of my collaborators
for their helpful contributions to this work, and the reviewers for their useful feedback.
I would like to thank my mother and sister for supporting me throughout my education,
my father for inspiring me to continue my education, and my grandparents for encouraging me
to become the best person that I can be.
ix
CHAPTER 1:
INTRODUCTION
Metals are mobile in groundwater under a range of environmental conditions. Binding
of metals to aqueous ligands (Xu and Allard, 1991; Bäckström et al., 2003; Croué et al., 2003),
colloids (Beveridge and Murray, 1976; Fowle et al., 2000), and mineral surfaces (BonnisselGissinger et al., 1999; Bäckström et al., 2003) in groundwater systems affects metal mobility and
transport through a number of processes. These processes, such as biomineralization
(Beveridge, 1989; Schultze-Lam et al., 1996; Bazylinski and Moskowitz, 1997), metal transport
(Fein et al., 1999; Moura et al., 2007), and bioavailability (Niyogi and Wood, 2004; van Leeuwen
et al., 2005), can control metal speciation and behavior in the environment. The ability to
predict the fate of a metal under supersaturated conditions and in the presence of a range of
natural constituents, such as ligands, colloids (e.g. bacteria and clays), natural organic matter
(NOM), and aqueous complexes, is crucial to a wide range of applications (e.g. predicting
contaminant transport and implementing remediation strategies). In order to predict the
behavior of a metal, we must first understand how it reacts with each component of a natural
system. This research investigates the behavior of metals in the presence of bacterial cell walls
and a range of naturally-occurring metal-binding ligands.
Bacteria are present in a wide range of geologic systems and are ubiquitous in nearsurface environments (Madigan et al., 2009). These organisms can affect the fate of
contaminant metals by creating localized super-saturated conditions and precipitating metals
1
through biomineralization processes (Lowenstam, 1981; Bazylinski and Moskowitz, 1997).
Bacteria can also affect the fate of metals through cell wall adsorption reactions (Beveridge and
Murray, 1976; Ledin et al., 1999). Through these reactions, aqueous metal cations, or charged
metal complexes, bind to negatively-charged deprotonated functional groups within the
bacterial cell wall matrix, tying the mobility of the metal to that of the bacterial cell to which it is
attached. For example, Pang et al. (2005) demonstrate that Cd is up to 20 times more mobile
when it is adsorbed to bacterial cells than when the metal is a free cation, indicating that the
mobility of a metal ion that is adsorbed to a bacteria cell is linked to the mobility of the bacteria.
If bacterial cells are immobile, however, metal binding onto bacterial cells can result in
decreased metal mobility.
Bacteria can also affect metal transport through a range of biomineralization processes.
These processes, such as biologically-induced and biologically-controlled mineralization, result in
the precipitation of metals from solution either from direct contact with bacteria cells or their
exudates (Beveridge 1989; Ghiorse and Ehrlich 1992; Southam and Beveridge 1992; Mandernak
et al., 1995; McLean et al., 1996; Warren et al., 2001; Perez-Gonzalez et al., 2010). Biologicallyinduced precipitation occurs when metal cations react with bacterial metabolic products causing
supersaturation and precipitation of a solid phase, whereas biologically-controlled precipitation
is the result of an organism expending energy to exert a direct control on the precipitation of a
metal cation for a specific purpose. For instance, Rivadeneyra et al. (2006) demonstrate that the
addition of magnesium and calcium to a carbonate-, phosphate-, and bacteria-bearing system
results in the precipitation of carbonate phases through biologically-induced mineralization
processes that are not observed in bacteria-free controls. The researchers attribute the
mineralization to the fact that the metabolism of the bacteria creates changes in pH, ionic
strength and ionic make-up of the local medium, which in turn creates favorable conditions for
2
magnesium and calcium adsorption to the bacterial cell wall. The adsorbed metal ions then
attract carbonate anions, which result from the metabolism of organic nutrients, beginning the
precipitation of calcium and magnesium carbonate phases on the bacterial cell wall.
Virtually all research investigating biomineralization has involved metabolizing bacteria.
However, bacteria exist under oligotrophic conditions in a wide range of natural systems (Billen
et al., 1990; Noe et al., 2001). A number of studies (e.g. Ferris et al., 1987; Lowenstam & Weiner,
1989; Châtellier et al., 2001; Ben Chekroun et al., 2004; Beazley et al., 2007; Dupraz et al., 2009)
have proposed that the functional groups on the cell walls of bacteria can act as nucleation sites
for the non-metabolic precipitation of minerals, leading to a third type of biomineralization
which I refer to as passive biomineralization. Despite these claims in the literature, the evidence
in support of passive biomineralization is equivocal. Studies have shown associations between
bacterial cells and mineral precipitates (e.g. Konhauser et al., 1993), but a spatial association
itself does not prove that the cell wall caused the mineral precipitation; the association could be
a result of electrostatic interactions between previously precipitated minerals and the cells.
Despite the growing number of claims, no study to date has unequivocally demonstrated that
the process of passive binding of metal cations to cell wall ligands affects mineral precipitation
or that cell wall nucleation of precipitates can occur. Chapter 2 presents research that
unequivocally demonstrates the ability of cell walls to passively nucleate the precipitation of
minerals within the cell wall matrix under some saturation state conditions and for some
elements.
Metal transport in groundwater systems can also be affected by the adsorption of
aqueous metal cations onto charged surfaces (e.g., bacterial cell walls) and by the formation of
aqueous complexes. The adsorption of a wide range of metals onto bacterial cells has been
studied (e.g. Beveridge and Murray, 1976, 1980; Beveridge, 1989; Mullen et al., 1989; Fein et al.,
3
1997, 2002; Borrok et al., 2004, 2007; Wu et al., 2006). The cell wall of a bacterium contains
proton-active functional groups, such as carboxyl, phosphoryl, hydroxyl, amino, and sulfhydryl
groups (Beveridge and Murray, 1976; Degens and Ittekkot, 1982; Guiné et al., 2006; Madigan et
al., 2009; Mishra et al., 2009, 2010). When deprotonated, these functional groups have the
ability to adsorb cations (e.g. metals, aqueous complexes) from solution (Beveridge and Murray,
1976; Ledin et al., 1996; Fortin and Beveridge, 1997; Warren and Ferris, 1998; Ohnuki et al.,
2005; Borrok et al., 2007). It has been shown that adsorption of metals to bacterial surfaces is
rapid (Fowle and Fein, 2000; Yee et al., 2000), dependent on solution pH (Fein, 2006), and
reversible (Fowle and Fein, 2000). In addition to affecting metal mobility, metal adsorption likely
represents the first step in bioavailability of metals to bacteria. According to the Biotic Ligand
Model, the bioavailability of toxic metals, such as Hg, is a result of the adsorption of the metal to
a biological surface of the living organism (Di Toro et al., 2001; Santore et al., 2001; Paquin et al.,
2002; Niyogi and Wood, 2004; van Leeuwen et al., 2005). Thus, it is important to construct
quantitative models of Hg adsorption onto bacteria that are capable of accounting for Hg
partitioning under a range of conditions of geologic and environmental interest. Mercury is of
particular interest because it might exhibit different aqueous complexation behavior and/or
form different types of bonds than other previously studied metals. For instance, because it is a
B-type metal, Hg has a high affinity to bond with sulfur ligands (Reddy and Aiken, 2000;
Ravichandran et al., 2004). Because bacterial cell walls contain sulfhydryl functional groups
(Mishra et al., 2009, 2010) and natural organic matter contains sulfur compounds (Haitzer et al.,
2003; Hertkorn et al., 2008), the affinity of Hg for sulfur compounds may have a significant
effect on the behavior of Hg adsorption behavior in the presence of bacteria and natural organic
matter.
4
Quantitative models have been developed and extensively used to quantify metal
adsorption onto bacterial surfaces. Many researchers utilize empirical models to quantify the
extent of metal adsorption to a surface. Dissociation constants, Kd values, used in empirical
models are affected by any change to a system parameter, including pH, ionic strength, and fluid
composition, and thus the results cannot be applied to conditions other than those studied
directly in the laboratory (Bethke and Brady, 2000; Koretsky, 2000). Other researchers have
used surface complexation models to calculate equilibrium constants for metal binding to
bacterial surfaces (Plette et al., 1995; Fein et al., 1997; Cox et al., 1999; Fowle et al., 2000; Fein
et al., 2001; Yee and Fein, 2001). This type of modeling approach can account for effects of
changing pH, solution composition, and solute:sorbent ratios because the approach explicitly
accounts for the reactions that occur on bacterial surfaces and within the aqueous phase;
however, applying a surface complexation model can be difficult due to the necessity of
obtaining or calculating a stability constant for each of the metal-bacteria surface complexes
that occur in the system of interest. Currently, theoretical models of metal bioavailability involve
simplistic and unrealistic representations of metal binding onto organisms (e.g., the Biotic
Ligand Model) (Di Toro et al., 2001; Santore et al., 2001; Paquin et al., 2002; Niyogi and Wood,
2004; van Leeuwen et al., 2005). Improvements in these models requires a more sophisticated
and accurate understanding of the binding of metals of environmental interest, especially in
complex geologic systems that may contain competing ligands, such as NOM or colloids (Ledin
et al., 1999; Daughney et al., 2002; Moura et al., 2007). Chapter 3 presents research that
investigates the effects of chloride on the adsorption of mercury onto a range of bacterial
species and provides thermodynamic equilibrium constants for mercury adsorption onto the
bacterial cell wall functional groups as calculated by surface complexation models. Chapter 4
5
presents research that examines the effects of fulvic acid on the adsorption behavior of mercury
to a range of bacterial species.
Despite the growing body of research aimed at determining the effect of bacteria on the
environmental fate of metals in groundwater systems, a number of key questions remain
unanswered. The research in this dissertation answers some of these questions. In Chapter 2, I
investigate the effects of non-metabolizing bacteria on the precipitation of metal phosphates.
The results show that non-metabolizing bacteria can passively precipitate uranyl phosphate
nanoparticles within the cell wall matrix from over-saturated conditions, but do not lead to
passive precipitation of lead phosphates or calcium phosphates. Additionally, non-metabolizing
bacteria control the size of the precipitate formed in both the uranyl and calcium systems,
precipitating smaller particles in the biotic samples relative to the abiotic controls. In Chapter 3,
I probe the effects of three species of bacteria and chloride on the adsorption behavior of
mercury. The results show that each bacterial species has an extremely high binding affinity for
mercury in both the absence and presence of chloride, more so than has been observed for
other metals. More importantly, the adsorption behavior of mercury to bacterial cells in both
the absence and presence of chloride does not exhibit typical cation adsorption behavior as a
function of pH, and I construct a surface complexation model that accounts for this unique
behavior. Chapter 4 presents my study of the effects of fulvic acid on the adsorption behavior of
mercury in the presence of a range of bacterial species. The experiments show that the
presence of fulvic acid results in high aqueous mercury concentrations relative to FA-free
controls. These findings suggest that fulvic acid competes with bacterial surfaces for mercury
ions and results in higher concentrations of available mercury relative to FA-free systems.
Surface complexation models were constructed to calculate Hg-bacteria binding equilibrium
constants for results from both Chapters 3 and 4; these binding constants can be used in future
6
studies to predict the behavior of Hg under environmental conditions in the presence of
bacteria. In general, the results of my dissertation research expand our understanding of the
effects of bacteria on the environmental fate and speciation of some key metals in groundwater
systems, and the results can be used to not only model the mobility of those metals but also to
guide remediation strategies aimed at removing those metals from contaminated systems.
7
CHAPTER 2:
THE EFFECTS OF NON-METABOLIZING BACTERIAL CELLS ON THE
PRECIPITATION OF URANIUM, LEAD AND CALCIUM PHOSPHATES
2.1 Abstract
In this study, I tested the potential for passive cell wall biomineralization by determining
the effects of non-metabolizing bacteria on the precipitation of uranyl, lead, and calcium
phosphates from a range of over-saturated conditions. Experiments were performed using
Gram-positive Bacillus subtilis and Gram-negative Shewanella oneidensis MR-1. After
equilibration, the aqueous phases were sampled and the remaining metal and P concentrations
were analyzed using inductively coupled plasma-optical emission spectroscopy (ICP-OES); the
solid phases were collected and analyzed using X-ray diffractometry (XRD), transmission
electron microscopy (TEM), and X-ray absorption spectroscopy (XAS).
At the lower degrees of over-saturation studied, bacterial cells exerted no discernible
effect on the mode of precipitation of the metal phosphates, with homogeneous precipitation
occurring exclusively. However, at higher saturation states in the U system, I observed
heterogeneous mineralization and extensive nucleation of hydrogen uranyl phosphate (HUP)
mineralization throughout the fabric of the bacterial cell walls. This mineral nucleation effect
was observed in both B. subtilis and S. oneidensis cells. In both cases, the biogenic mineral
precipitates formed under the higher saturation state conditions were significantly smaller than
those that formed in the abiotic controls.
8
The cell wall nucleation effects that occurred in some of the U systems were not
observed under any of the saturation state conditions studied in the Pb or Ca systems. The
presence of B. subtilis significantly decreased the extent of precipitation in the U system, but
had little effect in the Pb and Ca systems. At least part of this effect is due to higher solubility of
the nanoscale HUP precipitate relative to macroscopic HUP. This study documents several
effects of non-metabolizing bacterial cells on the nature and extent of metal phosphate
precipitation. Each of these effects likely contributes to higher metal mobilities in geologic
media, but the effects are not universal, and occur only with some elements and only under a
subset of the conditions studied.
2.2 Introduction
Mineral precipitation reactions affect the mobility and distribution of mass in a wide
range of geochemical systems. Bacteria are ubiquitous in near-surface environments, and can
control precipitation reactions in these systems through a number of biomineralization
mechanisms. Two general classifications of biomineralization reactions have been described
(Lowenstam, 1981; Bazylinski and Moskowitz, 1997): biologically-induced mineralization (BIM)
and biologically-controlled mineralization (BCM), both of which are driven by bacterial
metabolic processes. In BIM, precipitation is not directly controlled by the organism, but occurs
in response to interactions between elements in bulk solution and metabolic exudates from the
organism. For example, sulfate-reducing bacteria produce sulfide, which can react with aqueous
Zn when released from the cell to precipitate extracellular sphalerite (ZnS) (Labrenz et al., 2000).
In BCM, organisms expend energy to exert a direct control on precipitation, and the biominerals
are used for a specific function and are typically located within a cell. For example,
9
magnetotactic bacteria promote the internal formation of magnetite crystals for use as a
navigational aide (Lefevre et al., 2009; Yu-Zhang et al., 2009).
There has been considerable speculation that a third type of biomineralization reaction,
non-metabolic passive cell wall nucleation of minerals, occurs and that this process, integrated
over time for the bacterial biomass in soils and surface water systems, represents a significant
vector for transformation of aqueous ions to clay minerals and other inorganic and organic
phases (e.g., Urrutia and Beveridge, 1994; Schultze-Lam et al., 1996). Both field (Ferris et al.,
1987; Konhauser et al., 1993; Bonny and Jones, 2003; Fortin and Langley, 2005; Demergasso et
al., 2007) and laboratory (Macaskie et al., 2000; Warren et al., 2001; Rivadeneyra et al., 2006)
studies have examined mineral formation in super-saturated systems and have found a close
spatial association between bacterial cells and a range of extracellular precipitated mineral
phases. Despite the increasing number of studies to claim the importance of passive cell wall
biomineralization (Lowenstam and Weiner, 1989; Châtellier et al., 2001; Ben Chekroun et al.,
2004; Beazley et al., 2007; Dupraz et al., 2009), the nature of the evidence to date is equivocal. A
range of studies have documented associations between bacterial cells and mineral precipitates
(Konhauser, 1997; Arp et al., 1998; Douglas and Beveridge, 1998; Konhauser, 1998; Warren et
al., 2001; Perez-Gonzalez et al., 2010), but a spatial association in and of itself does not prove a
role of the cell wall in the precipitation reaction. Spatial associations between cells and
precipitates that form away from the cells can be promoted through electrostatic attraction
between cells and precipitates (Ams et al., 2004). Although passive binding of aqueous cations
to anionic sites located within bacterial cell walls can affect the speciation and distribution of
metals in bacteria-bearing systems (Beveridge and Murray, 1976; Fein et al., 1997; Kulczycki et
al., 2002; Deo et al., 2010; Li and Wong, 2010), no study has demonstrated that this process
affects mineral precipitation or that cell wall nucleation of precipitates can occur.
10
In addition to possible cell wall influences on precipitation, bacteria may influence
mineral precipitation by exuding a range of organic molecules. For example, organic molecules
exuded by biofilms widely affect the precipitation of calcite, influencing not only the growth
kinetics, but the morphology as well (Mann et al., 1990: Archibald et al., 1996; McGrath, 2001;
Meldrum and Hyde, 2001; Braissant et al., 2003; Hammes et al., 2003; Tong et al., 2004; Bosak
and Newman, 2005; Dupraz et al., 2009), likely through incorporation effects (Lowenstam and
Weiner, 1989). Studies have also shown that various organic molecules widely affect the
structure and morphology of a range of minerals, including numerous iron oxides (Châtellier et
al., 2001; Châtellier et al., 2004; Larese-Casanova et al., 2010; Perez-Gonzalez et al., 2010),
uranyl phosphate (Macaskie et al., 2000), and silica (Williams, 1984).
In this study, I probed the role of non-metabolizing bacteria in the formation of metal
phosphate minerals from over-saturated solutions. I selected U, Pb, and Ca in order to
investigate metals that exhibit a broad range of binding affinities with phosphorus. In general,
authigenic precipitation of minerals from saturated solutions in bacteria-rich settings is an
important geochemical process in a number of natural and engineered geological systems, so it
is crucial to understand bacterial effects on the precipitation reactions in order to model mass
transport in these systems. For example, the exposure of Fe(II)-bearing anaerobic groundwaters
to oxidizing bacteria-bearing conditions leads to Fe(III)-oxide precipitation and coating of
mineral grains which is ubiquitous in subsurface environments (Schwertmann et al., 1985;
Sullivan and Koppi, 1998). Phosphate systems are of particular interest due to the importance of
P cycles and the low solubilities of many metal-phosphate phases. Reduction of Fe(III)-oxides by
iron-reducing bacteria releases Fe(II) to solution and can lead to the precipitation of vivianite
(Fe3(PO4)2·8H2O), which is a major sink for Fe and for heavy metals in fresh water sedimentary
systems (Taylor and Boult, 2007); anthropogenic contamination of groundwater and soil
11
systems can lead to precipitation (or co-precipitation) of heavy metals as oxides and phosphate
phases in these systems (e.g., Kirpichtchikova et al., 2006; Manceau et al., 2007; Terzano et al.,
2007); and remediation strategies such as phosphate amendments rely on precipitation
reactions in bacteria-bearing systems to reduce concentrations of dissolved metals in systems,
such as those contaminated with dissolved U (e.g., Beazley et al., 2007; Martinez et al., 2007;
Wellman et al., 2007; Ndiba et al., 2008) or by acid mine drainage (e.g., Schultze-Lam et al.,
1996). The common denominator between all of these systems is the precipitation of phosphate
and other mineral phases in environments that can be rich in non-metabolizing bacterial cells
and/or bacterial exudates. Though most natural systems may not attain the degrees of
supersaturation investigated in this study, some may, including mid-ocean ridge hydrothermal
systems (Dekov et al., 2010), and groundwater mixing zones where ferrous iron oxidizes and
precipitates as ferric oxide coatings (James and Ferris, 2004).
The objective of this study was to determine if, and under what conditions, the presence
of non-metabolizing bacteria or bacterial exudates can influence precipitation reactions. My
experimental results can be used, therefore, to determine if the mobilities of the precipitating
elements are likely to be markedly different than they would be if the precipitation occurred
without bacteria present.
2.3 Methods
2.3.1 General approach
I measured the nature and extent of metal phosphate precipitation as a function of
aqueous saturation state in systems that contained suspensions of non-metabolizing cells of
either Bacillus subtilis (ATCC 23875) or Shewanella oneidensis MR-1 (ATCC BAA-1096),
12
comparing the results to those of abiotic controls. In the experiments, I created a range of oversaturated solutions by adding various concentrations of P in the form of Na2HPO4 to solutions
containing dissolved U, Pb, or Ca in 0.1 M NaClO4 in which washed, non-metabolizing bacterial
cells were suspended. I sampled the aqueous phase and analyzed for total remaining metal and
P in solution using ICP-OES. In addition, I characterized the solid phase of each system using
TEM, XRD, and XAS.
2.3.2 Experimental methods
2.3.2.1 Bacterial preparation
Bacillus subtilis and S. oneidensis cells were grown aerobically in 5 mL of trypticase soy
broth medium with 5% yeast extract for 24 hours at 32 oC. The cells were then transferred to 1 L
of trypticase soy broth medium with 5% yeast extract and incubated at 32 oC for another 24
hours. The cells were then collected via centrifugation at 8100g for 5 min. The resulting pellet
was washed five times with 0.1 M NaClO4 (following a procedure described in more detail by
Borrok et al., 2007), and pelleted after each wash using the centrifugation method described
above. After five washes, the pellet was centrifuged for 1 hour at 8100g to remove all excess
liquid and to obtain a wet biomass value.
2.3.2.2 Kinetics experiments
Kinetics experiments were performed to determine the time required for the metal and
P concentrations in the experiments to reach steady state. Precipitation experiments were
prepared according to the method described below. Aqueous samples were extracted from
each precipitation kinetics experiment at 0.25, 0.5, 1, 2, 4, 6, 18, 24, and 48 hours. The samples
were filtered through 0.2 μm PTFE syringe filters, acidified using trace metal grade 15.8 N HNO3
13
at a sample:acid ratio of 5 mL:8 μL, and refrigerated pending ICP-OES analysis. Results (not
shown) indicated that no change in metal or P concentration occurred after 2 hours in the
abiotic controls and the B. subtilis experiments, and after 3 hours in the S. oneidensis
experiments; all subsequent abiotic controls and B. subtilis experiments were allowed to react
for 2 hours, and subsequent S. oneidensis experiments were allowed to react for 3 hours.
2.3.2.3 Batch precipitation experiments
To prepare the experiments, aqueous metal, P, and suspended bacteria parent solutions
were mixed in different proportions to achieve the desired final concentrations. A 10-3.08 M U
parent solution was prepared in a Teflon bottle by dissolving UO2(NO3)2 in 0.1 M NaClO4; a 10-2.30
M Ca parent solution was prepared in a Teflon bottle by dissolving Ca(ClO4)2(H2O)4 in 0.1 M
NaClO4; and a 10-3.02 M Pb parent solution was prepared in a Teflon bottle by diluting a
commercially-supplied 1000 ppm aqueous Pb standard (in which the Pb is dissolved in 2% HNO3)
using 0.1 M NaClO4; a 10-2.19 M P parent solution was prepared in a Teflon bottle by dissolving
Na2HPO4 in 0.1 M NaClO4. A 6.25 gm (wet mass) L-1 bacterial parent solution was prepared by
suspending a known mass of washed, non-metabolizing bacterial cells in 0.1 M NaClO4.
Each experimental system was prepared by adding a weighed mass of bacterial parent
suspension, followed by a weighed mass of the U, Ca, or Pb parent solution, to 0.1 M NaClO4 in
Teflon tubes to achieve the desired concentrations. The final parent solution to be added was
the P one. In the U experiments, the initial U concentration was 10-4.20 M and the initial P
concentrations ranged from 10-5.50 to 10-3.50 M. In the Pb experiments, the initial Pb
concentration was 10-4.20 M and the initial P concentrations ranged from 10-5.50 to 10-3.50 M. The
initial Ca concentration in all Ca experiments was 10-3.00 M and the initial P concentrations
ranged from 10-5.00 to 10-2.00 M. The bacterial concentration for all biotic experiments ranged
14
from 0.31 gm wet biomass L-1 to 2.50 gm wet biomass L-1 (the bacterial concentration for all
results presented hereafter was 0.62 gm wet biomass L-1, unless otherwise noted), and the
abiotic controls were conducted with identical metal and P concentrations to those used in the
biotic experiments, but with no bacteria present. Cells were assumed to be non-metabolizing
due to the lack of nutrients and electron donors in the suspensions; however, no direct
confirmation of their metabolic state was performed. Inactivated cells could not be used as
controls due to likely changes to cell wall chemistry and/or structure that accompany any
passivation procedure.
After the P parent solution was added to each metal-bearing bacterial solution, the pH
of each experiment was adjusted immediately to the desired pH using 0.2 M HNO3 and/or 0.2 M
NaOH. The final pH values of the U, Pb and Ca systems were 4.50±0.10, 6.00±0.10, and
8.00±0.20, respectively. The pH of each experimental system was adjusted manually every 15
minutes throughout each experiment to maintain the desired pH, except for the last thirty
minutes during which the experiments were undisturbed. In general, the pH drifted slightly
toward circum-neutral values, but only minor adjustments, if any, were necessary after the first
hour of each experiment. The suspensions were constantly agitated on an end-over-end rotator
at 40 rpm for the duration of the experiment. After the prescribed equilibration time, all
suspensions were centrifuged at 8100g for 5 minutes. The supernatant was filtered through 0.2
μm PTFE syringe filters, acidified using trace metal grade 15.8 N HNO3 at a sample:acid ratio of 5
mL:8 μL, and refrigerated pending ICP-OES analyses. The solid phase was maintained at 4 oC
pending XRD, TEM, and XAS analysis. All U and Pb experiments were conducted under
atmospheric conditions, and all Ca experiments were conducted in a N 2/H2 atmosphere in order
to exclude atmospheric CO2 and to prevent possible calcium carbonate precipitation. All
experiments were performed in triplicate by conducting three independent experiments.
15
2.3.2.4 Precipitation experiments using bacterial exudate solution
A solution containing bacterial exudate molecules with no cells present was prepared in
the following manner: B. subtilis cells were added to 0.1 M NaClO4 to reach a concentration of
0.62 gm wet biomass L-1. The pH of the suspension was adjusted to 4.50 ± 0.10 using small
amounts 0.2 M HCl and/or 0.2 M NaOH. The pH was monitored every 15 minutes and
adjustments were made for two hours. The suspension was then centrifuged at 8100g for 10
minutes to remove all bacteria from solution. An aliquot of the supernatant was immediately
collected, filtered through a 0.2 μm PTFE syringe filter, and acidified using 15.8 N HNO3 at a
sample:acid ratio of 5 mL:8 μL. This sample was analyzed with ICP-OES to determine the starting
concentration of P in the exudate solution and with a total organic carbon (TOC) analyzer to
determine the concentration of dissolved carbon in the solution. The resulting concentrations
were 10- 5.41 ± 0.74 M P and 10-2.71 ± 0.17 ppm C. The remainder of the supernatant was then used in
place of the 0.1 M NaClO4 in an abiotic control precipitation experiment for the U system only.
At the completion of the experiment, samples were collected and analyzed as described above.
2.3.2.5 Biogenic mineral isolation
As describe below, the U experiments were the only ones to yield cell wall-nucleated
biomineralization under some of the conditions studied. In order to measure the solubility of
these precipitates in separate experiments, I isolated the particles from their cell wall
framework using a procedure similar to the one described by Ulrich et al. (2008). Biotic U
precipitation experiments were prepared according to the above method using B. subtilis cells.
After the prescribed equilibration time, the biomass was centrifuged for 5 minutes at 8100g, and
the supernatant was decanted. The bacteria/mineral pellet was re-suspended in a 20% bleach
solution, diluted with 18 MΩ ultrapure water, and placed on a rotating table at 32 oC overnight.
16
The suspension was centrifuged for 10 minutes at 8100g and decanted. The pellet was then
rinsed three times with 18 MΩ ultrapure water, until the pH of the wash supernatant was
circum-neutral, centrifuging for 10 minutes at 8100g and decanting between each rinse. The
pellet was suspended in 10 mL of 18 MΩ ultrapure water, transferred into a 60 mL separatory
funnel, and 50 mL of hexane was added to separate the organic debris from the minerals. The
funnel was capped and shaken vigorously for 3 minutes, then left undisturbed overnight. The
water portion was collected, centrifuged for 10 minutes at 8100g, and the supernatant was
decanted. The pellet was rinsed once with 18 MΩ ultrapure water, then centrifuged for 10
minutes at 8100g and decanted. The bleach/hexane process was repeated until no bacterial
remnants were present in the collected sample as determined by optical microscopy. Once the
biogenic minerals were isolated, the pellet was washed a final time with 18 MΩ ultrapure water,
centrifuged for 10 minutes at 8100g, the supernatant was decanted, and the particles were
allowed to air dry. XRD analysis of the biogenic minerals suggested that the minerals were
unaffected by the bleach/hexane treatment, and that they had the same crystal structure as the
precipitates that formed in the parallel abiotic controls (Figure 1). Scanning electron microscopy
(SEM) analysis showed that the minerals were needle-like with a length ranging from 10 to 30
nm.
2.3.2.6 Solubility experiments
Separate solubility experiments were performed using the isolated and washed biogenic
HUP particles. A known mass of the dry mineral powder was transferred to a Teflon tube and 18
MΩ ultrapure water was added to reach a concentration of 3 gm L-1. Small aliquots of 0.2 M
HNO3 or 0.2 M NaOH were added to adjust the pH of the solution to 4.20 ± 0.10. The pH of the
17
Figure 1: XRD diffractogram from the isolated biotic precipitate used in
the solubility experiment. The upper diffractogram is from the reference
HUP mineral.
solution was adjusted every hour in the first 24 hours until the pH value remained within the
desired range. A 2 mL sample was extracted after 24 hours, and every 48 hours after that for a
total of 23 days. After extraction, samples were filtered immediately through 0.2 μm PTFE
syringe filters, gravimetrically diluted with 18 MΩ ultrapure water, acidified using trace metal
grade 15.8 N HNO3 at a sample:acid ratio of 5 mL:8 μL, and refrigerated pending ICP-OES
analysis of dissolved U and P concentrations.
18
2.3.3 Analytical methods
2.3.3.1 TEM
Using TEM, I examined the solid phase run products from both abiotic and biotic
samples, and from a high and low saturation state for each metal system studied. For the U
system, the P concentration conditions studied with TEM were 10-4.49 (sample U5), 10-3.89 (U8),
10-3.65 (U10), and 10-3.49 M (U11) (Table 1); for the Pb system, the P concentration conditions
studied were 10-4.49 (Pb4), 10-3.79 (Pb6), 10-3.65 (Pb7), and 10-3.49 M (Pb8) (Table 2) ; for the Ca
system, the P concentration conditions studied were 10-3.09 (Ca4), 10-2.49 (Ca7), and 10-2.01 M
(Ca11) (Table 3). At the completion of each precipitation experiment, the pellet was suspended
in a 2% gluteraldehyde fixative solution. The suspension was rotated end-over-end for 1 hour,
then centrifuged and decanted. The pellet was rinsed three times with 18 MΩ ultrapure water.
The suspension was suspended in a 0.2% OsO4 fixative solution and rotated end-over-end for 1
hour, then centrifuged and decanted. The pellet was rinsed three times with 18 MΩ ultrapure
water. The pellet was subjected to a series of ethanol solutions, starting at 50% ethanol and
ending with 100% ethanol, to remove all water from the pellet. The dehydrated pellet was
suspended in a series of Spurs resin solutions, starting with a 1:1 mixture of resin and 100%
ethanol and ending with 100% resin, enabling infiltration of the bacteria by the resin. The
infiltrated pellet was placed in the tip of a 1 mL BEEM capsule, and the capsules were filled with
100% resin and placed in a 70 oC oven for 24 hours. The sample blocks were removed from the
capsules, sectioned by ultramicrotomy to a 110 nm thickness, and mounted onto 200 mesh
copper grids. Only the grids for the Pb and Ca systems were stained with uranyl acetate and lead
citrate; the U system grids were not stained. TEM images were collected using a Hitachi H-600
TEM operated at 75 kV acceleration voltage, as well as a JEOL 2100F TEM operated at 200 kV
19
using various modes: bright field (BF), dark field (DF), and scanning TEM (STEM). Chemical maps
were determined by an electron dispersive X-ray (EDX) detector using the K line for P and the M
line for U using the JEOL 2100F TEM.
TABLE 1
STARTING CONDITIONS FOR PRECIPITATION EXPERIMENTS (URANIUM SYSTEM)
ID
Initial [U]
(log M)
Initial [P]
(log M)
U1
U2
U3
U4
U5
U6
U7
U8
U9
U10
U11
-4.20
-4.20
-4.20
-4.20
-4.20
-4.20
-4.20
-4.20
-4.20
-4.20
-4.20
-5.49
-5.09
-4.79
-4.62
-4.49
-4.19
-4.01
-3.89
-3.79
-3.65
-3.49
Saturation
Index
(log (Q/K))
0.74
1.13
1.41
1.58
1.69
1.94
2.07
2.14
2.20
2.27
2.32
20
XRD
TEM &
XAS








TABLE 2
STARTING CONDITIONS FOR PRECIPITATION EXPERIMENTS (LEAD SYSTEM)
ID
Initial [Pb]
(log M)
Initial [P]
(log M)
Pb1
Pb2
Pb3
Pb4
Pb5
Pb6
Pb7
Pb8
-4.20
-4.20
-4.20
-4.20
-4.20
-4.20
-4.20
-4.20
-5.79
-5.19
-4.71
-4.49
-4.01
-3.79
-3.65
-3.49
Saturation
Index
(log (Q/K))
4.29
4.91
5.77
6.20
7.15
7.60
7.89
8.19
XRD
TEM







TABLE 3
STARTING CONDITIONS FOR PRECIPITATION EXPERIMENTS (CALCIUM SYSTEM)
ID
Initial [Ca]
(log M)
Initial [P]
(log M)
Ca1
Ca2
Ca3
Ca4
Ca5
Ca6
Ca7
Ca8
Ca9
Ca10
Ca11
-3.00
-3.00
-3.00
-3.00
-3.00
-3.00
-3.00
-3.00
-3.00
-3.00
-3.00
-4.49
-3.79
-3.49
-3.09
-2.79
-2.62
-2.49
-2.31
-2.19
-2.09
-2.01
21
Saturation
Index
(log (Q/K))
2.31
5.25
5.26
6.36
7.12
7.51
7.75
8.04
8.20
8.29
8.34
XRD
TEM






2.3.3.2 XRD
Some of the solids from the abiotic control experiments and from the biotic
experiments were selected for detailed characterization by XRD. These solids were ground into a
fine powder using acetone and an alumina mortar and pestle. The slurry was transferred onto a
zero-background silica XRD slide and allowed to air dry. The slide was then measured at room
temperature using a Scintag X-1 Powder XRD with a copper radiation source. Data were
collected every half-degree from 5 to 60 degrees.
2.3.3.3 Synchrotron experiments
The solid run products from four biotic experiments and from the corresponding four
abiotic controls in the U system were prepared for XAS analysis to characterize the crystallinity
and structure of the precipitates. The concentrations of P in these four experiments were 10-4.49
(sample U5), 10-3.89 (sample U8), 10-3.65 (sample U10), and 10–3.49 (sample U11) M (Table 1).
Resulting bacteria/mineral pellets were immediately packaged on ice for overnight shipment. Xray absorption near edge structure (XANES) and extended X-ray absorption fine structure
(EXAFS) at the U L3-edge (17166) were collected at room temperature for all pellets. A silicon (1
1 1) crystal monochromator was used to select a single energy beam. A Rh-coated harmonic
rejection mirror was used to further eliminate the high harmonic component in the beam. The
incident ionization chamber was filled with 100% N2 gas, and the transmission and reference
ionization chambers were filled with 50% N2 gas and 50% Ar gas, respectively. All of the spectra
were collected in transmission mode as the fluorescence spectra suffered self-absorption
problems due to the high concentration of uranyl phosphate mineral in the samples (Bunker,
2010).
22
Abiotic control samples were precipitated and air dried before processing. Samples
were ground into fine powder using a corundum mortar and pestle, then mixed with graphite
powder to reach relative homogeneity before being loaded into Plexiglas holders and sealed
with Kapton film. At the energy of the U L3-edge, the extra coverage of Kapton film did not
affect the measurements. Biotic samples, present as a paste, were prepared for measurement
by loading the paste into slotted Plexiglas holders, which were then covered with Kapton film.
Prepared biotic samples were refrigerated until data collection. All measurements were
conducted within 72 hours of sample preparation.
For every sample, 10 XANES spectra were initially collected, each lasting less than a
minute, in order to monitor for possible radiation damage to the sample. Due to the
heterogeneity of the samples, EXAFS spectra were collected after the XANES measurements at
10 different spots, with two measurements at each spot. No radiation damage was observed in
the spectra within the 1 minute data acquisition period.
The data were processed using the UWXAFS package (Stern et al., 1995). The program
Athena (Ravel and Newville, 2005) was used to remove the background using the AUTOBK
algorithm (Newville et al., 1993) and to convert the data from k space into R space via Fourier
transformation. The cutoff of background-Rbkg was set to 1.1 for all measurements. The
program Artemis (Ravel and Newville, 2005) was used to fit the experimental EXAFS spectra.
Well defined mineral structures were input into Atom (Ravel et al., 2001) and used to generate
theoretical EXAFS paths in FEFF6 (Zabinsky et al., 1995). Shell-by-shell fitting was obtained using
the program FEFFIT (Newville, 2001), and the statistical factors reduced-χ2 and R-factor were
used as criteria to optimize the fitting.
23
2.3.3.4 ICP-OES
ICP-OES element standards with the same ionic strength matrix as the experimental
samples were prepared gravimetrically by diluting commercially-supplied 1,000 ppm aqueous
Ca, Pb, U, and P standards with 0.1 M NaClO4. The concentrations of the U and Pb standards
ranged from 10-6.70 to 10-4.10 M. The concentrations of the Ca standards ranged from 10-4.90 to 103.00
M, and the concentrations of the P standards ranged from 10-5.80 to 10-2.60 M. The standards
were acidified following the same procedure as was applied to the samples. The standards and
samples were analyzed with a Perkin Elmer 2000DV ICP-OES within 5 days of collection. U was
analyzed at 424.167 nm, Pb was analyzed at 220.356 nm, Ca was analyzed at 227.546 nm, and P
was analyzed at 214.914 nm. The set of standards was analyzed before, in between, and after
the samples were analyzed to check for machine drift. Analytical uncertainty, as determined by
repeat analyses of the standards, was ±2.75%.
2.3.3.5 TOC
TOC standards were prepared by gravimetrically diluting commercially-supplied 1,000
ppm C aqueous standard using the same ionic strength buffer solution as the experimental
samples. The standards were then acidified with 6M HCl and immediately sealed with parafilm.
The standards and samples were analyzed with a Shimadzu TOC – V/TNM within 24 hours of
collection.
2.3.4 Thermodynamic modeling
2.3.4.1 Saturation state calculations
To determine initial saturation state values for each of the experimental systems,
activity quotients (Q) were calculated using a Newton-Raphson iteration technique to solve the
24
non-linear system of mass balance and mass action equations listed in Tables 4, 5, and 6. The
starting molarities of each metal and P were used as mass balance constraints, and the resulting
Q was calculated according to the following dissolution reactions for hydrogen uranyl
phosphate, lead phosphate, and hydroxylapatite:
(UO2)(HPO4)·3H2O (s)  3 H2O + UO22+ + HPO42Pb3(PO4)2 (s)  3 Pb2+ + 2 PO43Ca5(PO4)3OH (s)  5 Ca2+ + 3 PO43- + OHso that the Q value for each reaction corresponds to the following terms, respectively:
QU = aH2O3 • aUO2 • aHPO4
QPb = aPb3 • aPO42
QCa = aCa5 • aPO43 • aOH
Activity coefficients were calculated using an extended Debye-Hückel equation with A,
B, and å values of 0.5101, 0.3285, and 5.22, respectively (Helgeson et al., 1981). Saturation state
values were then calculated by comparing the resulting Q values to the equilibrium constants, K,
for the respective mineral, according to Equation 7:
Saturation Index = log (Q / K)
In the calculations, I assume water activities of unity, and the equilibrium constant
values that were used for Reactions 1-3 were 10-13.17, 10-43.53, and 10-53.28, respectively (Zhu et al.,
2009; Martell and Smith, 2001; Gorman-Lewis et al., 2009).
25
TABLE 4
SYSTEM OF EQUATIONS USED FOR SATURATION STATE AND SOLUBILITY CALCULATIONS FOR
URANIUM SYSTEM
Reaction
Log K
2+
2-
(UO2)(HPO4)·3H2O (s) → 3 H2O + UO2 + HPO4
UO22+ + PO43- → UO2PO4UO22+ + HPO42- → UO2HPO40
UO22+ + H3PO40 → UO2H2PO4+ + H+
UO22+ + H3PO40 → UO2H3PO42+
2+
UO2 + 2 H3PO40 → UO2(H2PO4)20 + 2 H+
UO22+ + 2 H3PO40 → UO2(H2PO4)(H3PO4)+ + 2 H+
UO22+ + H2O → UO2OH+ + H+
UO22+ + 2 H2O → UO2(OH)20 + 2 H+
UO22+ + 3 H2O → UO2(OH)3- + 3 H+
2 UO22+ + H2O → (UO2)2OH3+ + H+
2 UO22+ + 2 H2O → (UO2)2(OH)22+ + 2 H+
3 UO22+ + 5 H2O → (UO2)3(OH)5+ + 5 H+
UO22+ + CO32- → UO2CO30
UO22+ + 2 CO32- → UO2(CO3)222+
2 UO2 + CO32- + 2 H2O → (UO2)2(CO3)(OH)3- + 3 H+
HPO42- → H+ + PO43H+ + HPO42- → H2PO4H+ + H2PO4- → H3PO40
H2O → OH- + H+
H2CO30 → HCO3- + H+
H2CO30 → CO32- + 2 H+
mH2CO3 = 10-4.97
(a) Gorman-Lewis et al., 2009.
(b) Guillaumont et al., 2003.
(c) Martell and Smith, 2001.
26
-13.17 (Ksp) a
13.23 b
7.24 b
1.12 b
0.76 b
0.64 b
1.65 b
-5.25 b
-12.15 b
-20.25 b
-2.70 b
-5.62 b
-15.55 b
9.94 b
16.61 b
-0.86 b
-12.35 b
7.21 b
2.14 b
-14.00 c
-6.35 c
-16.68 c
TABLE 5
SYSTEM OF EQUATIONS USED FOR SATURATION STATE CALCULATIONS FOR LEAD SYSTEM
Reaction
Log K
Pb3(PO4)2 (s) = 3 Pb2+ + 2 PO43Pb2+ + OH- = PbOH+
Pb2+ + 2 OH- = PbOH20
Pb2+ + 3 OH- = PbOH32 Pb2+ + OH- = Pb2OH3+
3 Pb2+ + 4 OH- = Pb3OH42+
4 Pb2+ + 4 OH- = Pb4OH44+
6 Pb2+ + 8 OH- = Pb6OH84+
Pb2+ + HPO42- = PbHPO40
Pb2+ + H2PO4- = PbH2PO4+
Pb2+ + 2 CO32- = Pb(CO3)22HPO42- = H+ + PO43H+ + HPO42- = H2PO4H+ + H2PO4- = H3PO40
H2O = OH- + H+
H2CO30 = HCO3- + H+
H2CO30 = CO32- + 2 H+
mH2CO3 = 10-4.97
-43.53 (Ksp)
6.3
10.9
13.9
7.6
32.1
35.1
68.4
3.1
1.5
9.05
-12.35
7.21
2.14
-14.00
-6.35
-16.68
(a) Martell and Smith, 2001.
27
TABLE 6
SYSTEM OF EQUATIONS USED FOR SATURATION STATE CALCULATIONS FOR CALCIUM SYSTEM
Reaction
Log K
Ca5(PO4)(OH)3 (s) = 5 Ca2+ + 3 PO43- + OHCa2+ + OH- = CaOH+
Ca2+ + PO43- = CaPO4Ca2+ + HPO42- = CaHPO40
Ca2+ + H2PO4- = CaH2PO4+
HPO42- = H+ + PO43H+ + HPO42- = H2PO4H+ + H2PO4- = H3PO40
H2O = OH- + H+
-53.28 (Ksp)a
1.3b
6.46 b
2.74 b
1.4 b
-12.35 b
7.21 b
2.14 b
-14.00 b
(a) Zhu et al., 2009.
(b) Martell and Smith, 2001.
2.3.4.2 HUP solubility calculation
The solubility of the isolated biogenic HUP particles was calculated using a similar
Newton-Raphson program to the one used to calculate saturation states to solve the non-linear
set of mass action and mass balance equations corresponding to the reactions listed in Table 1.
The total dissolved P concentration for the calculation was fixed at the average P concentration
from the biogenic HUP solubility experiments. The model was used to calculate the expected U
concentration based on the solubility product for macroscopic HUP reported by Gorman-Lewis
et al. (2009).
28
2.4 Results & Discussion
2.4.1 Uranium system
2.4.1.1 TEM
Element maps (a representative example of which is shown in Figure 2) of U and P
distributions in the biotic B. subtilis samples indicate that while P is distributed throughout the
cells, U is concentrated on the cell walls. These results suggest that the cells in these
experiments did not actively incorporate U into the cytoplasm through metabolic processes, and
that the U distribution in the biotic experiments is controlled by adsorption and/or precipitation
reactions on or within the bacterial cell walls.
The TEM images of the samples taken from the lower saturation state conditions
investigated (samples U5 and U8) suggest that precipitation of uranyl phosphates was
homogeneous, occurring exclusively in solution, and that the cell walls did not appear to
influence the mineralization reaction (Figs. 3a,b). The figures show some contact between the
precipitate and the bacterial cells in these samples, but the images do not offer evidence that
the cells were involved in the precipitation, and it is likely that the cell-mineral association is
coincidental only. Figure 3a and 2b also show no significant difference in the size of the mineral
precipitate between the abiotic control and the biotic experiment, which is consistent with a
lack of influence of the bacterial cells on the precipitation reaction at the lower saturation state
conditions investigated.
TEM evidence, however, indicates that under the higher saturation state conditions
investigated (sample U11), uranyl phosphate precipitation was heterogeneous, with nano-scale
29
Figure 2: Elemental map of biotic U11 sample. P is shown in red, U is shown in green. The scale
bar is 500 nm.
30
Figure 3: TEM bright field images for U system. (A) Abiotic U5 control; (B) Biotic U5
experiment; (C) Abiotic U11 control; (D) Biotic U11 experiment. All scale bars are
200 nm. The bacteria in (B) and (D) is B. subtilis.
crystals appearing to nucleate within the three-dimensional macromolecules that comprise the
bacterial cell walls (Figure 3c,d). Under these conditions, there is a distinct difference in
precipitate size between the abiotic control and the biotic experiment. The abiotic control
(Figure 3c) exhibits plate-like precipitates with edge lengths ranging from approximately 50 to
31
150 nm and thicknesses of approximately 10 nm. The lath-like precipitates observed in the
abiotic controls represent cross-sections of the plate-like precipitates that are oriented
perpendicular to the plane of the page. Close examination of the cell wall-controlled
precipitation (Figure 4) demonstrates that precipitation was uniformly distributed around each
cell and that the crystals are all plate-like in morphology with edge lengths ranging from
approximately 10 to 30 nm and a thickness of approximately 1 to 5 nm, with nucleation
occurring throughout the cell wall matrix and with crystals growing both into and out of the cell
itself. The same cell wall nucleation phenomenon was observed in samples from the parallel
systems that contained S. oneidensis MR-1 (Figure 5); however, with the Gram-negative species,
the nucleation appears to be restricted between the outer and plasma membranes, and the
particles are oriented parallel to the cell membranes. This can be compared to the randomly
oriented crystals that formed within the cell wall matrices of the Gram-positive B. subtilis
species.
These images provide unequivocal evidence that bacterial cell walls can nucleate
mineral formation. The particles visible within the bacterial cell walls depicted in Figures 3d, 4,
and 5 clearly nucleated in place, most likely nucleated on one or more types of cell wall
functional groups. Surface controlled precipitation is thought to stem from adsorption onto
surface binding sites (e.g., Farley et al., 1985; Warren and Ferris, 1998), and in the experiments
in which cell wall nucleation was evident, precipitation likely begins with uranyl adsorption onto
a cell wall binding site. The adsorbed uranyl forms a positively charged site, and in this way
phosphate adsorption can alternate with uranyl adsorption at this site to form a bacterial cell
wall precipitate.
32
Figure 4: TEM bright field images for U system. (A) Biotic U10
experiment; (B) close up of area in the black box in image A to illustrate
the texture of the biogenic U nanoparticulate precipitate; (C) Biotic U10
experiment; (D) close up of area located in black box in image C; (E)
Biotic U10 experiment; (F) close up of area located in the black box in
image F. The bacteria in all micrographs is B. subtilis.
33
Figure 5: TEM bright field image of uranyl phosphate biomineralization
in biotic (A) U5 and (B) U11 samples, showing texture and prevalence of
minerals within the S. oneidensis cell walls. The scale bars represent (A)
200 nm, and (B) 100 nm.
2.4.1.2 SAED and XRD
Selected area electron diffraction (SAED) patterns of the abiotic run products tested
indicated that the precipitated solids exhibit a high degree of crystallinity. SAED results for the
biotic samples exhibit a diffuse ring pattern, with some evidence of weak and ephemeral
diffraction patterns. This is evidence that the nanoparticles are crystalline, but because of their
small size they rapidly become amorphous under the electron beam. Solid run products from
abiotic controls and biotic experiments with starting P concentrations of 10-4.49, 10-3.89, 10-3.65,
and 10-3.49 M, the same samples (U5, U8, U10, and U11) that were analyzed with TEM, were
characterized using XRD to determine the crystallinity and identity of the precipitates. Each of
the samples exhibits a number of peaks in common with the diffractogram for a reference
sample of hydrogen uranyl phosphate (UO2HPO4•4H2O), or HUP, as well as some different peaks
34
(Figure 6). Each of the sample diffractograms exhibit peak shoulders at 2θ equal to 24.25 and
25.75 that correspond to characteristic peak angles in the reference pattern. Similarly, the
reference pattern and all of the samples exhibit a peak at 2θ equal to 51.75. Additionally, all of
the biotic experiments exhibit a peak at 2θ equal to 27.25, which corresponds with the peak at
the same angle in the reference pattern. However, the peaks exhibited at 2θ equal to 22.7 are
only present in the biotic U8 and U10 experiment diffractograms, and not exhibited in the
reference pattern. These peaks are likely a result of minor, unidentified mineral phases only
present in the biotic samples, or they may result from the HUP in the sample containing a
different number of water molecules than the HUP XRD standard. Additionally, the peak at 2θ
equal to 24.7 in the bacteria-only sample is present in diffractograms for each abiotic and biotic
sample, but is not present in the diffractogram for the mineral reference sample. This peak likely
results from a salt precipitate from the experimental solutions. Although there are variations in
peak intensities in the diffractograms between the precipitates from the abiotic controls and the
biotic experiments, and between precipitates from experiments with varying P concentrations,
the peak positions and intensities in each diffractogram are consistent with the HUP reference
pattern.
2.4.1.3 XAS
XANES spectra (Figure 7) indicate a U(VI) valence state for all of the samples, with no
reduction of U to U(IV) observed. The edge position of the U(IV) spectrum is shifted
approximately 4 eV towards lower energy relative to the U(VI) spectrum (Kelly et al., 2002), and
this shift was not observed in any of the samples. The shoulder structure approximately 15 eV
above the edge due to the multiple-scattering of the two axial oxygen atoms of the uranyl ion
35
Figure 6: XRD patterns from analysis of run products from U system experiments.
(Hennig et al., 2001) is a characteristic feature of the U(VI) valence state (Boyanov et al,, 2007),
and is present in the spectra of all of the samples. Both lines of evidence indicate that the vast
majority of the uranium in the biotic and abiotic samples remained as U(VI) during the
experiments, with no measureable reduction to U(IV).
EXAFS spectra at the U L3-edge show that at low saturation state conditions (biotic
sample U5), uranyl ions are present in the biotic sample dominantly as adsorbed species, bound
to carboxyl and phosphoryl groups on the bacterial cell walls. The signal strength of the
phosphorous peak (located at 3.0 Å) is weak compared to the HUP reference spectrum (Figure
8), and in general, the biotic U5 sample exhibits a markedly different spectrum than does the
HUP standard. The second oxygen peak is more distinguishable from the other samples, and the
36
Figure 7: k3-weighted EXAFS spectra of the biotic and abiotic samples
plotted with the HUP standard. Except for Biotic U5, which exhibits an
adsorption spectrum, all spectra have the small features around k = 10
Å-1, a signature feature of the autunite group.
peak at approximately 3.0 Å is damped. At 2.2 Å, the biotic U5 spectrum does not dip as much as
the HUP mineral spectrum, which corresponds to the contribution of a carbon atom. The fitting
suggests a binding environment of two axial oxygen atoms at 1.75 Å, and two split equatorial
oxygen shells: one at 2.19 Å with approximately 2.2 oxygen atoms, and the other at 2.34 Å with
approximately 5.3 oxygen atoms. This split of the equatorial oxygen shells results from the
uranyl ion binding to a phosphate group so that the symmetry of equatorial oxygen is
perturbed. The average number of bound C atoms at 2.90 Å from the U atom is 1.1, and the
37
average number of bound P atoms at a distance of 3.54 Å is 0.78. These results suggest that the
uranyl ion in biotic sample U5 is bound to both carboxyl and phosphoryl sites, a result that is
consistent with the findings of Kelly et al., (2002) who examined the adsorption of uranyl onto B.
subtilis cells. The model fit of this EXAFS spectrum is shown in Figure 9.
Although adsorbed U is the only form of U detected by XAS in the biotic U5 sample, with
increasing saturation state conditions, the EXAFS spectra indicate that U is present
predominantly as solid phase HUP. Figure 8 compares the EXAFS spectra from the abiotic and
biotic samples with that of the HUP standard. All the abiotic samples and most of the biotic
samples (except biotic U5) match the HUP mineral spectrum, exhibiting an axial oxygen peak at
1.4 Å, an equatorial oxygen peak at 1.8 Å, and a peak at 3.0 Å. (corresponding to phosphorus
atoms). Slight differences exist between the spectra from the abiotic and the biotic samples, but
these are likely due to experimental artifacts from the sample preparation procedure.
Heterogeneous samples are well known to exhibit amplitude reduction, known as “thickness
effects”, in transmission measurements, and can also introduce background variations in the
spectra. Because only small amounts of the abiotic precipitates were available for the
experiment, the dried precipitates were ground and mixed with graphite powder before being
mounted for measurement to obtain relatively homogenous samples. The EXAFS spectra were
taken from different spots of the sample, and the spots which exhibited obvious anomalous
background were abandoned. Despite these efforts to eliminate the artifacts from
heterogeneity, spectra from some samples still exhibited background anomalies. In addition to
the background artifacts, the possibility of amorphous phases existing together within the
mineral crystal cannot be ignored. In the amorphous phase, the disorder of the local structure
around uranium would reduce the amplitudes of the oxygen peaks. The biotic samples, on the
other hand, were more homogenous as a result of the biomass matrix. The differences in biotic
38
Figure 8: (A) Magnitude of U L3-edge EXAFS spectra after Fourier
transformation for the abiotic samples overlaid by the HUP standard; (B)
Magnitude of U L3-edge EXAFS spectra after Fourier transformation for
the biotic samples overlaid by the HUP standard. Spectra shown were
collected in transmission mode.
39
Figure 9: EXAFS data and fit in the magnitude of the Fourier transformed EXAFS spectrum.
samples were relatively small, except for the biotic U5 sample, which indicates U ions adsorbed
to the bacterial cell wall rather than nanoparticle formation. Fluorescence measurements (data
not shown here) of the samples in Figure 8 are consistent with transmission measurements,
which corroborates the validity of the measurements.
The k3-weighted EXAFS spectra (Figure 7) show the suppressed oscillations around
k~10, which is a characteristic signature for HUP/autunite/chernikovite group minerals (Fuller et
al., 2003). This feature is present in every sample (except biotic U5), which supports the
conclusion that the dominating phase in the abiotic and biotic samples is the HUP mineral
40
phase. With the exception of the biotic U5 sample, all of the spectra could be fit to the HUP
structure (Morosin, 1978) with 2 axial oxygen atoms at 1.78 Å, approximately 4 equatorial
oxygen atoms at 2.3 Å, and approximately 4 phosphorus atoms at 3.6 Å. The fitting to each
spectrum is shown in Figure 9 (details of the fitting paths and parameters are available in Tables
7 and 8). Fittings show consistent distances between the axial and equatorial oxygen and
uranium as well as the phosphorus and uranium atoms compared to the known HUP structure.
The shell coordination numbers are also consistent, within uncertainty, with the HUP structure.
Multiple scattering paths from the axial oxygen atoms and from the equatorial oxygenphosphorous atoms were also included to improve the quality of the fit.
TABLE 7
FITTING PATHS AND CORRESPONDING PARAMETERS USED FOR XAS ANALYSIS
Path
U  Oax
U  Oeq1
U  Oeq2 b
UP
U  Oax1  U  Oax1
U  Oax2  U  Oax1
U  Oax2  Oax1
U  P  Oeq
U  Oeq  P  Oeq
U  Oeq1  Oax a
UCb
U  C Oeq b
Ncoor
σ2 (x10-3 Å2)
ΔE0(eV)
2
N1
N2
N3
2
2
2
N3 X 2
N3
N1 X 4
N4
N1 X 4
2
ΔE01
ΔE02
ΔE02
ΔE03
ΔE01
ΔE01
ΔE01
ΔE03
ΔE03
ΔE04
ΔE03
ΔE03
R(Å)
ΔR1
ΔR2
ΔR3
ΔR4
ΔR1 X 2
ΔR1 X 2
ΔR1 X 2
ΔR4
ΔR4
ΔR2
ΔR5
ΔR5
σ1
σ22
σ22
σ23
σ21 X 2
σ21 X 2
σ21 X 2
σ23
σ23
σ22
σ23
σ23
(a) The coordination number of the axial oxygen in uranyl ion is 2, this number is set during the
fitting.
(b) Additional path used for uranyl phosphate mineral fitting.
(c) Additional path used for uranyl adsorption spectra fitting of biotic U5 sample.
41
TABLE 8
PARAMETERS FOR MAJOR FITTING PATHS USED IN THE FITTING OF XAS DATA
Sample
Path
R(Å)
Nd,eq
σ2 (x10-3 Å2)
Biotic U5
U  Oax
U  Oeq1
U  Oeq1
UP
UC
U  Oax
U  Oeq1
UP
U  Oax
U  Oeq1
UP
U  Oax
U  Oeq1
UP
U  Oax
U  Oeq1
UP
U  Oax
U  Oeq1
UP
U  Oax
U  Oeq1
UP
U  Oax
U  Oeq1
UP
1.75 ±0.02
2.28 ± 0.04
2.13 ±0.05
3.58 ±0.02
2.54 ±0.03
1.78 ±0.01
2.28 ±0.01
3.57 ±0.03
1.78 ±0.01
2.27 ±0.01
3.58 ±0.02
1.78 ±0.01
2.27 ±0.01
3.54 ±0.03
1.80 ±0.01
2.27 ±0.01
3.57 ±0.03
1.78 ±0.01
2.27 ±0.01
3.56 ±0.02
1.79 ±0.01
2.27 ±0.01
3.57 ±0.03
1.77 ±0.01
2.27 ±0.01
3.59 ±0.02
2
3.69 ±1.72
2.53 ±1.42
1.48 ±1.03
2.56 ±0.91
2
5.37 ±0.77
5.33 ±2.63
2
4.24 ±0.58
4.84 ±2.22
2
4.67 ±0.56
4.56 ±1.73
2
4.48 ±0.78
6.34 ±2.8
2
4.23 ±0.45
4.67 ±2.05
2
4.02 ±0.58
2.69 ±1.70
2
5.44 ±0.81
2.85 ±1.40
3.2 ± 1.2
5.7 ±3.0
5.7 ±3.0
3.2 ±3.3
3.2 ±3.3
2.4 ±0.4
6.1 ±1.2
6.1 ±3.3
2.1 ±0.4
4.8 ±1.2
6.0 ±3.1
2.1 ±0.4
5.7 ±1.1
9.5 ±3.2
4.7 ±0.6
5.0 ±1.3
8.1 ±3.4
1.6 ±0.3
5.4 ±1.0
7.3 ±3.2
1.7 ±0.4
4.1 ±1.1
2.7 ±3.5
2.2 ±0.4
7.0 ±1.3
2.0 ±2.6
Biotic U8
Biotic U10
Biotic U11
Abiotic U5
Abiotic U8
Abiotic U10
Abiotic U11
The XAS results indicate that bacteria do not affect the mineral that precipitates during
the experiments, and that HUP is the only significant solid phase to form in both the abiotic
controls and the biotic experiments. Fittings of the EXAFS spectra (Figure 9) to the theoretical
42
model indicate that the structure of the precipitate in all of the abiotic controls, as well as in all
biotic experiments, is consistent with the mineral structure of HUP. Furthermore, and perhaps
most importantly, the XAS results strongly suggest that, as predicted by surface precipitation
theory, uranyl adsorption onto cell wall functional groups represents the first step in cell wall
nucleation of uranyl phosphate minerals. Under the lower saturation state conditions studied,
even though uranyl phosphate precipitation occurred in the system, uranium is present in the
sample dominantly as adsorbed uranyl species. With increasing saturation state conditions, the
adsorbed uranyl signal becomes overwhelmed with the uranyl phosphate precipitate, and under
the highest saturation states studied, the precipitation becomes clearly nucleated within the cell
wall.
2.4.1.4 ICP-OES
In the discussion of the aqueous chemistry results, I referred to example saturation
state conditions that correspond to the numbers in Figures 10a and 10b. Both the starting and
final concentrations for those example experiments are shown with corresponding number
labels and arrows. Saturation state condition 1 represents the lowest saturation state studied;
increasing saturation state condition numbers indicate increasing saturation state conditions.
For saturation state conditions 2 and 3 (Figure 10a), the abiotic controls removed significantly
more U from solution than the B. subtilis biotic experiments performed at 0.62 gm wet biomass
L-1. At saturation state condition 1, the biotic experiments removed slightly more U from
solution than the abiotic controls. This slight increase in removed U is likely in part a result of U
adsorption onto the biomass in the experiment, a result consistent with the XAS findings for
these low saturation state conditions. Additionally, the biotic experiments show an increase in
final P concentrations relative to the experimental starting conditions at saturation state
43
condition 1. This increase is likely due to P exuded from the bacteria during the experiment, and
some of the enhanced U removal relative to the abiotic controls may be due to enhanced HUP
precipitation from this additional P in the system. At saturation state conditions 2 and 3, the
amount of P exuded represents a lower percentage of the total P in the experimental systems,
and no significant increase in P is observed in those systems. Under all saturation states
investigated, the abiotic controls removed more P from solution than did the biotic experiments
relative to the starting conditions.
As the bacterial concentration was varied from 0.31 to 2.50 gm (wet biomass) L-1, the
amount of U removed from solution did not exhibit a consistent trend as a function of bacterial
concentration (Figure 10a). At all of the bacterial concentrations studied, the abiotic controls
removed more U from solution at saturation state conditions 2 and 3 than did the biotic
experiments. With increasing bacterial concentration, the final aqueous P concentration in the
biotic experiments increased as well, likely due to bacterial exudates which contain P. However,
the relative increase in P concentration decreased as the saturation state increased to condition
3.
Shewanella oneidensis biotic experiments removed slightly more U from solution at low
saturation states (condition 1) than did the abiotic controls, but the two types of experiments
removed approximately equal concentrations of U from solution under higher saturation state
conditions (Figure 10b, condition 2). The abiotic controls removed up to one log unit more P
from solution at low saturation states than did the biotic experiments. Similar to the B. subtilis
biotic experiments, the lowest saturation state S. oneidensis biotic experiments exhibited
elevated final P concentrations, relative to both the starting conditions and the abiotic controls.
This elevated P concentration is likely due to P that is exuded from the bacteria. The bacteriallyexuded P in the S. oneidensis system is more readily available for U removal than the P exuded
44
A
1
1
1
3
2
2
3
2
3
B
2
1
1
1
2
2
Figure 10: Changes in the aqueous concentrations of U and P in the U
experiments. (A) B. subtilis; (B) S. oneidensis. All experiments were
performed in triplicate (symbols represent the mean). Error bars
represent one standard deviation (note that some error bars are smaller
than the symbol). Each arrow connects the starting condition (arrow
tail, asterisks) to the final U and P concentrations in the abiotic control
or biotic experiments (arrow head, squares and circles). The numerals
“1”, “2”, and “3” represent saturation state conditions discussed in
detail in the text and are presented here for reference.
45
by B. subtilis, as evidenced by the greater removal of U from solution at the lowest saturation
state condition in the S. oneidensis system relative to the B. subtilis system (Figs. 10b and 10a,
respectively). At high saturation states, there was no significant difference in final U and P
concentrations between the abiotic controls and the biotic experiments in the S. oneidensis
system.
The higher aqueous U concentrations in the biotic experiments relative to the abiotic
controls are not likely caused by nucleation kinetics effects. If the presence of the bacteria
accelerated the nucleation kinetics, a result consistent with the presence of the smaller crystals
in the biotic experiments relative to the abiotic controls, then one would expect lower
concentrations of U to remain in solution as faster precipitation kinetics usually cause more
complete precipitation reactions (Kasama and Murikami, 2001; Fritz and Noguera, 2009).
Similarly, cell wall adsorption of U should cause enhanced removal of U from solution relative to
the abiotic control experiments (Fowle et al., 2000; Gorman-Lewis et al., 2005; Knox et al. 2008).
However, the opposite occurs in most of the experiments, with higher aqueous U
concentrations in the B. subtilis biotic experiments. The concentration of bacteria in the system
does not significantly affect the extent of U and P removal within a range of 0.31 to 2.50 gm wet
biomass L1 (Figure 10a), also suggesting that U adsorption onto the bacteria does not control U
concentrations in the higher saturation state experiments. This behavior is not a result of
increased saturation state conditions in biotic experiments, since higher saturation states would
result in less U remaining in solution in the biotic experiments compared to the abiotic controls
(Ohnuki et al., 2005).
Elevated U concentrations can be caused by inhibition of precipitation by aqueous U
complexation with organic exudates. To test whether aqueous U-organic complexes affected the
extent of precipitation and were the cause for the observed elevated aqueous U concentrations
46
in the biotic experiments, I used an organic exudate solution to perform a cell-free control
experiment. Figure 11 shows that at low saturation states (condition 1), the exudate solution
contained an elevated P concentration relative to both the starting conditions and the abiotic
control, confirming that bacteria exude P into solution. This effect is less apparent as the
experimental P concentration increases. At the lowest saturation states investigated, there was
no significant removal of U by the exudate solution, which is consistent with the XAS results
which show that at low saturation states, U is dominantly removed by adsorption to cell walls.
This also suggests that the exuded P is present as an organo-phosphate and is unavailable for
precipitation with U. If the exudates contained orthophosphate, we would expect to observe
enhanced U removal in the exudates solutions relative to the abiotic controls. As the saturation
state increases to conditions 2 and 3, the exudate solution removes more U from solution than
the biotic experiments, but removes less U from solution than the abiotic controls. These results
suggest that U-organic aqueous complexes form under the experimental conditions, accounting
for at least a portion of the increased final U concentration in the biotic experiments. However,
because the exudate solution experiments result in more U removal than do the biotic
experiments, it is evident that these aqueous complexes only account for a portion of the
elevated U concentrations in the biotic experiments, and that another process also contributes
to the observed elevated U concentrations in the biotic experiments.
2.4.1.5 Solubility
Complexation of U with organic exudates explains at least part of the enhanced U
concentrations observed in the biotic experiments; however, at higher initial P concentrations,
complexation does not explain the discrepancy between the abiotic controls and the biotic
experiments. It is under these conditions that I observed cell wall mineralization and smaller
47
-7
-6
-5
-4
-4
Final [U] (log M)
-5
-6
Starting Conditions
-7
Abiotic Control
0.62 g wet biomass / L
Bacterial Exudate Experiment
-8
Final [P] (log M)
Figure 11: Aqueous chemistry results for the bacterial exudate
experiment (shown as hollow triangles) compared to aqueous chemistry
results for the U system (as shown in Figure 10A). Each arrow connects
the starting condition (arrow tail, asterisks) to the final U and P
concentrations in the abiotic control or biotic experiments (arrow head,
squares and circles). The numerals “1”, “2”, and “3” represent
saturation state conditions discussed in detail in the text and are
presented here for reference.
particle sizes. These particles appear to be plate-like in morphology, with edge dimensions of
much less than 30 nm in all dimensions. It is possible that the solubility of these nanoparticles is
higher than the solubility of the much larger abiotic precipitates, and the solubility experiments
were designed to test this hypothesis.
48
Figure 12 depicts the experimental measurements of the solubility of the isolated
biogenic precipitates (isolated from biotic U10). The measured U and P concentrations attained
steady-state log molalities of total U and P in solution were -4.34 ± 0.07 and -3.13 ± 0.08,
respectively, with no consistent change in concentration after 2 days. The solubility product of
HUP, determined by Gorman-Lewis et al. (2009) using 300 μm crystals, was used to calculate an
expected solubility of macroscopic HUP crystals for comparison. For these calculations, I account
for aqueous U and P speciation using the reactions and equilibrium constants shown in Table 1.
At the measured equilibrium P concentration of the biogenic HUP solubility experiment,
macroscopic HUP would be in equilibrium with a solution with a U log molality of -5.86 (0.10/+0.08). The biogenic HUP exhibited a U concentration approximately 1.5 orders of
magnitude higher than the concentration calculated for macroscopic HUP, suggesting that the
particle size of these nanoscale-sized particles can exert a large influence on their solubilities.
The results of the solubility measurements suggest that in addition to the effect of the aqueous
U-exudate complexation, the size of the biogenic nanoprecipitates that form under high
saturation state conditions likely contributes to the enhanced U concentrations that I observed
in the biotic experiments.
2.4.1.6 Effects of bacteria on uranyl phosphate precipitation
My results present evidence for passive cell wall biomineralization, a type of
biomineralization in which the high binding affinity of cell walls for aqueous metal cations
creates nucleation sites for mineral precipitation reactions in saturated systems. Although it is
not clear from the data which cell wall functional groups are involved and what the exact
precipitation mechanism is, the data demonstrate unequivocally that the presence of bacteria in
49
Figure 12: Measured U and P concentrations from the solubility
experiments involving biogenic hydrogen uranyl phosphate (HUP)
precipitates. Model P concentrations were fixed at the average
experimental value, and the model U line is the calculated U
concentration in equilibrium with macroscopic HUP, using the Ksp value
reported by Gorman-Lewis et al. (2009).
some precipitating systems can alter the extent and morphology of the precipitation reaction,
and is likely to affect the fate and mobility of the precipitating elements.
Passive cell wall biomineralization and the formation of nanoprecipitates of uranyl
phosphate could significantly affect the mobility of U compared to the mobility exhibited if the
precipitation occurred without bacteria present. Nanoprecipitates of uranyl phosphate may be
released from the cell walls in which they formed after cell death, and due to their small size,
the particles may be highly mobile in a geologic matrix. In addition, as the data suggest,
nanoprecipitates can exhibit markedly higher solubilities than macro-scale crystals, and organic
bacterial exudates can form aqueous complexes with dissolved uranium. Both of these
50
processes affect the mobility of uranium in the aqueous phase, increasing the equilibrium
concentration of U in solution at a given P concentration.
2.4.2 Lead system
2.4.2.1 TEM
Figure 13 shows TEM micrographs of biotic samples under high and low saturation
states (biotic Pb4 and Pb8). All of the electron dense (dark) particles in the bulk solution in the
figure represent the mineral precipitate. The mineral precipitates in these images exhibit the
same morphology and are similar in size (note that the scale bars are different in each
micrograph). It is also evident that although the precipitate and the bacteria are in contact at
some points, the contact appears to be coincidental only and no strong spatial correlation exists.
I concluded from this visual evidence that passive cell wall mineralization does not occur in the
Pb system under any of the saturation state conditions investigated.
Figure 13: TEM bright field images for Pb system: (A) Biotic Pb4
experiment (scale bar is 200 nm); (B) Biotic Pb8 (scale bar is 100 nm).
51
2.4.2.2 XRD
The solid run products from biotic experiments Pb4, Pb6, and Pb8 were analyzed by XRD
(Figure 14). The diffractograms for these samples exhibit the same peaks, suggesting that the
precipitate in each biotic experiment was the same mineral, a result that is consistent with the
TEM results above. Therefore, the precipitate in the Pb system is unaffected by varying
saturation states within the range investigated in this study. Additionally, the diffractograms of
the biotic experiments are all consistent with the reference pattern (ICDD 00-002-0750) for lead
phosphate (Pb3(PO4)2). XRD analyses were not performed on the abiotic controls due to the
difficulty of harvesting a large enough mass of precipitate at the low Pb concentrations
investigated.
Figure 14: XRD patterns for biotic samples from the Pb system. The
lower pattern is a reference for Pb3(PO4)2.
52
2.4.2.3 ICP-OES
Under saturation state condition 1, the abiotic controls removed half a log unit less Pb
from solution than did the biotic experiments (Figure 15). Under this condition, the biotic
experiments exhibited an increase in the final concentration of P relative to the abiotic controls
and the starting condition. This increase in P in the biotic experiments, which is not seen in the
abiotic controls, is likely a result of P exuded from the bacteria during the experiment.
Therefore, if the exuded P is at least in part present as orthophosphate, the enhanced Pb
removal from solution in the biotic case could be due to enhanced Pb3(PO4)2 precipitation due to
the elevated saturation state that results from the exuded P. Alternatively, the enhanced
removal in the biotic experiments could be due to Pb adsorption onto the biomass in the biotic
experiments. At saturation state condition 2, the extents of Pb removal by the abiotic controls
and the biotic experiments were not significantly different, nor did the P concentration change
during the course of either the biotic or abiotic experiments.
2.4.2.4 Effect of bacteria on lead phosphate precipitation
The Pb system results demonstrate that the presence of bacteria does not strongly
affect the extent or nature of Pb-phosphate precipitation under the conditions studied. Under
low saturation state conditions, I observed enhanced removal of Pb from solution in the biotic
systems relative to the abiotic controls, and this effect could be due either to the P that is
exuded by the bacteria or to biomass adsorption of Pb. The bacteria do not affect the
mineralogy nor the morphology of the precipitates in the Pb system, and consistent with these
observations, the TEM images showed little or no association between the bacteria and the
precipitate.
53
-5.5
-5
-4.5
-4
-3.5
-4
[Pb] final (log M)
-4.5
Starting Conditions
Abiotic Control
-5
0.62 g wet biomass / L
-5.5
-6
-6.5
[P] final (log M)
Figure 15: Changes in the aqueous concentrations of Pb and P in the Pb
experiments with B. subtilis. All experiments were performed in
duplicate. Error bars represent one standard deviation (note that some
error bars are smaller than the symbol). Each arrow connects the
starting condition (arrow tail, asterisks) to the final Pb and P
concentrations in the abiotic control or biotic experiments (arrow head,
squares and circles). The numerals “1” and “2” represent saturation
state conditions discussed in detail in the text and are presented here
for reference.
54
2.4.3 Calcium system
2.4.3.1 TEM
Under low saturation state conditions (Figs. 16a,b), the precipitates in both the abiotic
controls (abiotic Ca7) and the biotic experiments (biotic Ca7) exhibit plate-like morphologies
with average dimensions of approximately 50 nm x 50 nm x 10 nm. Under higher saturation
state conditions (Figs. 16c,d) the precipitate in the abiotic control (abiotic Ca11) exhibits the
same characteristics as the abiotic control precipitate at low saturation states. However, the
biotic experiment at high saturation states (biotic Ca11) produces smaller precipitates, with
average dimensions of approximately 20 x 20 x < 10 nm. In Figures 16b and 16d, there appears
to be a spatial association between the mineral precipitate and the cell wall; however, it is
uncertain whether this association is coincidental or a result of the cell wall involvement in the
precipitation process. Therefore, although there is no evidence that passive cell wall nucleation
occurs in this system under the investigated conditions, the presence of the bacteria affects the
size of the mineral precipitate under high saturation state conditions.
2.4.3.2 XRD
Biotic Ca4 and abiotic and biotic Ca7 and Ca11 samples were characterized with XRD
(Figure 17). The abiotic controls each exhibit distinct peaks (e.g. at 2θ equal to 16.3, 26.1, 31.7,
and 32.6), but the peaks in the biotic experiment diffractograms are less distinct, with significant
peak broadening becoming more apparent with increasing saturation state. For example, in the
diffractogram for biotic Ca11, the peaks at 2θ of 26.1, 31.7, and 32.6 appear to be one broad
peak instead of the three distinct peaks seen in abiotic Ca11. The peak broadening effect that is
55
Figure 16: TEM bright field images for Ca system: (A) Abiotic Ca7
control; (B) Biotic Ca7 experiment; (C) Abiotic Ca11 control; (D) Biotic
Ca11 experiment. All scale bars are 100 nm.
56
Figure 17: XRD data from run-products of Ca experiments.
evident at the high saturation states in the biotic experiments likely results from the formation
of smaller precipitates under these conditions, as observed in the TEM images (Figure 16d).
Furthermore, the diffractograms from all of the Ca experiments are consistent with the
reference diffractograms (ICDD 01-071-5049) for hydroxylapatite (HA, Ca10(PO4)6(OH)2),
suggesting that HA is the dominant precipitate to form under all of the experimental conditions.
57
2.4.3.3 ICP-OES
Under virtually all of the saturation state conditions studied, the bacteria do not affect
the extent of Ca removal during precipitation relative to the abiotic controls (Figure 18). The
bacteria do, however, release P into solution, resulting in higher final P concentrations in
solution relative to both the abiotic controls and the starting conditions. With increasing
experimental P concentration, the input of P from the bacteria becomes less important relative
to the starting P concentration. At the highest saturation states studied (condition 2), the biotic
samples exhibit Ca concentrations that are approximately 0.25 log molality units higher than
those of the abiotic controls. As I observed in the U experiments, the elevated aqueous Ca
concentrations remaining in solution in the biotic experiments are likely a result of aqueous Ca
complexes with organic exudates. These complexes render the Ca unavailable for mineral
precipitation, and as a result the remaining aqueous Ca concentrations in the biotic experiments
are elevated relative to the abiotic controls.
2.4.3.4 Effect of bacteria on calcium phosphate precipitation
The results of the Ca experiments indicate that the presence of bacteria does not affect
the extent of Ca precipitation from solution, except at the highest saturation state conditions
investigated, where binding with bacterial exudates may affect the extent of Ca removal.
Bacterial cells do not affect the mineralogy of the precipitates in the Ca system. However, the
presence of bacteria results in a more fibrous morphology of the precipitates compared to that
seen in the abiotic controls, and results in a decrease in the size of the precipitate under high
saturation state conditions, as indicated by the TEM results. The size effect of the bacteria in the
Ca experiments is likely due to the presence of organic bacterial exudates in solution and the
interaction of these molecules with the precipitating HA particles. Lebron and Suarez (1996)
58
-5
-4
-3
-2
[Ca] final (log M)
-3
-3.5
-4
Starting Conditions
Abiotic Control
0.62 g wet biomass / L
-4.5
[P] final (log M)
Figure 18: Changes in the aqueous concentrations of Ca and P in the Ca experiments
with B. subtilis. All experiments were performed in duplicate. Error bars represent
one standard deviation (note that some error bars are smaller than the symbol). Each
arrow connects the starting condition (arrow tail, asterisks) to the final Ca and P
concentrations in the abiotic control or biotic experiments (arrow head, squares and
circles). The numerals “1” and “2” represent saturation state conditions discussed in
detail in the text and are presented here for reference.
reported a similar effect on the size of calcite precipitates in the presence of varying
concentrations of dissolved organic carbon (DOC). With increased concentrations of DOC,
Lebron and Suarez (1996) observed a decrease in calcite particle sizes from >100 μm at a DOC
concentration of 0.02 mM to <2 μm at a DOC concentration of 0.15 mM. Consistent with this
observation, the biotic experiment diffractograms exhibited a general peak broadening effect,
59
which became more pronounced with increasing saturation states. Studies have reported that
particle size and peak width in XRD diffractograms are inversely correlated, such that smaller
particles produce wider peaks in the diffractogram relative to the same mineral with a larger
particle size (Weibel et al., 2005; Sanchez-Bajo et al., 2006). The observed gradual peak
broadening effect in the biotic experiments with increasing saturation state suggests that the
precipitate size and/or crystallinity decrease as the saturation state increases.
2.5 Conclusions
In this study, I investigated the effects of non-metabolizing bacteria on the precipitation
of metal phosphates under a range of saturation states. The results demonstrate several distinct
bacterial effects. At high saturation states in the U system, I observed passive cell wall
nucleation of uranyl phosphate minerals within the cell wall framework of both B. subtilis and S.
oneidensis cells. These nucleated particles, although of the same mineralogy and morphology as
forms under abiotic conditions, were dramatically smaller than the abiotic precipitates.
Furthermore, the extent of U removal in the biotic systems was significantly reduced relative to
the abiotic controls, in part due to the elevated solubility of the smaller nucleated particles, and
in part due to the presence of bacterial exudate molecules that formed aqueous complexes with
U and prevented the same degree of uranyl phosphate precipitation as occurred in the abiotic
experiments. I did not observe the same passive cell wall mineralization phenomenon in the Ca
or Pb systems. However, the presence of bacteria did decrease the size of the precipitates in the
Ca system at high saturation state. The experimental results strongly suggest that the bacterial
effects that I observed are likely to be element and/or saturation state specific. It is likely that
highly stable metal-phosphoryl binding, such as exists in the U system, is required to trigger
metal-phosphate cell wall mineralization.
60
The observations provide the first comprehensive evidence for the passive cell wall
biomineralization of metal phosphates, in which the high binding affinity of cell walls for
aqueous metal cations creates nucleation sites for mineral precipitation reactions in saturated
systems. These nucleation sites likely promote heterogeneous nucleation of metal phosphates
on or in the cell wall through surface complexation reactions, as seen by Fowle and Fein (2001).
The passive cell wall biomineralization mechanism does not change the mineral that
precipitates. It does, however, exert a strong control on the size of the precipitate that forms
during the experiments.
2.6 Acknowledgements
Funding for this research was provided in part by a U.S. Department of Energy, Office of
Science and Technology and International (OST&I) grant under the Source Term Thrust program,
and in part by a U.S. Department of Energy, Environmental Remediation Science Program grant.
The experiments and analyses were performed at the Center for Environmental Science &
Technology, University of Notre Dame. The XAS measurements were obtained at the MRCAT-10ID Beamline at the Advanced Photon Source (APS), Argonne National Laboratory. TEM images
were obtained at the Integrated Imaging Facility at the University of Notre Dame and at the
Institut de Minéralogie et de Physique des Milieux Condensés, Paris, France. I would like to
thank Andrew Quicksall for providing suggestions and feedback throughout the project. Three
journal reviews were extremely helpful, and significantly improved the presentation of this
work.
61
CHAPTER 3:
THE EFFECTS OF CHLORIDE ON THE ADSORPTION
OF MERCURY ONTO THREE BACTERIAL SPECIES
3.1 Abstract
Bulk adsorption experiments were conducted in order to investigate the ability of three
bacterial species to adsorb Hg in the absence and presence of chloride from pH 2 to 10.
Adsorption experiments were performed using non-metabolizing cells of Bacillus subtilis,
Shewanella oneidensis MR-1, and Geobacter sulfurreducens suspended in a 0.1 M NaClO4
electrolyte to buffer ionic strength. After equilibration, the aqueous phases were sampled and
analyzed using inductively coupled plasma-optical emission spectroscopy (ICP-OES) for
remaining Hg concentrations.
In both chloride-free and chloride-bearing systems, the three bacterial species studied
exhibited similar adsorption behaviors. Chloride causes a dramatic shift in the adsorption
behavior of each of the bacterial species. In the absence of chloride, each of the species exhibits
maximum adsorption between pH 4 and 6, with decreasing but still significant adsorption with
increasing pH from 6 to approximately 10. The extent of Hg adsorption in the chloride-free
systems is extensive under all of the experimental conditions, and the concentration of
adsorbed Hg exceeds the concentration of any individual binding site type on the cell envelope,
indicating that binding onto multiple types of sites occurs even at the lowest pH conditions
studied. Because binding onto an individual site type does not occur exclusively under any of the
62
experimental conditions, individual stability constants for Hg-bacterial surface complexes cannot
be determined in the Cl-free system. In the presence of chloride, all of the bacterial species
exhibit minimal Hg adsorption below pH 4, increasing adsorption between pH 4 and 8, and
slightly decreasing extents of adsorption with increasing pH above 8. The low extent of
adsorption at low pH suggests that HgCl20, which dominates aqueous Hg speciation below pH
5.5, adsorbs only weakly. The increase in Hg adsorption above pH 4 is likely due to adsorption of
HgCl(OH)0, and is limited by site availability and transformation to Hg(OH) 20 as pH increases. I
use the adsorption data to determine stability constants of the HgCl(OH)- and Hg(OH)2-bacterial
cell envelope complexes, and the values enable estimations to be made for Hg adsorption
behavior in bacteria-bearing geologic systems.
3.2 Introduction
Bacteria are present in soils and groundwater systems (Madigan et al., 2009), and
adsorption onto bacterial cell envelope functional groups can affect the speciation, distribution
and transport of heavy metals (Beveridge and Murray, 1976; Fortin et al., 1997; Ledin et al.,
1999; Small et al., 1999; Daughney et al., 2002). Although the adsorption behaviors of a wide
range of bacteria have been studied for a wide range of metals (e.g., Beveridge and Murray,
1976, 1980; Beveridge, 1989; Mullen et al., 1989; Fein et al., 1997, 2002; Borrok et al, 2004,
2007; Wu et al., 2006), Hg has received less attention. Recent studies have found that protonactive sulfhydryl functional groups exist on the surface of bacterial cell envelopes (Guine et al.,
2006; Mishra et al., 2009; 2010). Many studies have demonstrated that Hg has a high binding
affinity for sulfur compounds (Fuhr and Rabenstein, 1973; Blum and Bartha, 1980; Compeau and
Bartha, 1987; Winfrey and Rudd, 1990; Benoit et al., 1999), and thus the adsorption of Hg to
bacteria may be dominated by this type of binding. Due to the high affinity for this bond to
63
form, bacteria have the potential to drastically affect the distribution, transport and fate of Hg in
soil and groundwater systems.
Several studies have investigated the extent to which bacteria adsorb Hg (Chang and
Hong, 1994; Ledin et al., 1997; Green-Ruiz, 2006; Mo and Lian, 2011), and all have observed that
Hg is extensively removed from solution in the presence of bacteria under a range of
experimental conditions, with Hg adsorption typically more extensive than that of other heavy
metals (Hassen et al., 1998). The extent of metal adsorption to bacteria can be quantified using
surface complexation models (SCMs). SCMs have been applied to a range of metal-bacteria
systems (e.g., Plette et al., 1996; Fein et al., 1997; Daughney and Fein, 1998; Cox et al., 1999;
Fowle and Fein, 2000; Borrok et al., 2004), though only one study has used this approach to
model Hg adsorption onto bacteria (Daughney et al., 2002). Daughney et al. (2002) measured Hg
adsorption onto Bacillus subtilis, a Gram-positive bacterial species, as a function of bacteria-toHg ratio, pH, chloride concentration, bacterial growth phase and reaction time, and used the
data to constrain stability constants for both chloride-free and chloride-bearing Hg-bacterial
surface complexes. It is crucial to test the accuracy of these stability constants, and also to
determine if other bacterial species exhibit similar Hg adsorption behavior. Adsorption
represents the first, and rate controlling, step in the bioavailability of some metals to bacteria
(Borrok et al., 2004; Sheng et al., 2011), so determining accurate and precise stability constant
values for Hg-bacterial surface complexes may be crucial for quantitative modeling of processes
such as bacterial Hg-methylation and Hg toxicity.
In this study, I test the findings of Daughney et al. (2002) by measuring Hg adsorption
onto B. subtilis, and I expand on the Daughney et al. (2002) study by measuring Hg adsorption
behavior onto two other representative bacterial species. Bacterial adsorption experiments
were conducted as a function of pH and chloride concentration using intact washed non64
metabolizing bacterial cells. In addition to the experiments involving the Gram positive species
Bacillus subtilis, I conducted parallel experiments involving a common Gram negative bacterial
species (Shewanella oneidensis MR-1) and a Gram negative species that is capable of Hg
methylation (Geobacter sulfurreducens) in order to investigate if cell envelope type affects Hg
adsorption and/or if methylating species exhibit unique Hg binding properties. I used the
experimental results to construct surface complexation models that enable the calculation of Hg
speciation and distribution in a wide range of natural and engineered bacteria-bearing systems.
3.3 Methods
3.3.1 Experimental Methods
3.3.1.1 Bacterial Growth & Washing Procedure
Bacillus subtilis (ATCC 23875) and Shewanella oneidensis MR-1 (ATCC BAA-1096) cells
were cultured and prepared aerobically following the procedures outlined in Borrok et al.
(2007). Briefly, cells were maintained on agar plates consisting of trypticase soy agar with 0.5%
yeast extract added. Cells for all experiments were grown by first inoculating a test-tube
containing 3 mL of trypticase soy broth with 0.5% yeast extract, and incubating it for 24 h at 32
C. The 3 ml bacterial suspension was then transferred to a 1 L volume of trypticase soy broth
with 0.5% yeast extract for another 24 h on an incubator shaker table at 32 C. Cells were
pelleted by centrifugation at 8100g for 5 min, and rinsed 5 times with 0.1 M NaClO4.
Geobacter sulfurreducens (ATCC 51573) cells were cultured and prepared using a
different procedure than described above. Cells were maintained in 50 mL of anaerobic
freshwater basal media (ATCC 51573) at 32 oC (Lovely and Phillips, 1988). Cells for all
experiments were grown by first inoculating an anaerobic serum bottle containing 50 mL of
65
freshwater basal media, and incubating it for 5 days at 32 oC. Cells were pelleted by
centrifugation at 8100g for 5 minutes, and rinsed 5 times with 0.1 M NaClO4 stripped of
dissolved oxygen by bubbling a 85%/5%/10% N2/H2/CO2 gas mixture through it for 30 minutes.
After washing, the three types of bacteria used in this study were then pelleted by
centrifugation at 8100g for 60 minutes to remove excess water to determine the wet mass so
that suspensions of known bacterial concentration could be created. All bacterial concentrations
in this study are given in terms of gm wet biomass L-1.
3.3.1.2 Bacterial Potentiometric Titrations
Surface complexation modeling requires determination of bacterial cell envelope site
concentrations and acidity constants. These parameters have been determined previously for B.
subtilis (Fein et al., 2005) and S. oneidensis MR-1 (Mishra et al., 2010), but they have not been
determined for G. sulfurreducens. To obtain these values, four replicate potentiometric
titrations of G. sulfurreducens cells (100 gm L-1) were conducted in 0.1 M NaClO4 under a N2
atmosphere with an automated burette assembly. The biomass suspension was prepared using
washed biomass and 0.1 M NaClO4 that was purged with N2 gas for 30 minutes prior to the
preparation of the suspension. The suspension pH was measured using a glass electrode filled
with 4 M KCl that was standardized using commercially supplied pH standards. The titrations
were performed by measuring the pH after each addition of aliquots of commercially supplied
volumetric standard of 1.030 M NaOH or 1.048 M HCl to the suspension. Acid or base additions
were made only after a maximum drift of 0.01 mV/s was attained.
The biomass suspension was titrated first with HCl to achieve a pH of ~2.0. The
suspension was then titrated with NaOH to a pH of ~10.0. Titrations of the electrolyte solution
66
only were performed before and after each biomass titration to verify mechanical accuracy and
reproducibility.
3.3.1.3 Batch Adsorption Experiments
Aqueous Hg(II), chloride, and suspended bacteria parent solutions were prepared using
circum-neutral 0.1 M NaClO4 electrolyte solution (pH adjusted to 7.0 ± 0.5 using 0.2 M HNO3
and/or 0.2 M NaOH), and either 1,000 ppm Hg(II) or Cl - volumetric aqueous standards, or
washed bacterial cells (as described above). The parent solutions were mixed together in the
following order: an aliquot of chloride parent solution was added to a bacterial suspension, and
then an aliquot of Hg(II) parent solution was added and the mixture was diluted with 0.1 M
NaClO4 to achieve a suspension with a log molality of Hg of -4.13, a log molality of Cl- of -3.00,
and either 0.2 gm wet biomass L-1 (for the B. subtilis and S. oneidensis experiments) or 0.1 gm
wet biomass L-1 (for the G. sulfurreducens experiments). Chloride-free experiments were also
conducted and prepared in an identical fashion, but excluding the chloride addition. Eight mL
aliquots of the suspension were added to 20 Teflon reaction vessels and the pH of each aliquot
was immediately adjusted to cover the pH range from 2 to 10, using 0.2 M HNO 3 and/or NaOH,
and the vessels were placed on an end-over-end rotator for the duration of the experiment (2 h
for B. subtilis and G. sulfurreducens, and 3 h for S. oneidensis). Kinetics experiments (data not
shown) were conducted to determine the duration required for each system to attain steadystate conditions. The pH of each experiment was monitored and adjusted if necessary using 0.2
M HNO3 and/or NaOH every 15 minutes throughout the duration of the experiment except
during the last 30 minutes, during which the suspensions were undisturbed. At the completion
of each experiment, the final pH of each solution was measured and the contents were filtered
through a 0.2μm PTFE syringe filter to remove the bacteria. The aqueous phase was collected
67
and acidified using 15.8 N HNO3 at a sample:acid ratio of 5 mL:8 μL and refrigerated pending
aqueous Hg analysis. All experiments were performed under atmospheric, room temperature
conditions. Three replicate experiments were conducted for each experimental condition.
3.3.2 Analytical Methods: Inductively-Coupled Plasma – Optical Emission Spectroscopy (ICP-OES)
Ionic strength matrix-matched ICP-OES standards were prepared gravimetrically by
diluting a commercially-supplied 1,000 ppm Hg(II) aqueous standard with 0.1 M NaClO4, and
each standard was acidified using 8 μL of 15.8 N HNO3 per 5 mL sample. The log molality of the
Hg standards ranged from -6.30 to -4.05. The standards and samples were analyzed with a
Perkin Elmer 2000DV ICP-OES at a wavelength of 253.652 nm within 2 days of collection. The set
of standards was analyzed before and after all of the samples were analyzed, as well as after
every 15 samples, to check for machine drift. Analytical uncertainty, as determined by repeat
analyses of the standards, was ± 5.6%.
3.3.3 Thermodynamic Modeling
I used a non-electrostatic surface complexation approach to model proton and Hg
adsorption onto bacterial cell envelope functional groups (Fein et al., 1997; 2005). That is, I
modeled the acidity of surface functional groups via deprotonation reactions:
(1) R-AiH  R-Ai- + H+
where R represents the cell envelope macromolecule to which each type of functional group,
Ai, is attached. The distribution of protonated and deprotonated functional group sites can be
quantified via mass action equations, such as:

(2) K i 
[ R  Ai ] a H 
[ R  Ai H o ]
68
where Ki represents an acidity constant, a represents the activity of the subscripted species, and
the brackets represent the activities of surface sites in moles L-1 of solution.
In applying this approach to modeling the surface acidity of bacteria, I implicitly
assumed that the deprotonation of each type of functional group, Ai, can be represented as a
single deprotonation of an organic acid. Because all of the experiments were conducted at the
same ionic strength, I ignored potential ionic strength effects on the surface electric field,
applying a non-electrostatic model to account for the titration and Hg adsorption data.
Potentiometric titration experiments are essentially studies of proton adsorption and
desorption, yet because the solvent contains the same element as is reacting with the surface of
interest, it is impossible to apply a traditional mass balance approach. Instead, one must define
a zero proton condition for the bacterial cell envelope, and account for changes in proton
concentrations relative to that condition (e.g., Westall et al., 1995; Fein et al., 2005). After the
approach by Fein et al. (2005), I chose fully protonated cell envelope sites to represent the zero
proton condition, and I used FITEQL (Westall, 1982) to solve for the initial state of protonation in
each titration (Westall et al., 1995).
As is discussed below, the extent of Hg adsorption that I observed in the chloride-free
systems was too extensive to be able to isolate or model the extent of adsorption onto
individual cell envelope sites. For the chloride systems, I model the observed adsorption as
interactions between aqueous Hg species and deprotonated bacterial cell envelope sites:
(3) Hg species+x + R-Ai-  (R-Ai)(Hg species)x-1
where ‘Hg species+x’ represents the specific aqueous Hg species tested in each model, ‘(R-Ai)(Hg
species)x-1 represents the Hg-bacterial cell envelope complex, and x represents the charge of the
aqueous Hg species. The mass action equation for Reaction 3 is:
69
Kads 
(4)
[( R  Ai )( Hg species ) ( x 1)  ]

a ( Hg species  x ) [ R  Ai ]
where Kads is the thermodynamic equilibrium constant for Reaction 3, a represents the activity
of the species in parentheses, and brackets represent concentrations in mol L-1. Acid/base
potentiometric titration data provide constraints on the number of site types, their Ki values and
their site concentrations; Hg adsorption measurements conducted as a function of pH constrain
the number of sites involved in Hg binding, the pH range of influence, and the stability constants
for the important Hg-bacterial cell envelope complexes. I used the program FITEQL 2.0 (Westall,
1982) for the equilibrium thermodynamic modeling of the adsorption data, using the aqueous
speciation equilibria and equilibrium constants given in Table 9, and using the program’s activity
coefficient calculations via the Davies equation.
3.4 Results & Discussion
3.4.1 Potentiometric Titrations
Potentiometric titrations of G. sulfurreducens biomass were performed in order to calculate site
concentrations and pKa values for discrete proton-active cell envelope functional groups. G.
sulfurreducens exhibits significant proton buffering behavior over the entire pH range studied.
Each of the four replicate G. sulfurreducens sets of titration data is depicted in Figure 19. G.
sulfurreducens exhibits a similar total buffering capacity ((C(a) – C(b) – [H+] + [OH-]) / mb) to that
measured for other bacterial species. For example, between pH 3 and 9, G. sulfurreducens has a
buffering capacity of 3.5 ± 0.6 x 10-4 mol/g (reported error represents 1σ uncertainty), compared
to a value of 3.0 x 10-4 mol/g for Bacillus subtilis (Fein et al., 2005), 3.1 x 10-4 for Shewanella
oneidensis (Mishra et al., 2010), and 1.27 x 10-4 and 2.23 x 10-4 mol/g for Acidiphilium
70
acidophilum and Bacillus pseudofirmus, respectively (Kenney and Fein, 2011). Borrok et al.
(2005) observed that a wide range of bacterial species exhibit similar buffering behaviors, and
the titration data demonstrate that G. sulfurreducens exhibits that same buffering behavior.
The potentiometric titration data were used to quantify the site concentrations and acidity
constants for G. sulfurreducens. One-, 2-, 3-, 4-, and 5-site models were tested in order to
determine the number of proton-active surface site types on G. sulfurreducens cell envelopes
needed to account for the observed buffering behavior. The addition of each additional site
significantly lowered the V(Y) (variance) value from an average of 185.6 for the 1-site models of
the 4 titrations to an average of 0.26 for the 4-site models (an ideal V(Y) value is 1). A 5-site
model failed to converge for each set of titration data, indicating insufficient experimental data
to constrain parameters for 5 site types. Figure 20 shows a representative titration for G.
sulfurreducens with the corresponding best fit 4-site model. The model yields an excellent fit to
the observed buffering behavior across the pH range of the study. G. sulfurreducens has similar
site concentrations and pKa values to B. subtilis and S. oneidensis (Table 10), though G.
sulfurreducens has the lowest concentration of total surface sites of the three species. The
presented site concentrations and pKa values in Table 10 represent averages of the 4 individual
forward titration model results, and are used as a basis for the Hg adsorption modeling.
71
TABLE 9
HG REACTIONS USED TO CONSTRUCT SCMS
Reaction
Log K
H2O – H+ = OHH2CO30 – H+ = HCO3H2CO30 – 2H+ = CO32H2CO30 – H2O = CO20
+
Na + H2CO30 – 2H+ = NaCO3Na+ + H2CO30 – H+ = NaHCO30
Na+ + H2O – H+ = NaOH0
Hg2+ + H2O - H+ = HgOH+
Hg2+ + 2H2O - 2H+ = Hg(OH)20
Hg2+ + 3H2O - 3H+ = Hg(OH)32Hg2+ + H2O - H+ = Hg2(OH)3+
3Hg2+ + 3H2O - 3H+ = Hg3(OH)33+
Hg2+ + H2CO30 – 2H+ = HgCO30
Hg2+ + H2CO30 – H+ = HgHCO3+
2+
Hg + H2CO30 + H2O – 3H+ = Hg(OH)CO3Hg2+ + Cl- = HgCl+
Hg2+ + 2Cl- = HgCl20
Hg2+ + 3Cl- = HgCl3Hg2+ + 4Cl- = HgCl42Hg2+ + Cl- + H2O – H+ = HgCl(OH)0
-14.00 b
-6.355 a
-16.67 a
2.770 b
-15.41 b
-6.60 b
-14.2 b
-3.40 a
-5.98 a
-21.1 a
-3.30 b
-6.40 b
-3.91 a
0.42 a
-11.355 a
7.31 a
14.00 a
14.925 a
15.535 a
4.27 a
(a) Powell et al., 2005.
(b) Martell and Smith, 2001.
72
2.5E-04
[c(a) - c(b)] / gm bacteria
2.0E-04
1.5E-04
1.0E-04
5.0E-05
0.0E+00
-5.0E-05
-1.0E-04
2
4
6
pH
8
Figure 19: Four replicate forward potentiometric titration of 100 gm L-1
G. sulfurreducens in 0.1 M NaClO4.
73
10
2.5E-04
2.0E-04
(Ca - Cb) / mb
1.5E-04
1.0E-04
5.0E-05
0.0E+00
2
3
4
5
6
7
8
-5.0E-05
-1.0E-04
pH
Figure 20: Best fit 4-site model results (smooth curve) for one
representative potentiometric titration of G. sulfurreducens (data
points).
74
9
10
TABLE 10
SITE CONCENTRATIONS AND PKA VALUES USED FOR SCMS
Bacteria
Site 1
Site 2
Site 3
Site 4
Site Concentrations (mol sites / gm bacteria)
a
-5
-4
75
B. subtilis
S. oneidensisb
G. sulfurreducens
8.1 ± 1.6 x 10
8.9 ± 2.6 x 10-5
8.4 ± 0.66 x 10-5
1.1 ± 0.36 x 10
1.3 ± 0.20 x 10-4
9.1 ± 0.41 x 10-5
B. subtilisa
S. oneidensisb
G. sulfurreducens
-3.3 ± 0.2
-3.3 ± 0.2
-3.4 ± 0.1
-4.8 ± 0.1
-4.8 ± 0.2
-4.8 ± 0.1
-5
[sites]total
-5
4.4 ± 1.3 x 10
5.9 ± 3.3 x 10-5
4.1 ± 0.24 x 10-5
7.4 ± 2.1 x 10
1.1 ± 0.60 x 10-4
3.4 ± 0.63 x 10-5
-6.8 ± 0.3
-6.7 ± 0.4
-6.5 ± 0.2
-9.1 ± 0.2
-9.4 ± 0.5
-8.8 ± 0.3
pKa
Reported uncertainties are 1σ errors.
(a) Fein et al., 2005.
(b) Mishra et al., 2010
3.1 x 10-4
3.9 x 10-4
2.5 x 10-4
3.4.2 Adsorption Experiments
In the absence of chloride, the adsorption behavior as a function of pH is more complex
than has been observed for other metal cations (e.g., Fein et al., 1997; Fein et al., 2001), with
the extent of adsorption increasing from pH 2 to 4, and in general decreasing from 4 to 9 (Figure
21). The extent of Hg adsorption that I observed is notable. In the chloride-free experiments, B.
subtilis, S. oneidensis, and G. sulfurreducens adsorbed a maximum of approximately 2.0 x 10-4,
3.0 x 10-4, and 3.5 x 10-4 mol Hg per gm (wet mass) of bacteria, respectively. In similar Hg
adsorption experiments involving B. subtilis but conducted under much lower Hg loading
conditions, Daughney et al. (2002) measured a maximum of only approximately 5.0 x 10-6 mol
Hg per gm (wet mass). Clearly, these bacteria exhibit a much higher capacity for Hg than was
probed by the Daughney et al. (2002) experiments.
In the presence of chloride, the pH dependence of Hg adsorption that I observed
changes dramatically to that typically observed with metal cations, even though the dominant
aqueous Hg species are neutral or negatively charged. There is only a small extent of adsorption
below pH 4, with adsorption increasing slightly from pH 2 to 4; the extent of adsorption
increases more markedly with increasing pH between approximately pH 4 and 8, and the extent
of adsorption decreases slightly with increasing pH above pH 8. The addition of chloride to the
experimental system significantly decreases the extent of Hg adsorption onto the bacteria under
low pH conditions relative to the chloride-free system, and does not markedly affect the extent
of Hg adsorption above pH 6 (Figure 22).
In general, the bacterial species tested exhibit broadly similar bulk adsorption behaviors
in the absence and presence of chloride, although there are some differences. In the chloride-
76
[Hg] adsorbed (mol) / gm bacteria
4.0E-04
B. subtilis
3.5E-04
S. oneidensis
3.0E-04
G. sulfurreducens
2.5E-04
2.0E-04
1.5E-04
1.0E-04
5.0E-05
0.0E+00
0
2
4
6
pH
8
10
12
Figure 21: Hg adsorption onto bacterial species normalized per gram of bacteria. The
initial molality of Hg in the adsorption experiments is 7.41 x 10-5.
free system, although experimental uncertainties are relatively high, in the mid-pH range tested,
G. sulfurreducens removes more Hg from solution than S. oneidensis which removes more than
B. subtilis; the differences between species are less under lower and higher pH conditions. In the
chloride systems, B. subtilis and G. sulfurreducens remove nearly identical amounts of Hg from
solution, but above pH 4, S. oneidensis removes more Hg than the other two species.
3.4.3 Thermodynamic Modeling
The effects of pH and chloride on the adsorption of Hg onto the bacteria studied here
likely reflect both changes to the speciation of the cell envelope functional groups and changes
in the aqueous Hg speciation that accompany the pH and chloride concentration changes. In
order to determine the dominant adsorption reactions, it is crucial to define the speciation of Hg
77
2.5E-04
[Hg] adsorbed (mol) / gm bacteria
B. subtilis
S. oneidensis
2.0E-04
G. sulfurreducens
1.5E-04
1.0E-04
5.0E-05
0.0E+00
1
2
3
4
5
6
pH
7
8
9
10
11
Figure 22: Hg adsorption onto bacterial species, normalized per gram of bacteria, in
the presence of chloride. The solid black curve represents the model fit for B. subtilis,
the dashed black line represents the model fit for S. oneidensis, and the solid grey line
represents the model fit for G. sulfurreducens. The initial molality of Hg in the
adsorption experiments is 7.41 x 10-5 and the initial molality of Cl is 1.00 x 10-3.
in solution. Aqueous Hg speciation diagrams, calculated for the experimental conditions using
the aqueous complexation reactions and stability constants listed in Table 9, are depicted in
Figure 23.
In the chloride-free system, the extent of Hg adsorption that I observed is greater under
all pH conditions than any of the individual binding site concentrations, meaning that under all
conditions multiple site types must be responsible for the observed adsorption. For example,
approximately 1.0 x 10-4 mol of Hg are adsorbed per gram of B. subtilis at pH 2 (Figure 21), which
represents a total concentration of adsorbed Hg of 2.1 x 10-5 M. However, the total
concentration of Site 1, which deprotonates at the lowest pH of the 4 potential binding site
78
Figure 23: Aqueous Hg speciation in the (A) absence and (B) presence of
chloride under the experimental Hg and chloride concentration
conditions. Only species with calculated concentrations above 0.01 x 105
M are shown.
79
types, is only 1.6 x 10-5 M. At pH 4.5, B. subtilis exhibits a maximum extent of Hg adsorption with
4.3 x 10-5 M Hg adsorbed. The total concentration of all four binding site concentrations is 6.2 x
10-5 M, again suggesting that more than one type of site is involved in Hg binding. It is unusual to
observe such a high degree of site saturation by an adsorbing metal, and this behavior suggests
that Hg-bacterial site stability constants are quite high. However, because individual Hg-site
binding could not be isolated under any of the experimental conditions, individual Hg-site
stability constants could not be determined in the chloride-free system.
In the chloride system, I observed a stronger pH dependence for the Hg adsorption, and
under low pH conditions the extent of adsorption is less than the concentration of any individual
binding site type. Because Hg-chloride species dominate in the chloride experiments, and
because chloride-free bacterial species are unlikely to be important under those conditions, the
lack of stability constants for the chloride-free system does not hinder the ability to model the
chloride system. The dramatic effect of chloride on the adsorption behavior is paralleled by the
drastic change in aqueous Hg speciation with the addition of chloride (Figure 23b). Under the
conditions of my experiments and below pH 6, HgCl20 is the dominant Hg species; however,
under those pH conditions little to no Hg adsorption is observed, suggesting that HgCl 20 does not
adsorb to bacterial functional groups strongly. Adsorption in the chloride systems increases with
increasing pH above pH 4, similar to the behavior of HgCl(OH)0. Although Daughney et al. (2002)
modeled Hg adsorption onto B. subtilis as HgCl20 and HgCl(OH)0 binding onto a protonated
bacterial site in order to account for the pH-independent adsorption that they observed under
low pH conditions, I tested a range of models involving deprotonated sites only due to the
absence of adsorption in the experiments under the low pH conditions at which protonated
sites dominate the cell wall speciation. My general approach was to model the adsorption
behavior using the minimum number of adsorbed Hg species required. Because more binding
80
site types become deprotonated with increasing pH, I modeled the low pH adsorption data first
and determined whether additional adsorbed Hg species were required in order to account for
the higher pH data. For each bacteria, Hg adsorption increased slightly from pH 2 to 4, under
conditions where HgCl20 dominates the aqueous Hg speciation and R-A11- increases in
concentration due to the deprotonation of this site over this pH range. Therefore, I modeled the
pH 2-4 data for each bacterial species with the following reaction:
(5) HgCl20 + R-A11-  R-A1-HgCl21In order to determine if an additional complex is required to account for the observed Hg
adsorption, I used the calculated value for the equilibrium constant for Reaction (5) to predict
the adsorption behavior under higher pH conditions, assuming that only the R-A1-HgCl21complex controls the Hg adsorption behavior. For example, Figure 24 depicts the fit of Reaction
(5) to the data from the B. subtilis experiments. The predicted Hg adsorption behavior using
Reaction (5) fits the experimental data well from pH 2 to 4 (the pH range used initially to
constrain the K value for this reaction), but dramatically under predicts the extent of adsorption
that I observed at higher pH values. This under-prediction represents strong evidence for the
presence of an additional adsorbed Hg species or multiple species above pH 4. Above
approximately pH 4, HgCl(OH)0 increases in concentration markedly (Figure 23b), mirroring the
dramatic increase in Hg adsorption that I observed above pH 4. For this reason, I added the
following reaction to the model:
(6) HgCl(OH)0 + R-A21-  R-A2-HgCl(OH)1and used the pH 2-6 data from the B. subtilis experiments to simultaneously solve for K values
for Reactions (5) and (6). Again, I used these calculated K values to predict the adsorption
behavior above pH 6 (Figure 24), and find that as expected Reactions (5) and (6) provide an
81
excellent fit to the data up to pH 6, but that the concentrations of sites R-A1 and R-A2 limit the
predicted extent of adsorption which plateaus significantly below the observed extent of Hg
adsorption, and then drops to even lower concentrations with increasing pH as the
concentration of HgCl(OH)0 in solution decreases and the concentration of Hg(OH)20 increases
above pH 7. Following the same modeling approach, I determined that the observed Hg
adsorption data from the B. subtilis experiments require an additional Hg-bacterial surface
complex to account for the pH dependence across the pH range studied, represented by the
following adsorption reaction:
(7) HgCl(OH)0 + R-A31-  R-A3-HgCl(OH)1I solved for K values for Reactions (5) – (7) simultaneously with the entire dataset from the B.
subtilis experiments, and the resulting model yields an excellent fit to the data across the pH
range studied (Figure 24). Models that involve adsorption of Hg(OH)20 onto any of the binding
sites yielded significantly worse fits to the data. Similar modeling exercises were applied to the
G. sulfurreducens and the S. oneidensis datasets (Figures 25 and 26), and the calculated K values
from each of the three datasets are listed in Table 11.
For the G. sulfurreducens model, the results are similar to the B. subtilis model, with
HgCl20 adsorbing to R-A11- and HgCl(OH)0 adsorbing to R-A21- and R-A31-. However, the G.
sulfurreducens data also require an additional high pH Hg-bacterial species, and the data are
best-fit with the inclusion of the following reaction:
(8) Hg(OH)20 + R-A41-  R-A4-Hg(OH)21Similarly, the S. oneidensis model results are comparable to the G. sulfurreducens model,
requiring Reactions (5) – (8) to constrain the data. In addition, a fifth Hg species, presented in
82
3.5E-05
3.0E-05
[Hg] adsorbed (M)
2.5E-05
2.0E-05
1.5E-05
1.0E-05
5.0E-06
0.0E+00
1
2
3
4
5
6
pH
7
8
9
10
Figure 24: Comparison of model fits (curves) to B. subtilis experimental data (solid
squares) for the adsorption of Hg according to Reaction(s): (5) only (dashed grey
curve); (5) and (6) (dotted black curve); and (5), (6), and (7) (solid black curve).
83
11
2.5E-05
[Hg] adsorbed (M)
2.0E-05
1.5E-05
1.0E-05
5.0E-06
0.0E+00
1
2
3
4
5
6
pH
7
8
9
10
11
Figure 25: Comparison of model fits (curves) to G. sulfurreducens experimental data
(solid squares) for the adsorption of Hg according to Reaction(s): (5) only (dashed grey
curve); (5) and (6) (dotted black curve); (5), (6), and (7) (long dashed grey curve); and
(5), (6), (7), and (8) (solid black curve). Using only Reactions (5) through (7), as was used
for the B. subtilis modeling, results in a model fit that poorly constrains the data at high
pH, indicating that another reaction is necessary to account for the observed Hg
adsorption. It is likely that Hg(OH)20 is involved in the high pH adsorption, as it is the
dominant aqueous Hg species at high pH. Adding Hg(OH)20 onto R-A41- (Reaction (8))
yields a model fit that fits the data well across the entire pH range.
84
5.0E-05
[Hg] adsorbed (M)
4.0E-05
3.0E-05
2.0E-05
1.0E-05
0.0E+00
1
2
3
4
5
6
pH
7
8
9
10
11
Figure 26: Comparison of model fits (curves) to S. oneidensis experimental data (solid
squares) for the adsorption of Hg according to Reaction(s): (5) only (dashed grey curve);
(5) and (6) (dotted black curve); (5), (6), and (7) (long dashed grey curve); (5), (6), (7),
and (8) (solid grey curve); and (5), (6), (7), (8), and (9) (solid black curve). Using only
Reactions (5) through (8), the model does not constrain the high pH data well, thus an
additional surface species is necessary. It is likely that Hg(OH)20 is involved in the high pH
adsorption because it is the dominant aqueous Hg species under the high pH conditions
where we see a misfit between the data and the model predictions. Models invoking
Hg(OH)20 adsorption onto R-A31- or onto R-A41- do not improve the model fit, as these
reactions cause less HgCl(OH)0 to adsorb onto these sites due to site mass balance
constraints. However, a model that involves Hg(OH)20 adsorption onto R-A21- (solid black
curve) yields an excellent fit to the data across the pH range studied.
85
TABLE 11
CALCULATED STABILITY CONSTANTS (LOG K) FOR HG ADSORPTION ONTO BACTERIA
Bacteria
Rxn (5)
Rxn (6)
Rxn (7)
Rxn (8)
Rxn (9)
B. subtilis
G. sulfurreducens
S. oneidensis
3.9 ± 0.2
3.9 ± 0.4
3.7 ± 0.2
4.3 ± 0.4
6.4 ± 0.4
5.4 ± 0.3
5.1 ± 0.3
3.6 ± 0.2
8.9 ± 0.5
8.4 ± 0.5
7.6 ± 0.4
4.2 ± 0.2
Reported uncertainties were determined by varying each average K value individually to create
an adsorption envelope that encompasses 95% of the data within the pH range of influence.
the following reaction, was required to fully constrain the data (see explanations in captions for
Figures 25 and 26):
(9) Hg(OH)20 + R-A21-  R-A2-Hg(OH)21The thermodynamic modeling results suggest there is some variance between stability
constants for each bacterial species. The stability constants for Reaction (5) and (8) do not vary
between the bacterial species studied within experimental uncertainty, however the stability
constants for Reactions (6) and (7) exhibit significant variation between the species. Some of this
variation certainly reflects real differences between the bacterial species. However, the
differences between the K values for G. sulfurreducens and B. subtilis, which exhibit similar
extents of adsorption and similar site concentrations and pKa values, may reflect the fairly large
experimental uncertainty associated with the G. sulfurreducens data.
86
Though my study is similar to that of Daughney et al. (2002), both my observed Hg
adsorption behaviors and the models that I used to account for that adsorption differ from
those of the Daughney et al. (2002) study in some ways. Daughney et al. (2002) observed
significant and relatively pH-independent adsorption below pH 4-5 and above pH 7-8, and
modeled that behavior by invoking HgCl20 and HgCl(OH)0 onto protonated sites. I used the
reactions and calculated stability constants from Daughney et al. (2002) to predict the extent of
adsorption under the experimental conditions. The resulting predicted extents of adsorption
(not shown) are inconsistent with the measurements, yielding pH-independent adsorption
across the pH range of my study at a level that indicates complete saturation of bacterial binding
sites under all pH conditions. This result is likely due to an inconsistency between the Daughney
et al. (2002) K values and their reported reaction stoichiometries, and for this reason my
reported model will likely yield more accurate predictions of Hg binding behavior in a wide
range of geologic and engineered systems.
3.5 Conclusions
In this study, I documented extensive adsorption onto three different bacterial
envelopes in both chloride-free and chloride-bearing systems. The experimental results
demonstrate that Hg adsorption to bacterial species is dependent upon pH, chloride
concentration, and bacterial surface site speciation. In the absence of a competitive ligand, such
as chloride, Hg adsorption to bacterial cells does not exhibit typical metal cation adsorption
behavior. Additionally, the extent of Hg adsorption onto surface sites in the absence of a ligand
is extensive, with the concentration of adsorbed Hg exceeding the concentration of any
individual site type under all of the pH conditions tested. In the presence of chloride, the
behavior of Hg adsorption changes dramatically, with increasing adsorption as pH increases
87
likely due to the relatively weak interaction of the aqueous HgCl 20 complex with bacterial
binding sites. Thermodynamic modeling results suggest that the adsorption of HgCl(OH) 0 and
Hg(OH)2 onto bacterial surface sites are the dominant adsorption reactions under most
conditions studied, with log stability constant values ranging from 4.2 to 8.9. These results can
be used to help better understand the thermodynamics of Hg-Cl-bacterial interactions under
natural geologic conditions, such as in chloride-rich seawater and bacteria-laden groundwater,
and the results suggest that bacteria are likely to compete effectively with a range of other
ligands present in geologic environments to control Hg distribution and speciation.
3.6 Acknowledgements
The experiments and analyses were performed at the Center for Environmental Science
& Technology, University of Notre Dame. I would like to thank Jennifer Szymanowski and Brian
Farrell for assistance with data collection and processing.
88
CHAPTER 4:
THE EFFECT OF NATURAL ORGANIC MATTER ON THE
ADSORPTION OF MERCURY TO BACTERIAL CELLS
4.1 Abstract
I investigated the ability of non-metabolizing Bacillus subtilis, Shewanella oneidensis
MR-1, and Geobacter sulfurreducens bacterial species to adsorb mercury in the absence and
presence of Suwanee River fulvic acid. Bulk adsorption experiments were conducted at three pH
conditions, and the results indicate that the presence of FA decreases the extent of Hg
adsorption to biomass under all of the pH conditions studied. I used the experimental results to
calculate apparent binding constants for Hg onto both the bacteria and the FA. The calculations
yield similar binding constants for Hg onto each of the bacterial species studied. The calculations
also indicate similar binding constants for Hg-bacteria and Hg-FA complexes, and the values of
these binding constants suggest a high degree of covalent bonding in each type of complex,
likely due to the presence of significant concentrations of sulfhydryl functional groups on each.
My results suggest that although FA can compete with bacterial binding sites for aqueous Hg,
because of the relatively similar binding constants for the types of sorbents, the competition is
not dominated by either bacteria or FA unless the concentration of one type of site greatly
exceeds that of the other.
89
4.2 Introduction
Heavy metals, such as Hg, adsorb to proton-active functional groups on bacterial cell
envelopes (e.g., Beveridge and Murray, 1976; Fortin and Beveridge, 1997; Daughney et al., 2002;
Fein, 2006; Kenney and Fein, 2011), affecting the speciation and distribution of these metals in
geologic systems. Recent studies (e.g., Guiné et al., 2006; Mishra et al. 2009; 2010) have shown
that at least some bacterial cell envelopes contain proton-active sulfhydryl functional groups.
Because Hg binds readily and strongly to sulfur compounds (Compeau and Bartha, 1987;
Winfrey and Rudd, 1990; Benoit et al., 1999), bacterial adsorption of Hg may dramatically affect
the distribution, transport and fate of Hg in geologic systems.
Natural organic matter (NOM) is present in nearly every near-surface geologic system,
and complexation reactions between metals and NOM can dramatically change the behavior of
the metals in the environment (McDowell, 2003; Ravichandran, 2004). NOM molecules contain
a range of functional group types, including carboxyl, phenol, amino, and sulfhydryl groups, that
have the potential to create highly stable complexes with metal ions across the pH range
(Ephraim, 1992; Ravichandran et al., 1999; Drexel et al., 2002; Haitzer et al., 2002; Croué et al.,
2003; Ravichandran, 2004). Hg binds strongly to the sulfhydryl groups present within the NOM
structure (Dong et al., 2011; Muresan et al., 2011). The relative thermodynamic stabilities of HgNOM and Hg-bacteria complexes are not well known. Depending on these relative stabilities,
the formation of metal-NOM complexes may decrease adsorption of Hg to bacteria cell
envelopes due to a competitive ligand effect, or under certain conditions may increase
adsorption of Hg to bacteria due to ternary complexation with NOM. For example, investigating
Pb, Cu, and Ni separately, Borrok et al. (2007) found that ternary metal-FA-bacteria complexes
form, and that the importance of the complexes is strongly affected by pH. Conversely,
Wightman and Fein (2001) found that the presence of NOM decreases the amount of Cd
90
adsorbed to bacteria under mid- and high-pH conditions, and that the presence of Cd does not
affect the adsorption of NOM to bacteria, suggesting that ternary complexes do not occur. No
studies have been conducted to date to determine the effects of NOM on Hg binding to
bacteria. However, because Hg forms strong complexes both with cell envelopes (Daughney et
al., 2002; Dunham-Cheatham et al., 2012) and NOM (Loux et al., 1998; Ravichandran, 2004;
Skyllberg et al., 2006), it is likely that significant changes to Hg adsorption behavior occur in the
presence of NOM.
In this study, I used bulk adsorption experiments, conducted as a function of pH and FA
concentration, using intact non-metabolizing bacterial cells to study Hg binding onto three
different bacterial species and to compare the ability of bacteria to adsorb mercury in the
presence and absence of a fulvic acid (FA). I used the experimental results to calculate apparent
stability constants for Hg-bacteria and Hg-FA complexes, allowing for quantitative modeling of
the competitive binding that can occur between bacteria and FA in more complex settings. This
study examined both Gram-positive and Gram-negative bacterial species in order to determine
if cell envelope structure affects the binding reactions, and one species was a Hg methylator,
which I examined in order to determine if the extent or nature of Hg binding onto that species
differed from that exhibited by the non-methylators.
4.3 Methods
4.3.1 Experimental Methods
4.3.1.1 Bacterial Growth and Washing Procedure
Bacillus subtilis (ATCC 23875), a Gram-positive aerobic soil species, and Shewanella
oneidensis MR-1 (ATCC BAA-1096), a Gram-negative facultative anaerobic species, cells were
91
cultured and prepared following the procedures outlined in Borrok et al. (2007). Briefly, cells
were maintained on agar plates consisting of trypticase soy agar with 0.5% yeast extract added.
Cells for all experiments were grown by first inoculating a test-tube containing 3 mL of
trypticase soy broth with 0.5% yeast extract, and incubating it for 24 h at 32 C. The 3 ml
bacterial suspension was then transferred to 1 L of trypticase soy broth with 0.5% yeast extract
for another 24 h on an incubator shaker table at 32 C. Cells were pelleted by centrifugation at
8100g for 5 min, and rinsed 5 times with 0.1 M NaClO4.
Geobacter sulfurreducens (ATCC 51573), a Gram-negative species capable of Hg
methylation, cells were cultured and prepared using a different procedure than detailed above.
Cells were maintained in 50 mL of anaerobic freshwater basal media at 32 oC (Lovely and
Phillips, 1988). Cells for all experiments were grown by first inoculating an anaerobic serum
bottle containing 50 mL of freshwater basal media, and incubating it for 5 days at 32 oC. Cells
were pelleted by centrifugation at 8100g for 5 minutes, and rinsed 5 times with 0.1 M NaClO4
stripped of dissolved oxygen by bubbling a 85%/5%/10% N2/H2/CO2 gas mixture through it for 30
minutes. After washing, each of the three types of bacteria was then pelleted by centrifugation
at 8100g for 60 minutes to remove excess water in order to determine the wet mass so that
suspensions of known bacterial concentration could be created. All bacterial concentrations in
this study are given in terms of gm wet biomass L-1. Bacterial cells were harvested during
stationary phase, and all experiments were performed under non-metabolizing, electron donorfree conditions.
4.3.1.2 Batch Adsorption Experiments
To prepare experiments, aqueous Hg, NOM, and suspended bacteria stock solutions
were mixed in different proportions to achieve the desired final concentrations for each
92
experiment. The experiments were conducted in sets with constant pH (at pH 4.0 ± 0.1, 6.0 ±
0.1, or 8.0 ± 0.3) and constant bacterial concentration (0.2 gm bacteria L-1 in all cases) at three
different FA concentrations (0, 25, or 50 mg L-1), with Hg log molalities ranging from -6.30 to 5.00 (0.1 to 2.0 mg L-1).
FA stock solutions were prepared in Teflon bottles by dissolving dried, powdered
International Humic Substances Society Suwannee River FA Standard I in a 0.1 M NaClO4 buffer
solution to achieve the desired final FA concentration for each experiment. A known mass of
wet biomass was then suspended in the FA stock solution, and the pH of the FA-bacteria parent
solution was immediately adjusted to the experimental pH using 0.2 M HNO3 and/or NaOH. To
prepare experimental solutions, aliquots of the FA-bacteria parent solution were added
gravimetrically to Teflon reaction vessels, followed by a small aliquot of commercially-supplied
1,000 mg L-1 Hg aqueous standard to achieve the desired final Hg concentration. The pH of each
suspension was again adjusted immediately to the experimental pH. The vessels were placed on
an end-over-end rotator to agitate the suspensions for the duration of the experiment (2 h for B.
subtilis and G. sulfurreducens and 3 h for S. oneidensis, as determined by initial kinetics
experiments (results not shown)). The pH of the suspensions was monitored and adjusted every
15 minutes throughout the duration of the experiment, except during the last 30 minutes, when
the suspensions were undisturbed. At the completion of each experiment, the pH of the
suspensions was measured and the experimental suspensions were centrifuged at 8100g for 5
minutes. The aqueous phase was collected for Hg analysis by inductively-coupled plasma optical
emission spectroscopy (ICP-OES). Duplicate experiments were performed for each experimental
condition.
93
4.3.2 Analytical Methods: Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES)
ICP-OES standards were prepared gravimetrically by diluting a commercially-supplied
1,000 mg L-1 Hg aqueous standard with pH-adjusted 0, 25, or 50 mg L-1 FA stock solution made in
0.1 M NaClO4 so that the pH, ionic strength, and FA concentration of the standards closely
matched that of the samples. I found significant interference when standards and samples were
not closely matched in this way. The log molality of the Hg standards ranged from -6.60 to -5.00.
The standards and samples were all stored in Teflon containers and analyzed with a Perkin
Elmer 2000DV ICP-OES at wavelength 253.652 nm within 1 day of collection. The set of
standards was analyzed before and after all of the samples were analyzed, as well as after every
15 samples, to check for machine drift. Analytical uncertainty, as determined by repeat analyses
of the standards, was ± 2.8% for the 0 mg L-1 FA samples, ± 7.7% for the 25 mg L-1 FA samples,
and ± 9.5% for the 50 mg L-1 FA samples. Neither standards nor samples were acidified prior to
analysis. Fulvic acid concentration strongly affected system performance and signal strength,
likely due to spectral interferences caused by the FA molecule. For each pH and FA
concentration condition studied, I conducted biomass-free control experiments to determine
the extent of Hg loss due to adsorption onto the experimental apparatus as well as any
interferences caused by the presence of FA during the ICP-OES analysis.
4.3.3 Thermodynamic Modeling
Surface-complexation models were constructed to model Hg binding with bacterial cell
envelope functional groups and with those on the FA molecules, and to quantify the
competition between the two. Observed adsorption reactions between aqueous Hg species and
deprotonated bacterial cell envelope sites and/or FA binding sites were modeled according to
the following generic reaction:
94
(10)Hg speciesx+ + R-Ai-  (R-Ai)(Hg species)(x-1)+
where ‘Hg speciesx+’ represents the specific aqueous Hg species considered, ‘R-Ai-’ represents
the deprotonated cell or FA binding site, ‘(R-Ai)(Hg species)’ represents the Hg-bacterial cell
envelope or Hg-FA complex, and the ‘x’ represents the charge of the aqueous Hg species.
Stability constants for each of the Hg-bacterial cell envelope and Hg-FA complexes are expressed
as the corresponding mass action equation for Reaction (10):
(11) Kads 
[( R  Ai )( Hg species ) ( x 1) ]

a ( Hg species x  ) [ R  Ai ]
where Kads is the thermodynamic equilibrium constant for Reaction (10), the square brackets
represent concentrations in mol L-1, and a represents the activity of the species in parentheses.
I used FITEQL 2.0 (Westall, 1982) for the equilibrium thermodynamic modeling of the
adsorption data, using the aqueous speciation equilibria and equilibrium constants given in
Table 12, and using the Davies equation within FITEQL to calculate activity coefficients. Because
all of my experiments were conducted at the same ionic strength, I applied a non-electrostatic
model to account for the Hg adsorption data. Bacterial site concentrations and acidity constants
used in the calculations for B. subtilis, for S. oneidensis, and for G. sulfurreducens are from Fein
et al. (2005), Mishra et al. (2010), and Dunham-Cheatham et al. (2012), respectively. The
objective of the modeling exercise was not to construct precise site-specific mechanistic binding
models, but rather to provide a quantitative means of estimating the competitive binding of
bacteria and FA under a range of relative concentration conditions. Toward this end, because
specific binding constants for Hg with each site type on the FA molecule are not known, I
modeled Hg binding with the FA as a single complexation reaction between Hg 2+ and the
deprotonated form of a generic FA site. I assumed that this generic binding site exhibits an
95
acidity constant equal to the average of the acidity constants of all of the FA sites, with a site
concentration equal to the total concentration of all FA sites, using the average values from
Borrok and Fein (2004) as a model of the FA site speciation. The calculated acidity constant and
site concentration for this generic site are listed in Table 12.
4.4 Results
Consistent with previous studies of Hg adsorption onto bacteria (Daughney et al., 2002;
Dunham-Cheatham et al., 2012), I observed extensive adsorption of Hg onto the bacterial
species studied in the absence of FA, with the extent of adsorption relatively independent of pH
between pH 4 and 8 (Figure 27, top plots). For example, approximately 77% of the Hg in a 2 mg
L-1 Hg solution adsorbs at pH 4 onto 0.2 gm L-1 S. oneidensis, while approximately 75% adsorbs at
pH 8. The presence of FA decreases the amount of Hg adsorbing to cell envelopes of each of the
bacterial species and at each of the pH conditions studied (Figure 27, middle and bottom plots).
With 50 mg L-1 FA, the extent of adsorption at pH 4 decreases to 65%, and at pH 8 to 50%. My
experimental results also indicate that the three bacterial species studied here exhibit similar
extents of Hg adsorption under each experimental condition, consistent with the observations
from a number of previous studies (e.g. Cox et al., 1999; Yee and Fein, 2001; Borrok et al., 2005;
Johnson et al., 2007). The data suggest that as the concentration of FA increases, so does the
amount of Hg remaining in solution. These results indicate that FA competes with the bacterial
cells for the adsorption of Hg, and that the adsorption of Hg to FA results in a competitive ligand
effect. As a result, less Hg is available for adsorption to proton-active functional groups on the
bacterial cell envelope, and less Hg is removed from solution. These results are not surprising, as
FA molecules contain sulfhydryl groups within their structure and sulfhydryl groups bind
96
TABLE 12
HG REACTIONS USED IN THE SPECIATION MODELING
Reaction
+
Log K
-
H2O – H = OH
H2CO30 – H+ = HCO3H2CO30 – 2H+ = CO32H2CO30 – H2O = CO20
+
Na + H2CO30 – 2H+ = NaCO3Na+ + H2CO30 – H+ = NaHCO30
Na+ + H2O – H+ = NaOH0
Hg2+ + H2O - H+ = HgOH+
Hg2+ + 2H2O - 2H+ = Hg(OH)20
Hg2+ + 3H2O - 3H+ = Hg(OH)32Hg2+ + H2O - H+ = Hg2(OH)3+
3Hg2+ + 3H2O - 3H+ = Hg3(OH)33+
Hg2+ + H2CO30 – 2H+ = HgCO30
Hg2+ + H2CO30 – H+ = HgHCO3+
2+
Hg + H2CO30 + H2O – 3H+ = Hg(OH)CO3B1- + H+ = B1-H0
Bacillus subtilis
Shewanella oneidensis
Geobacter sulfurreducens
B2- + H+ = B2-H0
Bacillus subtilis
Shewanella oneidensis
Geobacter sulfurreducens
B3- + H+ = B3-H0
Bacillus subtilis
Shewanella oneidensis
Geobacter sulfurreducens
FA- + H+ = FA-H0
-14.00 b
-6.355 a
-16.67 a
2.770 b
-15.41 b
-6.60 b
-14.2 b
-3.40 a
-5.98 a
-21.1 a
-3.30 b
-6.40 b
-3.91 a
0.42 a
-11.355 a
3.30 c
3.30 d
3.36 e
4.80 c
4.80 d
4.81 e
6.80 c
6.70 d
6.49 e
5.85 f
(a) Powell et al., 2005.
(b) Martell and Smith, 2001.
(c) Fein et al., 2005.
(d) Mishra et al., 2010.
(e) Dunham-Cheatham et al., 2012.
(f) Calculated as the average of all reported pKa values in Table 2 from Borrok and
Fein (2004). Assumed total site concentration is the sum of the average site
concentrations for the individual FA sites: 5.50 x 10-3 moles of sites per gram of
humic substance.
97
Figure 27: Aqueous chemistry results for Hg isotherms in the absence and presence of FA at pH
4 (A, B, C), pH 6 (D, E, F), and pH 8 (G, H, I). Plots A, D, and G present the results for the FA-free
controls, plots B, E, and H present the results for the 25 mg L-1 FA experiments, and plots C, F,
and I present the results of the 50 mg L-1 FA experiments. B. subtilis is represented by the blackoutlined, grey-filled squares, S. oneidensis is represented by the solid black diamonds, and G.
sulfurreducens is represented by the hollow circles. The black line on each plot represents 100%
Hg adsorption under each experimental condition.
98
strongly with Hg (Xia et al., 1999; Hesterberg et al., 2001; Drexel et al., 2002; Haitzer et al., 2002;
2003), leading to effective competition with bacterial cell envelopes which also contain protonactive sulfhydryl functional groups (Guiné et al., 2006; Mishra et al., 2009; 2010). In the
experimental systems, FA binding sites outnumber those present on the bacteria. For example,
50 mg L-1 FA corresponds to approximately 2.8 x 10-4 moles of sites L-1 (Borrok and Fein, 2004),
while 0.2 gm L-1 B. subtilis biomass contains 4.7 x 10-5 total moles of sites L-1. At pH 8, 50 mg L-1
FA does diminish the extent of Hg adsorption, but only from approximately 70% (with no FA
present) to 60%. It appears that given equal site concentrations, bacterial binding of Hg would
dominate the competition with FA.
4.5 Discussion
The experimental results presented here suggest that bacterial cell envelope functional
groups and FA functional groups exhibit reasonably similar binding affinities for Hg under the
experimental conditions. Hg binding onto the bacterial cell envelopes is extensive, and although
Hg binds strongly with FA, especially with the sulfhydryl groups present within FA (Xia et al.,
1999; Hesterberg et al., 2001; Drexel et al., 2002; Haitzer et al., 2002; 2003), the presence of
even up to 50 ppm FA with only 0.2 gm (wet mass) L-1 of bacteria does not cause the speciation
of Hg to be dominated by the FA. The results strongly suggest that there is a fairly equal
competition between the bacterial and FA binding sites for the available Hg.
In order to quantify the competitive binding, I used a semi-empirical surface
complexation approach. First, I used the FA-free adsorption data at pH 4, 6, and 8 to solve for
equilibrium constants for the following Hg2+ adsorption reactions, respectively:
(12)R-A1- + Hg2+  R-A1-Hg+
99
(13)R-A2- + Hg2+  R-A2-Hg+
(14)R-A3- + Hg2+  R-A3-Hg+
where R-A1, R-A2, and R-A3 represent the bacterial functional groups with the three lowest pKa
values, respectively. At pH 4, the R-A1 sites are the dominant deprotonated sites available for
Hg2+ binding for each bacterial species; at pH 6, both R-A1 and R-A2 sites are deprotonated; and
at pH 8, R-A1, R-A2, and R-A3 sites likely contribute to the binding of Hg2+. Therefore, I used the
pH 4 data to constrain the stability constant value for Reaction (12) alone, then fixed that value
and used the pH 6 data to solve for the stability constant value for Reaction (13) with a model
that involved Reactions (12) and (13) simultaneously. I then used the values that I calculated for
the stability constants for Reactions (12) and (13) and the pH 8 data to solve for the best-fitting
value for Reaction (14) with a model that involved Reactions (12) - (14) simultaneously. This
modeling approach assumes that Hg2+ binding at a given pH occurs dominantly onto sites with
pKa values lower than the pH of the experiments; that is, dominantly onto deprotonated sites.
However, the resulting stability constant values, which are tabulated in Table 13, yield excellent
fits to the FA-free Hg adsorption data as a function of pH and Hg loading (e.g., Figure 28). The
calculated stability constants for each reaction for each bacterial species studied here are similar
to each other. The log stability constant values for Reaction (3) range from 7.3 for B. subtilis to
7.8 for G. sulfurreducens; those for Reaction (13) range from 11.2 for S. oneidensis to 11.6 for
both B. subtilis and G. sulfurreducens; and those for Reaction (14) range from 15.6 for S.
oneidensis to 16.5 for G. sulfurreducens. The fact that the stability constant values increase by
four-to-five orders of magnitude from one site to the next likely is due to the simplified nature
of the adsorption model. I assumed that Hg2+ is the adsorbing aqueous Hg species under all pH
conditions. However, Hg(OH)2 is the dominant aqueous Hg species under the experimental
100
TABLE 13
CALCULATED LOG STABILITY CONSTANT VALUES FOR REACTIONS (12) – (15)
[FA]
(mg L-1)
pH
0
4
25, 50
6
8
Bacteria
Reaction
(12)a
Reaction
(13)b
Reaction
(14)c
B. subtilis
S. oneidensis
G. sulfurreducens
B. subtilis
S. oneidensis
G. sulfurreducens
B. subtilis
S. oneidensis
G. sulfurreducens
B. subtilis
S. oneidensis
G. sulfurreducens
7.3 ± 0.1
7.6 ± 0.2
7.8 ± 0.2
11.6 ± 0.2
11.2 ± 0.1
11.6 ± 0.1
16.4 ± 0.1
15.6 ± 0.1
16.5 ± 0.1
Average value:
Reaction
(15)d
25 mg L-1
Reaction
(15)d
50 mg L-1
13.4 ± 0.2
13.8 ± 0.2
13.8 ± 0.1
14.3 ± 0.1
14.4 ± 0.2
14.4 ± 0.2
14.9 ± 0.2
14.9 ± 0.2
14.6 ± 0.3
14.3 ± 0.2
13.4 ± 0.1
13.6 ± 0.3
13.6 ± 0.1
14.0 ± 0.1
14.2 ± 0.3
14.2 ± 0.1
14.4 ± 0.2
15.0 ± 0.4
14.6 ± 0.2
14.1 ± 0.2
(a) R-A1- + Hg2+  R-A1-Hg+
(b) R-A2- + Hg2+  R-A2-Hg+
(c) R-A3- + Hg2+  R-A3-Hg+
(d) FA- + Hg2+  FA-Hg+. Both columns present the calculated log stability constant values for
the adsorption of Hg to deprotonated FA, as expressed in Reaction (15). The left column
presents the values for the 25 mg L-1 FA conditions, and the right column presents the
values for the 50 mg L-1 FA conditions.
101
Figure 28: Representative model fits for S. oneidensis at pH 6
under 0 mg L-1 FA (grey squares and grey curve) and 50 mg L-1
FA (solid black diamonds and black curve) conditions. The dotted
line represents 100% Hg adsorption under each experimental
condition.
conditions, and the concentration of Hg2+ is small and becomes smaller with increasing pH over
the pH range of my experiments. Therefore, because the extent of adsorption is relatively pH
independent, the stability constants that describe adsorption of Hg2+ onto bacterial binding sites
must become larger with each site considered.
Site-specific Hg binding constants have not been determined for Suwanee River FA, so I
could not compare the measured effects of the presence of FA with those I would predict from
speciation calculations. However, I used the measured extents of Hg adsorption in the presence
102
of FA to calculate empirical generic site Hg binding constants for the FA. That is, I modeled the
Hg binding onto the FA with the following single site reaction:
(15)FA- + Hg2+  FA-Hg+
where FA- represents the generic deprotonated site on the FA molecule. I modeled this site as a
hybrid of the 4 sites used by Borrok and Fein (2004) to account for FA protonation behavior,
with the pKa value of the hybrid FA site equal to the average of the pKa values used by Borrok
and Fein (2004) and the site concentration equal to the average of the total of the 4 sites for all
9 FAs modeled by Borrok and Fein (2004). Clearly, modeling Hg 2+ adsorption onto this hybrid
generic FA binding site is a simplification of the complex binding environment of Hg on the FA
molecule, but it allows us to quantify the competition between the FA and the bacterial cell
envelope, and to calculate quantitative estimates of the effects of each binding environment in
more complex settings.
The calculated stability constants, tabulated in Table 13, yield an excellent fit to the
observed effects of the presence of FA on Hg adsorption onto the bacteria studied here (e.g.,
Figure 28). The stability constants calculated for the three bacterial species are similar to each
other and do not vary systematically between bacterial species. Additionally, the 25 mg L -1 FA
data yield calculated Hg-FA stability constant values that are not significantly different from
those calculated using the 50 mg L-1 FA data. The calculated stability constant values do change
systematically with pH, with values increasing with increasing pH. This trend is likely a result of
the oversimplification of the Hg-FA binding model; it is probable that the FA molecule contains
multiple functional group types that deprotonate sequentially with increasing pH, not just the
one site type that I assumed in the models. However, the calculated log stability constant values
are not strongly dependent upon pH, with the largest spread being from 13.4 to 14.9 for the pH
103
4 to 8, 25 mg L-1 FA data for B. subtilis. Thus, the values in Table 13 can be used to yield
reasonable estimates of the competition between bacteria and FA in the pH and FA:bacteria
concentration ratio conditions studied here.
The calculated K values can be used to illustrate the direct competition between
bacteria and FA for available aqueous Hg2+. For example, the competition reaction between
bacterial site A2 and the FA binding site can be expressed as:
(16)R-A2-Hg+ + FA-  FA-Hg+ + R-A2where the log equilibrium constant for Reaction (16) can be calculated as the log K value for
Reaction (15) minus the log K value for Reaction (13), or values of 2.4 for B. subtilis, 3.0 for S.
oneidensis, and 2.6 for G. sulfurreducens under pH 6 conditions with 50 mg L-1 , 0.2 gm L-1
bacteria. These calculated equilibrium constant values for Reaction (16) can be used to quantify
the distribution of Hg between bacterial and fulvic acid binding sites for conditions with
different relative concentrations of each site type, and the large positive values suggest that on
a mass normalized basis, bacterial binding of Hg is greater than that exhibited by fulvic acid.
Although both bacteria and fulvic acids contain sulfhydryl binding sites that are especially
effective at binding Hg, the results suggest that these sites may exhibit a higher density on
bacteria than they do on fulvic acid.
4.6 Conclusions
The results from this study show that the presence of FA decreases the extent of Hg
adsorption onto three different bacterial species through competitive binding of the Hg. I used
the experimental results to calibrate a quantitative semi-empirical model of the binding of Hg to
bacteria and FA, and the stability constants that I calculated can be used to estimate the
104
distribution and speciation of Hg in bacteria- and FA-bearing geologic systems. Because
accessibility of Hg to bacteria for metabolic processes such as methylation may be controlled by
adsorption, the stability constants calculated in this study may also be useful in estimating the
bioavailability of Hg in soil and groundwater systems that contain significant concentrations of
fulvic acid.
4.7 Acknowledgements
The experiments and analyses were performed at the Center for Environmental Science
& Technology, University of Notre Dame.
105
CHAPTER 5:
CONCLUSIONS
Geochemists are faced with the challenge of quantifying the mobility and bioavailability
of contaminants in the subsurface. Due to the innate complexity of subsurface environments,
using simplified models to predict the mobility of these contaminants is impractical. Therefore,
it is beneficial to understand how each component of natural systems may affect the
contaminant of interest. Each project included in this research aimed to fill in holes in our
understanding of contaminant migration in subsurface environments by providing more
accurate and flexible geochemical models through the incorporation of parameters based on
experimental measurements.
In Chapter 2, I investigated the potential for passive cell wall biomineralization in the
presence of metals (e.g. uranium, lead and calcium), phosphate and non-metabolizing bacterial
cells. Prior to this study, research was presented that suggested the potential for passive cell
wall biomineralization, but the results from the research was equivocal. The previous research
could not prove that the association between the bacterial cells and the precipitates was not
merely a result of electrostatic interactions drawing the particle and the cell together, or a result
of metabolic processes of the bacteria. To determine if passive cell wall biomineralization can
occur, I used non-metabolizing bacterial cells to minimize the potential for interactions between
the metal and metabolic exudates, and conducted precipitation experiments under a range of
saturation state conditions. The results demonstrate that passive cell wall biomineralization
106
occurs under specific environmental conditions, and that the presence of bacteria may have a
significant effect on the size of metal phosphate precipitates. This effect results in a relatively
higher aqueous metal concentration compared to an abiotic system, the result of which is an
increased availability of the heavy metal.
In Chapter 3, I explored the adsorption behavior of Hg to a variety of bacterial cell types
in both the absence and presence of a competitive ligand, Cl. I conducted batch adsorption
experiments, measuring Hg adsorption onto 3 bacterial cell species in both the presence and
absence of Cl. The results show that Hg extensively binds to bacterial cell envelopes in both the
absence and presence of Cl, but does not exhibit typical metal cation adsorption behavior. The
stability constants calculated using the experimental data will yield more accurate predictions of
Hg binding behavior than the previously reported stability constants, as discussed in Chapter 3.
The results from Chapter 3 showed extensive and strong Hg binding to bacterial cells
despite the addition of a competitive ligand, which raises the question whether natural organic
matter (NOM) can compete with bacterial cell envelopes for the adsorption of Hg, since they are
both composed of proton-active functional groups that readily bind metals. In Chapter 4, I
conducted batch adsorption experiments adsorbing Hg onto bacterial cell walls in the absence
and presence of fulvic acid (FA). Fulvic acid-metal interactions have been widely studied;
however, no study to date has investigated the interactions between bacterial cells, Hg and FA,
and used the data to calculate stability constants for the Hg-bacteria and Hg-FA reaction.
Previous studies have focused on one ligand only and did not consider a 3-component system.
My results show that the extent of Hg binding to bacterial cell envelopes is decreased in the
presence of FA, and that FA does not form ternary complexes with bacteria and Hg, but instead
behaves as a competitive ligand for Hg binding. The results from this study can be used to
predict the distribution and speciation of Hg in FA- and bacteria-bearing systems. The
107
quantification of thermodynamic stabilities of the important mercury species in Chapters 3 and
4 are crucial to understanding the transport of the contaminant and in creating bioavailability
models to predict the fate of the metal.
The discoveries from each of the 3 studies lead to additional questions. For example, an
extension of the study in Chapter 2 would be to examine whether passive cell wall
biomineralization leads to the formation of other mineral types than phosphate minerals. The
phosphate minerals observed in my study likely occurred because of the phosphate groups
within the cell envelope; however, do other groups influence mineralization as well? Is the
mineralization specific to the group or can any of the groups present on the cell envelope
nucleate mineralization? Additionally, future studies could probe whether passive cell wall
mineralization occurs for a range of metals at the same saturation state or whether the
phenomenon is saturation state independent, and investigate a range of metals to determine if
passive cell wall biomineralization increases the aqueous metal concentrations relative to the
abiotic controls for all metals. To expand upon the studies in Chapters 3 and 4, one could
investigate the potential for reversibility of Hg adsorption to bacterial cell envelopes, and
determine if a competitive ligand has the potential to remove bound Hg from cell envelopes to
form aqueous complexes. Because Hg binds so readily and strongly with the sulfhydryl sites on
cell envelopes, it is possible that Hg will not exhibit reversible adsorption typical of most metals.
Additionally, research could be conducted to determine what types of sites, in addition to
sulfhydryl, dominate Hg binding to organics and if the type of binding site affects Hg binding
behavior, speciation and distribution throughout the experimental system.
Various approaches can be taken in order to answer these questions. Conducting
precipitation experiments similar to those outlined in Chapter 2 using a wider range of metals
and saturation state conditions would determine if passive cell wall biomineralization is
108
saturation state- or metal-specific. Once these questions are answered, a more in-depth
investigation of the function groups involved in the phenomenon could be conducted, using
analytical methods such as X-ray absorption spectroscopy to probe the binding environment of
the precipitates with the cells, to determine whether functional groups other than phosphates
participate in passive cell wall biomineralization. To answer the question regarding the
reversibility of Hg binding to cell envelopes, one could determine the difference in aqueous Hg
concentrations in a system with bacterially-bound Hg before and after the addition of a ligand
with a high binding affinity for Hg (e.g. sulfide or a reduced-sulfur containing complex, such as
NOM). If the aqueous Hg concentration increases after the addition of the ligand, it is likely that
the binding of Hg to bacterial cell envelopes is reversible. Using a variety of ligands with a range
of binding affinities for Hg would determine how easily reversible Hg binding to bacterial cell
envelopes is, the result of which could have major implications for the mobility of the metal in
geologic systems.
One challenge of understanding metal mobility in the environment is the complexity of
each geologic system. In order to predict the speciation, distribution, and mobility of each
element within a system, we must first know how each element reacts with every other element
present and be able to determine whether the element will adsorb to a surface to become
immobile, form a mobile aqueous complex, or precipitate from solution. If the element is
precipitated from solution, it may still be mobile in the solid phase or it may return to the
aqueous phase upon dissolution of the precipitate. In an effort to further our knowledge, the
studies contained within this dissertation were aimed to provide data and thermodynamic
models that will help us better predict the behavior of metals in the environment.
109
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