UNIVERSITY OF CALGARY
High Precision Measurements of 32S, 33S, and 34S Isotopic Composition by Multiple Collector
Inductively Coupled Plasma Mass Spectrometry.
by
Yahya Ahmad Alfayfi
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE
DEPARTMENT OF PHYSICS AND ASTRONOMY
CALGARY, ALBERTA
MAY, 2014
© Yahya Ahmad Alfayfi 2014
Abstract
A reliable method was developed for sulfur isotopic composition measurements of sulfur
solutions (10 ppm) using a 20 mL cyclonic quartz spray chamber and 50 μL/min (Glass
Expansion Inc.) quartz nebulizer coupled to a high mass resolution Multiple Collector
Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS). The repeatability of the method
was tested which three reference materials (IAEA S-1, IAEA S-2, and IAEA S-3) and the ability
to measure sulfur isotopic variations in hair sample was evaluated. The n(34S)/n(32S) and
n(33S)/n(32S) isotope amount ratios were measured free from isobaric interferences (e.g. 16O 2 +
on 32S+ and 32S1H+ on 33S+) and corrected for instrumental mass bias adopting the standardsample bracketing approach. The reproducibility of IAEA S-2 and IAEA S-3 is typically ± 0.19
‰ and ±0.52 (2sd) for δ34S and δ33S, respectively and ± 0.30 ‰ and ± 0.53 ‰ (2sd) for δ34S and
δ33S, respectively. Freedom from isobaric interferences was verified by plotting δ34S vs. δ33S.
Cleanliness of the spray chamber was found to be extremely important to achieve reliable data. A
microwave digestion method was developed to analyze sulfur in hair samples. The
reproducibility of hair samples was typically ± 0.45 ‰ and ± 0.75 ‰ (2sd) for δ34S and δ33S
values, respectively. Achieving a reliable method for δ34S and δ33S measurements will enable
now a high precision measurement of sulfur isotopic composition.
ii
Acknowledgements
I would like to express my sincere gratitude to my advisor, Prof. Dr. Michael Wiser, for
his supervision, help and valuable suggestions since the beginning of this study. He has always
supported and encouraged me and helped crystallize my ideas to successfully complete my
research.
I would like to express my very great appreciation to my thesis committee and for their
valuable comments and suggestions: Dr. Christopher Cully and Dr. Nasser Moazzen-Ahmadi.
I would like to thank the Department of Physics at Jazan University for their funding and
for supporting this study.
My grateful thanks are also extended to Dr. M. Musiani and Ms. C. Dubesky for the hair
samples. I am particularly grateful for the assistance given by Kerri Miller, Breege McKiernan,
and Nenita Lozano for their assistance with the sample preparation and the fieldwork.
iii
Table of Contents
Abstract .................................................................................................................................................... ii
Acknowledgements ............................................................................................................................. iii
Table of Contents.................................................................................................................................. iv
List of Tables .......................................................................................................................................... vi
List of Figures and Illustrations ..................................................................................................... vii
List of Symbols and Abbreviations ................................................................................................... x
1
2
Introduction ................................................................................................................................................ 1
1.1
The objective ....................................................................................................................................................... 3
2.1
Sulfur Isotope Variations in Nature ........................................................................................................... 6
Sulfur Isotopic Fractionation ................................................................................................................ 6
2.2
Sulfur Isotope Fractionation Process ...................................................................................................... 14
2.2.1
Mass dependent Isotopic Fractionation ............................................................................................................14
2.2.1.1
Equilibrium Isotope Fractionation .............................................................................................................14
2.2.1.3
Mass Dependent Fractionation Laws ........................................................................................................17
2.2.1.2
2.2.1.4
3
Kinetic Isotope Fractionation .......................................................................................................................16
Three isotope plot..............................................................................................................................................19
2.2.2
Mass Independent Isotopic Fractionation ........................................................................................................22
3.1.1
Sample Introduction...................................................................................................................................................25
3.1.3
Electrostatic and Magnetic Sectors ......................................................................................................................30
Mass Spectrometery of Sulfur Isotopes .......................................................................................... 24
3.1.2
3.1.4
3.2
3.3
ICP Source .......................................................................................................................................................................27
Multiple Collectors ......................................................................................................................................................32
Interferences on Sulfur Signal Intensities ............................................................................................. 34
Mass Bias on MC-ICP-MS .............................................................................................................................. 38
iv
4
The Analytical Method used to Meaure Sulfur Isotope Amount Ratios .............................. 41
4.1
Sample Preparation ........................................................................................................................................ 41
4.1.1
4.1.2
Preparation of Hair Samples ....................................................................................................................... 44
4.4
Measurement Conditions on the MC-ICP-MC ...................................................................................... 50
Preparation of Sulfur Standards................................................................................................................ 48
Results of δ34S and δ33S values Measurements by MC-ICP-MS................................................ 62
5.1
Mass Bias Evaluation on Sulfur Measurement .................................................................................... 62
5.3
Results and Discussion of the Hair Samples......................................................................................... 71
5.2
6
Isolation of Sulfur by Ion exchange .....................................................................................................................43
4.2
4.3
5
Parr Bomb vs. Microwave Digestion of Hair Samples .................................................................................42
Results and Discussion of the Standard Samples ............................................................................... 64
Conclusions ............................................................................................................................................... 77
References ........................................................................................................................................................ 80
Appendix ........................................................................................................................................................... 86
v
List of Tables
Table 3.1: isobaric interferences on sulfur isotope masses (Craddock et al., 2008). .................... 35
Table 4.1: The operating parameters for the Neptune (MC-ICP-MS) used for the measurement of
sulfur isotope ratios in IAEA reference materials and hair samples. ............................................ 49
Table 4.2: Sulfur isotope compositions in different mass positions on sulfur masses. ................ 58
Table 5.1: Sulfur isotope ratios determined by MC-ICP-MS analysis of IAEA S-1 and IAEA S-1
in 3% HNO 3 solution expressed in terms of δ34S and δ33S values. The average delta values are
reported within internal precision (1sd) for individual measurement. Reproducibility of sulfur
isotopes in IAEA S-1 and IAEA S-2 reference materials measured against IAEA S-1 during
individual session is shown within 2sd. The Δ33S values expressed in term of linear δ34S and δ33S
values (Δ33S= δ33S-0.515*δ34S) for the investigation of mass dependent fractionation processes.
....................................................................................................................................................... 66
Table 5.2: The δ34S and δ33S values of IAEA-S-1, IAEA-S-2, and IAEA-S-3 of accepted and the
recent published values. Delta values are reported in per mil (‰) and the uncertainties given in 2
sd. .................................................................................................................................................. 69
Table 5.3: The δ34S and δ34S values of the three sessions for the collected hair samples. The first
10 samples are from Two lakes located in Sunwapta area, the 20 samples after are from three
areas all located in Jasper, and two samples from Litle Smoky area. ........................................... 72
Table 5.4: Summary of three replicates hair samples collected from three Redrock Prairie Creek,
INP, and LSM. The average δ34S and δ33S values is reported in (‰), and the uncertainty in 2sd.
....................................................................................................................................................... 74
vi
List of Figures and Illustrations
Figure 2.1: Sulfur isotope distribution in nature (Wieser1 & Coplen, 2010). ................................ 8
Figure 2.2: A hypothetical three-isotope plot for sulfur. The mass dependent fractionation;
equilibrium and kinetic fractionation lines are shown. The sold line shows the equilibrium
fractionation trend with slope of 0.515 and the dashed line shows the kinetic fractionation trend
of sulfur with slope of 0.508. Thus, any deviation from these trends indicates mass independent
isotope fractionation...................................................................................................................... 21
Figure 3.2: On the left side of the figure is a Scott double-pass spray chamber (stable
introduction system) and on the right is a cyclonic spray chamber that was used in this study. .. 26
Figure 3.3: The ICP torch of MC-ICP-MS (Albarede & Beard, 2004). ....................................... 27
Figure 3.4: The interface region of an ICP-MS (Košler & Sylvester, 2003). ............................... 29
Figure 3.6: Peak profile when the adjacent ion beam profiles of ion species A and B with a mass
difference of ∆m are scanned across the detector slit. The beams are separated by the use of a
high-resolution entrance slit (narrow slit width). The detector slit width spans the mass range
from -15 to 15 on the horizontal scale. First the lighter ion species A enter the detector slit to
form the first plateau section of the peak while the heavier ions species B is clipped at the high
mass side of the detector slit. The second plateau is created when both ion beams (A + B) enter
the detector simultaneously. Then, ion beam A is clipped on the low mass side of the detector
slit and species B forms the third plateau section (Weyer & Schwieters, 2003). ......................... 36
Figure 4.2: The map was supplied from Dr. Marco Musiani's Landscape Ecology Research
Group at the University of Calgary. The map indicates where hair samples were collected. Each
yellow region represents the caribou ranges designated by the Province of Alberta. Red points
indicate specific areas from which samples were collected.......................................................... 46
vii
Figure 4.4: Mass scan using high-resolution mode with aspirating 10 ppm IAEA S-1 in 3%
HNO 3 (a) and 3% HNO 3 only (b) introduced into MC-ICP-MS using cyclonic spray chamber.
Three Faraday cup collectors L3, C, and H3 are arranged to measure 32S, 33S, and 34S,
respectively. All three sulfur isotopes are measured where they are free from spectral
interferences, i.e. at a magnetic field setting of 32.912 u relative to the axial detector 33S. The
plot (a) shows the signal of the measured sample free from interferences on the low mass
shoulder of the peak. The plot (b) shows clearly that no interferences are present where the signal
intensities of the sulfur isotopes 32S, 33S, and 34S are measured (i.e. where the dashed line is
positioned)..................................................................................................................................... 52
Figure 4.5: Peak shape for sulfur isotopes species. Green line is 32S, the red line is 33S, and the
blue line is 34S for 10 ppm S solution. Beams are collected simultaneously on three Faraday
cups: L3 (32S), C (33S), and H3 (34S). Significant interferences occur on all sulfur masses but the
plateau on the lower mass indicates resolved interferences where sulfur isotopes could be
detected. ........................................................................................................................................ 53
Figure 4.8: The signal intensities of sulfur isotopes were selected free from isobaric interferences
on the low mass shoulder of the peak on L3 for 32S signal, C for 33S signal, and H3 for 34S signal.
All collected signals of sulfur isotopes were within very small area of the free-interference
plateau, which is 0.005u wide. ...................................................................................................... 59
Figure 5.1: Section (a) shows the isotope amount ratios of measurements session (~ 10 hours) for
IAEA S-1, S-2, and S-3 standards. Section (b) shows the small window of the mass drift for
IAEA S-1 replicate measurements and the isotope amount ratios were between 0.04902 and
0.04897 through the session. There was no significant of mass drift through time but it is clear
viii
that the average measured values of the sulfur isotope amount ratio offset from the true value
(see section 3.3). ........................................................................................................................... 63
Figure 5.2: Three-isotope plots of δ33S versus δ34S for IAEA S-1 and IAEA S-3 during an
analytical section. The data are in good agreement with mass dependent fractionation process
because the slope of this plot is 0.515 that refers to mass fractionation laws (see section 2.2.1).
In addition, the data confirmed that there was no evidence of the spectral interferences on the
results. ........................................................................................................................................... 67
Figure 5.3: Reproducibility of sulfur isotopes for the reference materials S-2 and S-3 measured
against S-1 over different analytical sessions. The external reproducibility of S-2 and S-3 was
within ± 0.30 ‰ (2sd) for δ34S values and ± 0.53 (2sd) for δ33S values. For δ33S, the
reproducibility is slightly larger than the reproducibility on δ34S values because of lower 33S
signal intensities but still within expected range (Table 5.3)........................................................ 68
Figure 5.4: Three-isotope plot for sulfur measurements. Each point is the average of three
separate measurements. A linear relationship was observed between δ33S and δ34S values. The
slope of the line is 0.51 ± 0.01, which indicated that the sulfur isotopes were affected by mass
dependent fractionation. The slope (0.51±0.01) is the exponent β, which refers to the mass
dependent fractionation. ................................................................................................................ 73
ix
List of Symbols and Abbreviations
Symbol
GS-IRMS: Gas Source Isotope Ratio Mass Spectrometer
MC-ICP-MS: Multiple Collector Inductively Coupled Plasma Mass Spectrometry
SSB: Sample standard bracketing
M: Molar
EIF: Equilibrium Isotope Fractionation
KIF: Kinetic Isotope Fractionation
MDF: Mass dependent fractionation
MIF: Mass independent fractionation
MFL: Mass fractionation line
VCDT: Vienna Cañon Diablo Troilite
IAEA: The International Atomic Energy Agency
ESA: The electrostatic analyzer
Abbreviations
δ = Delta Value
β = Exponent for isotopic fractionation in equilibrium or kinetic fractionation
α = The fractionation factor
K= The equilibrium constant
f = Instrumental fractionation factor
Sd = Standard Deviation
u = Unified mass unit
V = Voltage
R= Resolving power
x
1 Introduction
Isotopes are atoms that contain the same number of protons, and therefore electrons, but
differ in their numbers of neutrons. Sulfur has four stable isotopes, 32S, 33S, 34S, and 36S with the
following abundances: 0.950 3957(90), 0.007 4865(12), 0.041 9719(87), 0.000 1459(21),
respectively (Coplen et al., 2002). Each isotope has 16 protons in the nucleus, but either 16, 17,
18 or 20 neutrons. Sulfur is present in many natural and anthropogenic compounds. Its reactivity
makes it an important element in a variety of natural biogeochemical processes, as well as a key
component of environmental and atmospheric contaminants. The abundances of sulfur stable
isotopes vary because of atmospheric, geologic, biological, and hydrologic processes. Thus,
sulfur compounds have distinct isotopic compositions depending on their source. As a result,
sulfur isotopic compositions have been used as a tracer in many environmental and ecosystem
studies to identify pollution sources and establish the link between the food sources for animals
and humans (Katzenberg & Krouse, 1989). The variations of sulfur isotopes abundances are very
small and their measurement requires a method that is capable of generating high precision data.
The isotopic composition of a light element such as sulfur is typically measured on gas
source isotope ratio mass spectrometers (GS-IRMS). Recently, multiple collector inductively
coupled plasma mass spectrometry (MC-ICP-MS) has become the method of choice for many
environmental applications using isotope ratio measurements (Horstwood & Nowell, 2005;
Vanhaecke & Kyser, 2012) because it combines the advantages of high ionization efficiency
from an ICP source and the precise measurements of the isotopic ions by a series of collectors
measuring the ion beams simultaneously. This thesis is concerned with the use of the MC-ICPMS method to measure sulfur isotope amount ratios. Sulfur isotope amount ratios are commonly
measured and expressed as the relative difference in the ratio of the less abundant isotope to the
1
number of the more abundant isotope in a sample with respect to the measurement of a standard.
This relative difference is represented in delta notation where, δ34S and δ33S are defined as
(Coplen, 2011):
𝛿 34 S = 𝛿(34 S) = δ(34 S/32 S) =
and
[𝑛(34 𝑆)/𝑛(32 𝑆)]𝑠𝑎𝑚𝑝𝑙𝑒 − [𝑛(34 𝑆)/𝑛(32 𝑆)]𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑
(1.1)
[𝑛(34 𝑆)/𝑛(32 𝑆)]𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑
[𝑛(33 𝑆)/𝑛(32 𝑆)]𝑠𝑎𝑚𝑝𝑙𝑒 − [𝑛(33 𝑆)/𝑛(32 𝑆)]𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑
𝛿 S = 𝛿( S) = δ( S/ S) =
(1.2)
[𝑛(33 𝑆)/𝑛(32 𝑆)]𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑
33
33
33
32
A positive delta value (δ34S or δ33S) indicates that the sample is enriched in the heavier
isotope relative to the standard, and a negative delta value (δ34S or δ33S) indicates that the sample
is depleted in the heavier isotope, 34S, relative to the standard. The international measurement
standard for measurement of the relative difference in sulfur isotopes is IAEA-S-1, which has an
accepted δ34S value of - 0.3 ‰ relative to Vienna Cañon Diablo Troilite (VCDT) (Krouse and
Coplen, 1997; Coplen and Krouse, 1998).
The determination of the isotopic abundance of sulfur isotopes, in particular of 33S, using
gas source mass spectrometry is complicated by the presence of oxygen isotopes and
isotopologues of SO 2 and the majority of the published studies report only δ34S values. However,
it is desirable to obtain δ34S data, which can provide important information on the nature of mass
dependent processes responsible for the cycling of sulfur. It was thought that δ33S values carry no
additional information but in recent years additional information on sulfur isotope fractionation
mechanisms have been obtained from the analysis of 33S (Farquhar et al. 2003; Johnston et al.
2005b; Ono et al. 2006). These authors mentioned that by studying all sulfur isotopes with very
high precision one could demonstrate that bacterial sulfate reduction follows a mass dependent
relationship that is slightly different from that expected by equilibrium fractionations. As a result,
2
samples with the same δ34S value can have different Δ33S values (see section 2.2.1.3), which
allow one to distinguish between different fractionation mechanisms and biosynthetic pathways
(Young et al. 2002; Ono et al. 2006). Thus measuring sulfur isotopic composition (δ34S and δ33S)
might have great potential in studying different sulfur isotopic fractionations or sulfur absorption
in the geologic record (see section 2.2.1).
1.1 The objective
The goal of the study was to achieve a reliable method for measuring sulfur isotopic
composition (δ34S and δ33S) in sulfur bearing materials using a high mass resolution Multiple
Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS). Particular attention
was given to the results of δ33S values because the existing literatures on sulfur isotope amount
ratios contain few accurate δ33S values. In the present study, δ33S values were obtained with high
precision, which allows one to study sulfur isotopic fractionations.
There are several challenges to achieve the goal of this project. First, sample preparation
for MC-ICP-MS is complex and time-consuming because of the steps required for extracting the
analyte (e.g. sulfur) from samples and the several subsequent steps required for ion exchange
separation. Such separation must be performed to purify the analyte so that it can be analyzed by
mass spectroscopy. Second, the instrumental mass bias within ICP-MS affects the accuracy and
precision of the sulfur isotopic compositions measurements. The instrumental mass bias results
from space charge and ion-diffusion effects on the transmission of ionized particles (Tanner,
1992; Vanhaecke et al., 1993; Marechal et al., 1999). Finally, sulfur isotope measurements by
ICP-MS suffer from isobaric and polyatomic interferences on S+, which leads to the inability to
measure sulfur isotopic compositions with high precision. Interference that occurs with ICP-MS
often arises because polyatomic ions are measured as the same nominal mass as the analyte of
3
interest. Interference can be derived from compounds of the element (e.g. SO 4 2-) in the samples
and in aqueous solution (including O and H), such as 16O+, and 1H+. For example, the 33S+
analysis may suffer from 16O17O+ interferences causing changes in the observed signal intensity
of measured isotopes (Nelms, 2005). Additionally, isobaric interferences, e.g. 32S1H+ on the 33S+
are a major obstacle for 33S+ analysis (Craddock, 2008). Sulfur isotopic measurement requires
high resolution to resolve the overlap of isobaric ions and hydride interference. This is
particularly significant for measuring 33S sulfur isotope ratios because of its low abundance
relative to 32S.
The reliability of the analytical method was verified using the international reference
materials from IAEA (IAEA S-1, S-2 and S-3). In addition, hair samples collected from wild
animals in central Alberta were measured for their sulfur isotopic composition as part of a large
study to trace the diet of wolves in west central Alberta areas to demonstrate a potential
application of this method. Analytical methods for measuring sulfur isotopic compositions were
developed because of the need to understand the predatory activity of wolves in central Alberta,
their food sources and geographical movements. Animals contain sulfur in their hair, which
reflects their history. The sulfur isotopic composition measurements will be used to quantify the
small variations in sulfur isotope abundances different locations. The sulfur isotopic composition
of animals in these different locations also varies as a result of local biological processes, sources
of diet, or geographical movements of the animals (Krouse & Herbert, 1988). Thus the hair
samples must be prepared for sulfur isotopic composition measurements on MC-ICP-MS.
This thesis is organized into six chapters beginning with the introduction to the research.
Chapter two offers a literature review to provide a discussion of the processes that affect sulfur
isotope composition. Chapter three presents the mass spectrometer used in this study. Chapter
4
four, reports a description of the experimental method used in this study including sample
extraction, purification steps, and the measurement of sulfur isotope amount ratios using MCICP-MS. Chapter five discusses the results of the methods. The final chapter summarizes the
research findings and provides conclusions and recommendations for future research.
5
2 Sulfur Isotopic Fractionation
This chapter describes sulfur isotope variations in nature and sulfur isotope
fractionation processes that can produce variations in sulfur isotope abundances such as
mass dependent fractionation (equilibrium and kinetic isotope fractionation) and mass
independent isotope fractionation. Most of the existing data on sulfur isotope variations
in nature have historically been limited to δ34S values. However, both δ34S and δ33S
measurement is required for studies of sulfur isotopic fractionation.
2.1 Sulfur Isotope Variations in Nature
Sulfur is present in nearly all natural environments. It appears as both sulfide and
sulfate in organic substances and in marine waters and sediments (Hoefs, 2010). Figure
(2.1) shows the ranges of δ34S values found in nature for some forms of sulfur. The zero
point of the δ34S scale is from troilite of the Canyon Diablo iron meteorite (CDT), which
was used as the reference standard for many years (Thode, 1991). However, the sulfur
isotope composition of CDT is not homogeneous and may display variations in 34S up to
0.4‰ (Hoefs, 2010). Thus, an advisory committee of IAEA introduced Vienna-CDT or
V-CDT in 1993 as a new reference scale. An artificially prepared Ag 2 S (IAEA-S-1) with
a δ34S VCDT of - 0.3‰ is now recommended as the new international standard reference
material (Coplen et al., 2002).
Sulfur in meteorites, basic sills, and igneous rocks of primary origin have δ 34S
P
values in a narrow range close to zero (Thode, 1991). Volcanic gases and rocks tend to
have δ 34S values distributed symmetrically about zero in a wider range. This distribution
P
is due to a variety of inorganic chemical reactions and equilibria, which tend to enrich
6
oxidized forms (e.g. SO 2 or SO 4 ) of sulfur and deplete reduced forms (H 2 S) of sulfur
in 34S (Thode, 1991).
7
Figure 2.1: Sulfur isotope distribution in nature (Wieser1 & Coplen, 2010).
8
Anthropogenic inputs of sulfur into the atmosphere are in the form of sulfur gases
such as SO 2 or H 2 S from the burning of fossil fuels. Krouse and Case (1983) were able to
provide estimates for the δ34S values for a unique situation in Alberta where industrial
SO 2 had δ34S values near + 20 ‰ for consistent SO 2 emersions. Volcanoes also
contribute to atmospheric sulfur, which yields δ34S values around +5 ‰, as well as
marine sulfate input to the atmosphere merged with sea spray where the δ34S values from
this input range from +17 to +21 ‰ (Nelms, 2005 & Hoefs, 2010). The ocean is a large
reservoir for sulfur since it contains such an enormous amount of sulfate. The δ34S values
have changed over geologic time but currently marine sulfate has a sulfur isotopic
composition of +21 ‰ for δ34S values (Meier-Augenstein & Kemp, 2012). Sulfur takes
on many forms in groundwater, but is usually found as sulfates and sulfides. This sulfate
occurs as a result of the dissolution of gypsum (CaSO 4 .2H 2 O) and anhydrite (CaSO 4 )
(Meier-Augenstein & Kemp, 2012). Additionally, elemental sulfur, some dissolved
organic sulfur, and mineral sulfur might be present in groundwater.
Terrestrial sulfur reservoirs contribute sulfur to the soil and biosphere though
either natural processes such as volcanism, geothermal spring activity, and weathering or
industrial processing (Krouse et al., 1991). Plants receive the majority of sulfur through
their roots as sulfate, from the atmosphere by wet deposition as water droplets falling
(e.g. acid rain, H 2 SO 4 ) to Earth or by dry deposition by uptake of SO 2 gas. The
concentration of sulfur in plants is on average about 0.25 % (Krouse et al., 1991; Mayer
& Krouse, 2004). Sulfur can be used as a tracer of dietary sources due to the small
fractionation during absorption by plants (Zazzo et al., 2011). Plants in specific area will
have δ 34S values reflecting the geographical location. For example, in Alberta, plants are
P
9
enriched with 34S if sample sulfur has been derived from an industrial source, which
emits SO 2 enriched in 34S. This finding could distinguish between the animals that feed
on vegetation, which has absorbsed SO 2 emissions. Thus, different geographical
locations have distinct δ34S values, which can be used to study the variations of sulfur
isotopic compositions for animals in certain locations.
Animals cannot assimilate sulfur to convert it to amino acids as plants, but they
are able to ingest these essential organo-sulfur compounds and interconvert them as
required (Krouse et al., 1991). Sulfate absorption by terrestrial plants appears to be
accompanied by isotopic fractionation (Krouse et al., 1991). Sulfur isotope fractionation
in food must be estimated where one source of sulfur dominates. This means that if δ 34S
P
values of ocean are found to be near +21‰, then fish, algae and plants are expected to
have δ34S values that are only slightly different than those from their main food source.
Further, measuring sulfur isotopic composition in animals could be used to trace the main
source of sulfur in their diet. Krouse et al., (1991) found that the fur of koala bears in
Australia had δ34S values about 2‰ depleted in 34S compared to their diet of eucalyptus
leaves. These authors also found the hair cut from kangaroos at certain locations had δ 34S
P
values close to the mean of their known food sources (Krouse et al., 1991).
10
Sulfur isotopic compositions have also been determined for hair in humans. Hair
contains 3-5 % sulfur in the amino acids cysteine and methionine (Zazzo et al., 2011).
The hair is particularly well-suited to be used to record environmentally determined
variations in δ34S values because it provides a record of the diet of the relevant organism.
The total variation in δ 34S of human hair is about 2 ‰ and appears to approximate the
P
mean sulfur isotope composition of the diet (Krouse et al., 1991). Sulfur isotope ratios of
human hair have been used to provide information about human origin and migration
(Santamaria-Fernandez, 2009). Naturally occurring variations in sulfur isotopes have
been used to associate humans and animals with specific geographic regions of the world
because the variation in sulfur isotopes reflect dietary factors (Ehleringer at el., 2007).
There are significant differences among δ34S values of plants and animals that
derive their sulfur from freshwater and marine ecosystems. The δ34S value of modern
marine ecosystem is about +21 ‰, while the δ34S value is varying between -22 to +22 ‰
for regions that depend on freshwater (Peterson & Fry, 1987; Thode, 1991; Richards et
al., 2003). This wide range of δ34S value for freshwater is due to reduction of sulfate
(SO 4 ) to hydrogen sulfide (H 2 S) by anaerobic bacteria that live in sediments of the rivers
and lakes (Faure, 1977). These bacteria generate energy for survival by using sulfate in
place of molecular oxygen during the oxidation of organic matter (Ricbards et al., 2003).
Because the energy required for breaking 32S-O bond is less then that required for 34S-O
bonds, the final H 2 S is significantly depleted in 34S and in some cases the 34S depletion
could reach 50 ‰ depending on the seasons and environmental conditions (Faure, 1977).
11
The difference between marine and freshwater δ34S values has been used to
discriminate between lake dwelling and migratory ocean fish that coexist in freshwater
lakes in northern Canada (Ricbards et al., 2003). The food web analysis of modern fauna
from the Canadian Arctic was able to distinguish between the animals that live in
terrestrial and coastal environments depending on diets using δ34S value (Krouse &
Herbert, 1988). Animals and birds that live overland had δ34S value less than +10 ‰
while the animals live in polar regions such as bears had values ranging between + 16 and
+18 ‰ reflecting their consumption of marine food. The potential for using sulfur
isotopic analysis in dietary research is fundamental to trace animal mobility and dietary
changes in west central Alberta (Chapter 5). The δ34S values in animal represent the
geographical location, which reflect the local geology and atmospheric sulfur
composition of the certain area (Faure, 1977; Krouse & Herbert 1988). Different
geographical locations have distinct δ34S values, which can be used to study the
variations of sulfur isotopic compositions for animals in certain location or used to for
comparison between different locations.
The sulfur emissions, mainly SO 2 , from industrial sources in Alberta, Canada is
significantly enriched in 34S compared to environmental expected (Krouse et al., 1991).
Sulfur isotopic compositions of SO 2 in Alberta were found to range from +7 to +28 ‰
(Krouse et al., 1991). The low δ34S values were for locations far away from sour gas
plants, whereas sites near sour gas plants have much higher values. Isotopic composition
of waters in Peace River location found to be depleted in 34S consistent with the isotopic
composition of the native soils in the region (Hitchon and Krouse, 1972). Sulfur isotopic
composition of surface of water (SO 4 2-) in most locations remote from industrial activity
12
tends to be near 0 ‰ or negative (Krouse et al., 1991). The δ34S values of soils in Alberta
range from -30 to +5 ‰ and near 0 ‰ are found in southeast Alberta, and the negative
δ34S values are found in the Peace River area and south-western corner (Krouse & Case,
1981; Krouse et al., 1991). Also, Krouse et al., (1991) found that the δ34S values in the
foothills near -10 ‰. These findings support the notion that stable sulfur isotope
abundance effectively supports many environmental investigations. Indeed, sulfur
isotopic composition can be used as a tracer for the fate of sulfur gas (e.g. SO 2 ) emissions
in the surrounding ecosystem.
13
2.2 Sulfur Isotope Fractionation Process
The process that partitions isotopes between two materials or two phases of the
same material with different isotope ratios called isotope fractionation. The partitioning
of isotopes could be between two substances such as reactant versus product or two
phases of the same substance such as liquid versus vapor. The main phenomena
producing isotope fractionations are mass-dependent equilibrium and kinetic
fractionation and mass independent isotopic fractionations.
2.2.1 Mass dependent Isotopic Fractionation
Isotopes of an element can react in physical processes (e.g. ultrafiltration or
gaseous diffusion of ions or molecules.) and chemical reactions (e.g. redistribution of
isotopes of an element among phases, molecules, or chemical species) at different rates
due to their small mass differences (Clark & Fritz, 1997; Coplen et al., 2002). Such
processes and reactions produce mass dependent isotope fractionation. There are two
types of mass-dependent processes that are responsible for naturally occurring isotopic
fractionation: equilibrium isotope fractionation and kinetic isotope fractionation.
2.2.1.1 Equilibrium Isotope Fractionation
Isotopic fractionation can be represented in equilibrium processes as an isotopic
exchange reaction (e.g. 32S and 34S) between any two molecular species or phases that are
participating in the reaction. For isotopic equilibrium, it requires that chemical
equilibrium exist, so the backward and forward reaction rates in equilibrium fractionation
are equal (Clark & Fritz, 1997). Different molecular species that contain a common
14
element might have different isotope ratios (Urey, 1947). For example the chemical
equilibrium between hydrogen sulfide (H 2 S) and sulfur dioxide (SO 2 ) is expressed as:
H 2 32S ⇌ 32SO 2
and
H 2 34S ⇌ 34SO 2
Thus, the overall isotope exchange equation given by the summation between the above
two exchange reactions as follow:
K
H 2 34S + 32SO 2 ⇔ H 2 32S + 34SO 2
(2.1)
The equilibrium fractionation is expressed by the equilibrium constant (K) of the reaction
(2.1), which is equal to the fractionation factor in this case.
𝐾=
[34 𝑆𝑂2 ]/[32 𝑆𝑂2 ]
[𝐻2 34 𝑆]/[𝐻2 32 𝑆]
(2.2)
The ratio 34S/32S will not be the same in the two equilibrated phases when K is not
unity (Thode, 1991). For example, the K of the isotopic exchange between H 2 S and SO 2
is 1.0064 at 800 K (Thode et al., 1971). Thus, δ34S value of SO 2 at 800 K under
equilibrium fractionation will be 6.4 ‰ greater than δ34S value of H 2 S. The value of
sulfur isotopic composition in SO 2 and H 2 S will be a function of temperature and
pressure. Increasing of temperature produce a small isotope fractionation. In addition,
pressure and bond strength between two phases can affect the equilibrium isotopic
fractionation.
15
2.2.1.2 Kinetic Isotope Fractionation
Kinetic fractionation occurs during processes that are incomplete and
unidirectional, such as evaporation and sulfate reduction by bacteria (Clark & Fritz,
1997). For example, sulfate reduction in which isotopic fractionation results from
differences in reaction rates of different isotopic species represents kinetic fractionation.
Competing reactions of the chemical reduction of sulfate are:
𝐾32
SO 4 2- �� H 2 32S
32
and
𝐾34
SO 4 2- �� H 2 34S
34
In this example, the isotope fractionation factor between the instantaneously generated
product and remaining reactant is given by the following expression:
𝐾32
𝐾34
The value of this rate constant was measured for sulfate reduction and was ~
1.022 at room temperature (Thode, 1991). As a result, the lighter isotope (32S) is enriched
in H 2 S and depleted in SO 4 2-. The δ 34S value of the produced (H 2 S) is depleted in 34S by
P
about 22 ‰ relative to the remaining SO 4 2- (Thode, 1991). In general, the magnitude of
isotopic fractionation for sulfur in kinetic processes depends on the rate of sulfate
reduction with highest fractionation appearing at low rates (Thode, 1991).
16
2.2.1.3 Mass Dependent Fractionation Laws
Isotopic fractionations depend on the relative mass difference between stable
isotopes (Johnson, et. al., 2004). Isotopic fractionations decrease with increasing atomic
number because the relative mass difference also decreases. For example, the relative
mass difference of 34S and 32S is approximately twice the difference between 33S and 32S,
and 33S has an extra neutron compared to 32S. Therefore, the fractionation between 34S
and 32S will be approximately twice as large than between 33S and 32S. If there are two
compounds (or two samples) a and b, the fractionation factor is defined as the isotope
amount ratio of the compound (a) divided the isotope amount ratio of the compound (b),
and the ratio (R) is the heavy over light isotope in a chemical compound.
𝛼𝑎−𝑏 =
𝑅𝑎
(2.3)
𝑅𝑏
The fractionation for two or more isotope is described by the relationship between
the fractionation factors of these isotopic ratios. For example, the fractionation factors for
sulfur isotopes are:
and
𝛼33/32
(33 𝑆/32 𝑆)𝑎
= 33 32
( 𝑆/ 𝑆)𝑏
(2.4)
(34 𝑆/32 𝑆)𝑎
(34 𝑆/32 𝑆)𝑏
(2.5)
𝛼34/32 =
Where, a and b refer to either two different compounds or different materials. The
fractionation factor in term of physical processes could reflect equilibrium and kinetic
fractionation of isotopes (Johnson and Beard, 2004). At equilibrium, the two fractionation
factor related by the expression:
17
1
1
ln 𝛼33/32 �𝑚𝑚32 − 𝑚𝑚33 �
=
ln 𝛼34/32 � 1 − 1 �
𝑚𝑚32 𝑚𝑚34
(2.6)
Where 𝑚𝑚32 is the atomic mass of 32S (31.972070), 𝑚𝑚33 is the atomic mass of 33S
(32.971456), and 𝑚𝑚34 is the atomic mass of 34S (33.967866). The equation (2.6) can be
rearranged to give:
𝛼33/32 = �𝛼34/32 �
𝛽
In equilibrium fractionation, the exponent is
1
1
�𝑚𝑚 − 𝑚𝑚 �
32
33
𝛽=
1
1
�𝑚𝑚 − 𝑚𝑚 �
32
34
(2.7)
(2.8)
By using the atomic mass of 32S, 33S, and 34S isotopes, the value of this exponent is 0.515.
The relationship between the fractionation factors above in kinetic process is
slightly different. Young et al., 2002 derived the relationship between these two
fractionation factors above in kinetic isotope fractionations given by the following
expression for sulfur:
𝑚𝑚32
ln 𝛼33/32 ln �𝑚𝑚33 �
=
ln 𝛼34/32 ln �𝑚𝑚32 �
𝑚𝑚34
(2.9)
This equation can be written as:
𝛼33/32 = �𝛼34/32 �
Where the exponent in kinetic fractionation is
𝛽=
𝑚𝑚
ln �𝑚𝑚32 �
33
𝑚𝑚
ln �𝑚𝑚32 �
34
18
𝛽
(2.10)
(2.11)
The value of this exponent for sulfur is 0.508 during kinetic fractionation, which
is obtained by substituting the atomic masses of sulfur isotopes. In summary, the
difference in mass dependent fractionation laws for sulfur is based on the mass dependent
fractionation processes: equilibrium or kinetic, which are characterized by the exponent β
𝛽
in the expression 𝛼33/32 = �𝛼34/32 � where it is 0.515 in equilibrium processes and 0.508
in kinetic processes. One way to compare measured data to theoretical mass fractionation
laws is to plot the data on a three isotope plot (e. g. δ34S on the x-axis and δ33S on the yaxis) and examine how closely the data follow the different trends defined by the
exponent β.
2.2.1.4 Three isotope plot
Hulston and Thode (1965) introduced the concept of mass fractionation in a threeisotope plot and showed that the delta values follow a linear trend where the slope is the
exponent β. When precise sulfur isotopic compositions collected by MC-ICP-MS are
plotted, it is possible to determine the values for β from the best fit slopes described by
the data that leads to a determination of the particular mass fractionation process. Figure
(2.2) shows mass dependent fractionation lines relative to equilibrium and kinetic isotope
fractionation on three-isotope plot. The slopes 0.515 and 0.508 on the plots of 𝛿 33 𝑆 vs.
𝛿 34 𝑆 values for the data indicates the mass fractionation curves which are defined by the
equilibrium and kinetic fractionation laws.
The determination of mass fractionation requires both δ34S vs. δ33S values to be
measured in a sample for ∆33S calculation. The simplest definition of ∆33S is given to
describe the fractionation that measured samples are followed.
19
∆33 𝑆 = 𝛿 33 𝑆 − β × 𝛿 34 𝑆
(2.12)
Where β is the main parameter that characterizes the mass dependent fractionation. The
∆33S notation used for determining small differences in β to facilitate the analysis of
fractionation laws as well as to distinguish between mass dependent and mass
independent isotopic fractionation. Hulston and Thode (1965) defined ∆33 𝑆 as 𝛿 33 𝑆 −
0.515 × 𝛿 34 𝑆, where 0.515 refers to the equilibrium value for β, and ∆33S is zero
because δ33S and 𝛿 34 𝑆 are defined in terms of the 33S/32S and 34S/32S ratios relative to V-
CDT.
20
Figure 2.2: A hypothetical three-isotope plot for sulfur. The mass dependent fractionation; equilibrium and kinetic fractionation lines are shown. The sold line shows the
equilibrium fractionation trend with slope of 0.515 and the dashed line shows the kinetic fractionation trend of sulfur with slope of 0.508. Thus, any deviation from these
trends indicates mass independent isotope fractionation.
21
2.2.2 Mass Independent Isotopic Fractionation
Most cases of isotope fractionation are described by a linear relationship between
δ33S and δ34S values. However, a few processes in nature do not follow this fractionation
(Vanhaecke & Kyser, 2012). Mass independent fractionation (MIF) is less common than
mass dependent isotope fractionation and has been only observed for a few metal
elements and oxygen in ozone (Hoefs, 2010). For example, Further, Farquhar et al.
(2000) reported significant mass independent sulfur isotope fractionations in sulfides
older than 2.4 Ga, whereas these fractionations do not occur in measurable amounts in
sulfides younger than 2.4 Ga. Figure (2.3) shows that the ∆33S was non-zero before 2.45
Ga but after that time all the ∆33S values ranged around zero per mil confirming that there
was no evidence of mass independent isotope fractionation. Recall, the ∆33S value is
calculated relative to mass-dependent fractionation, see equation (2.1.2). However, before
2.4 Ga (which is called Great Oxidation Event) photochemical reactions in the
atmosphere had the capacity to fractionate sulfur isotopes by mass-independent
mechanisms and the reactions of SO 2 to sulfuric acid (H 2 SO 4 ) is thought to be the source
reaction for these sulfur mass independent fractionation (Farquhar et al. 2001). These
authors interpret the disappearance of mass independent fractionations after 2.4 Ga as
evidence for the transition from an anoxic to anoxic atmosphere.
22
Figure 2.3: Compilation of Δ33S vs. age for rock samples. Note large Δ33S before 2.45Ga, indicated by vertical line, and
small but measurable Δ33S after 2.45 Ga (Farquhar et al. 2007).
23
3 Mass Spectrometery of Sulfur Isotopes
This chapter discusses multiple collector inductively coupled plasma mass
spectrometry (MC-ICP-MS) instrumentation (Figure 3.1), including the inductively
coupled plasma (ICP), source sector field magnetic sector, and multiple collection.
Further, the resolution of major isobaric interferences, including oxygen isotopologues,
and instrumental mass bias are discussed.
Figure 3.1: MC-ICP-MS (Thermo Fisher Scientific), a Nier-Johnson geometry design. The MC-ICP-MS includes
a sample introduction system. An inductively coupled plasma (ICP) is used for generating ions of interest from
the sample. An electrostatic filter selects ions of particular kinetic energy and a magnetic field separates the ions
according to their mass-to-charge ratio. A mass spectrum with flat-topped peaks suitable for isotope ratio
measurements is produced. Finally, multiple collectors measure the ion beams simultaneously (Jakubowski at
el., 2011).
24
3.1.1 Sample Introduction
The sample introduction system is designed to generate fine aerosols from a
sample solution that contains the element of interest (e.g. sulfur) (Thomas, 2008). The
size of the aerosol particles delivered to the plasma must be very small (less than 10 μm
diameter) because particles that are too large are not efficiently ionized in the plasma
source (Albarede & Beard, 2004). The sample introduction system consists of a
nebulizer, which is used to convert the sample solution into a fine aerosol, and a spray
chamber. When the solution sample enters into the nebulizer, it is broken by a flowing
gas into small droplets. The spray chamber is designed to remove any large droplets
produced by the nebulizer and allows only a small amount of aerosol to pass into the ion
source (Criss, 1999). Figure (3.1) shows two designs of spray chambers: a Scott doublepass spray chamber (stable introduction system) and cyclonic spray chamber. Both types
are typically used for the same function to remove the large droplets, and to carry the
small aerosol into the ICP-MS (Thomas, 2013). This is required to assure a stable plasma
and guarantee efficient ionization in the plasma. The cyclonic spray chamber was the
design used in this study because it offers somewhat higher sampling (analyte
introduction) efficiency, which translates into higher sensitivity and lower detection
limits (Thomas, 2013).
25
Figure 3.2: On the left side of the figure is a Scott double-pass spray chamber (stable introduction system) and
on the right is a cyclonic spray chamber that was used in this study.
26
3.1.2 ICP Source
The Inductively Coupled Plasma (ICP) is an ion source that operates at high
temperatures between 7000 to 10000 Κ to ionize atoms. The ICP source is built from
three main parts: a torch, a load coil and a RF generator. When RF power (1200 W) is
applied to the load coil, current occur within the coil at a rate corresponding to the
frequency of the generator, 27 MHz (Thomas, 2013). The current in the coil creates an
intense electromagnetic field at the top of the torch. With argon gas flowing through the
torch, an ignition spark is applied to the gas creating electrons, which are accelerated in
the magnetic field and collide with argon atoms (Thomas, 2013). This collision-induced
ionization continues creating electrons from the argon gas forming an ICP discharge
(Thomas, 2013). The ICP discharge is sustained within the torch, as RF power is
transferred from to the load coil through the inductive coupling process (Thomas, 2013).
Figure 3.3: The ICP torch of MC-ICP-MS (Albarede & Beard, 2004).
27
Figure (3.3) shows the analyte aerosol entering the plasma using a torch made of
quartz glass and consisting of three concentric tubes: outer tube, middle tube, and inner
tube. The outer tube has an Ar flow rate of 10-15 L/min, which used as a cooling agent to
keep the torch from melting (Albarede & Beard, 2004). An auxiliary gas (or intermediate
gas) flows through the middle tube in rate of ~1 L/min to stabilize the plasma. The
nebulizer gas in which the aspirated sample that has become a fine aerosol, is injected
into the inner tube using a carrier gas (the plasma gas, Ar) (Thomas, 2008). Moving
further into the plasma, the fine droplets is vaporized (by stripping away the water
molecules) and then atomized. Electrons are stripped off the atoms to form ions, which
are passed through the plasma torch into an interface region and then into the electrostatic
and mass analyzers.
The interface is needed to transfer the ions from the plasma, which is at
atmospheric pressure, to the mass spectrometer, which is under high vacuum as shown in
Figure (3.4). The interface region is designed with two cones (sampler and skimmer
cone) to step down the atmospheric pressure (760 torr) in the ICP source to the mass
spectrometer high vacuum, which is maintained at 10-7 to 10-9 torr (Vanhaecke & Kyser,
2012). These cones are made of nickel with very small orifices and the region between
them is maintained at a pressure of 1-2 torr with a mechanical pump. The orifice of the
sampler cone is between 0.8 -1.2 mm and is followed by a smaller orifice of the skimmer
cone 0.4-0.7 mm (Fontaine, 2010). The ion beam is extracted from the interface region
and shaped by the ion lenses that are placed behind the skimmer cone and guided through
the flight tube into the electrostatic sector.
28
Figure 3.4: The interface region of an ICP-MS (Košler & Sylvester, 2003).
29
3.1.3 Electrostatic and Magnetic Sectors
The MC-ICP-MS is a combination of electrostatic and magnetic sectors, which
serve to filter the energy of the ions and separate the ions according to their mass-tocharge ratio, respectively (Fontaine, 2010). The electrostatic analyzer (ESA) is positioned
in front of the magnetic sector to enable simultaneous detection of several isotopic ion
currents. (Fontaine, 2010; Nelms, 2005). The electrostatic analyzer (ESA) consists of two
curved plates of opposite charge to filter the ions based on their kinetic energy. The ions
are forced to move along a circular path when they travel through the electric field. The
force on the ions is equal to the centripetal force due to the electric field. Therefore, only
ions of a particular kinetic energy are focused at the entrance to the magnetic sector,
whereas ions of different kinetic energies are dispersed. The kinetic energy of an ion of
mass (m), charge (q) and with velocity (𝑣) accelerated in a source through a potential
difference (V) is given by:
1
𝐾𝐸𝑆𝐴 = 𝑞𝑉 = 𝑚𝑚𝑣 2
2
(3.1)
For ions leaving the ESA the electrostatic force (𝑞𝐸), were E is the magnitude if the
electric field, is equal to the centripetal force;
𝑚𝑚𝑣 2
= 𝑞𝐸
𝑟
(3.2)
2 𝐾𝐸𝑆𝐴
𝑞𝐸
(3.3)
Substitution of (3.1) in (3.2) provides:
𝑟=
When the ions are considered to pass through the ESA, equation (3.3) shows that they are
dispersed as a function of their kinetic energy. Only ions within an acceptable energy are
30
transmitted in the direction of the magnetic sector when a plate with a narrow slit is
placed behind the ESA and ions that showing either too high or too low energy are
removed from the beam. (Vanhaecke & Kyser, 2012). The narrower the slit, the spread of
energy decreases, but this also decreases the transmission efficiency.
After the ions leave the energy filter, they enter a magnetic field, which separates
ions according to their mass to charge ratio. The ion is moving in a circular path in the
magnetic field with radius 𝑟, so the ion motion is explained by the centripetal force (𝐹)
as:
𝐹 =
𝑚𝑚𝑣 2
𝑟
(3.4)
The magnitude of a force exerted by the magnetic field required to accelerate an ion
along a circular path is given by the Lorentz force:
𝐹 = q𝑣𝐵
The centripetal acceleration is
𝐹 = 𝑞𝑣𝐵 =
The radius is then described by
𝑟=
(3.5)
𝑚𝑚𝑣 2
𝑟
𝑚𝑚𝑣
𝑞𝐵
(3.6)
(3.7)
The ions were given kinetic energy in the ion source and this kinetic energy can be
written as a function of accelerating voltage (𝑉) and charge (𝑞):
𝐾 = 𝑞 𝑉 = ½ 𝑚𝑚𝑣 2
From this equation velocity (𝑣) will be
31
(3.8)
and the radius is also given by
2𝑞𝑉
𝑣=�
𝑚𝑚
𝑟=
√2𝑉𝑚𝑚
𝐵�𝑞
𝑚𝑚
𝐵2𝑟 2
=
𝑞
2𝑉
(3.9)
(3.10)
(3.11)
This equation gives the expression for deflecting ions of a given mass-to-charge ratio. It
shows the radius of the circular path of the ion when it passes through the magnetic field.
According to equation (3.11), changing 𝐵 and 𝑉 focuses a given 𝑚𝑚/𝑞 into a detector.
3.1.4 Multiple Collectors
The Thermo-Fisher Neptune MC-ICP-MS is equipped with eight Faraday cups
that can be positioned along the focal plane and one collector fixed in the axial position to
collect a maximum of nine ion currents (Wieser & Schwieters, 2005; Jakubowski et al.,
2011). Each Faraday cup is connected to a current amplifier, which converts the
incoming ion current into a voltage with a high-ohmic feedback resistor. Faraday
collectors must operate over a current range of 10-14 to 10-10 A using a 1011 Ω resistor
(Wieser & Schwieters, 2005). There are nine graphite Faraday cups, labelled H4, H3, H2,
H1, C, L1, L2, L3 and L4. The positions of the high (H) and low (L) cups are adjustable
and three of these cups can be arranged to simultaneously detect all the signal ion
because of sulfur. However, the signal intensities of sulfur isotopes suffer from molecular
interferences, which must be resolved from the sulfur isotopes. The resolution of the
sulfur isotopes from the interferences is described in the following section.
32
Figure 3.5: Pseudo-high mass resolution created by MC-ICP-MS. Schematic representation of measurement at ‘pseudo high resolution’ using
multi-collector ICP-MS. Lower figure: mass spectral peak obtained by scanning across the mass range of interest. The signals of analyte and
interfering ions are not completely resolved, but three peak sections can be recognized from left to right: contribution of analyte ion only,
contribution from both analyte and interfering ion and contribution from interfering ion only. Final data acquisition is carried out under the
conditions represented by the upper left figure: only analyte ions are permitted to enter the detector. For completeness, also the situation where
both ions contribute to the signal and only the interfering ion contributes to the signal have been shown in the upper part in the middle and right
figures, respectively (Jakubowski et al., 2011).
33
3.2 Interferences on Sulfur Signal Intensities
Isobaric interferences occur for equal mass ions of different elements, for
example, 33S analysis may suffer from the isobaric interference of 32S1H. This type of
interference could be corrected by measuring the signal intensity of the ion that interferes
with the sulfur isotope of interest and subtracting an appropriate correction factor from
the intensity of the interfered ion. Alternatively, high mass resolution could separate this
interference and enable the measurement of the sulfur isotope signal completely free from
interferences, Figure (3.5). Mass resolution is calculated as m /Δm where m is the mass
and distance between two peaks (Δ𝑚𝑚). Two peaks are said to be resolved when the valley
between them is 10 % of the peak height. Complete resolution (i.e. back to baseline
between peaks) is not possible for many isobaric interferences because of the small
relative mass differences between the interfering ions and the ion of interest, thus pseudohigh mass resolution must be used as shown in Figure (3.5). It is important that the
method used to resolve interferences preserve the flatness of the peak in order to achieve
high precision ion current measurements (Wieser & Schwieters, 2005). Such high
precision is obtained by modifying the source slit width to generate an interference-free
region between the ion beams in the focal plane of the mass spectrometer (Vanhaecke &
Kyser, 2012). Setting the slits on the detector wider than the beam widths at the focal
plane is what produces high precision isotope ratio measurements. On the MC-ICP-MS
used in this study, these widths correspond to three different mass resolutions: low (R
=300), medium (R = 4000), and high resolution (R = 10000) (Nelms, 2005). A wide
plateau is generated as the relatively narrow image of the ion beam is moved across the
wide detector opening (Vanhaecke & Kyser, 2012). The ions of isobaric interferences
34
always appear on the high mass side of elemental peaks because of their relatively large
mass defect compared to the mass of isotopic ions (Vanhaecke & Kyser, 2012). Thus,
one can position the detectors to measure only the target ion currents and avoid the
entrance of isobaric ions into the detectors.
Polyatomic ions are a matrix that is produced by molecular species, which are
formed in the argon plasma and in the interface between the ion source and the mass
spectrometer (Albarede & Beard, 2004; Craddock at el., 2009), specifically the
hydroperoxide group (16O16O+, 16O16O1H+, and 16O17O1H+). These polyatomic ions cannot
be resolved at low mass resolution, Table (3.1). For example, for 34S+ and when the
matrix is H 2 O, the interference is 16O 18O+ and the resolution required to resolve this
polyatomic interference is ~1300. Mass resolution at a range of 2000-5000 is required for
most polyatomic interferences, Table (3.1). This resolution is in the so-called “medium
range”, so a resolution in the range of 10,000 or higher is required for isobaric
interference to separate 33S from 32S1H for accurate and precise determination of 33S
isotopic composition (Krupp at el., 2003; Craddock at el., 2008; Amrani at el., 2009).
Isotope
Interference
m /Δm
32S
16O 16O
1801
1461
1260
3907
1297
1000
2977
33S
34S
16O 17O
16O 17O 1H
32S1H
16O 18O
16O 17O1H
33S1H
Table 3.1: isobaric interferences on sulfur isotope masses (Craddock et al., 2008).
35
Figure 3.6: Peak profile when the adjacent ion beam profiles of ion species A and B with a mass difference of ∆𝒎 are scanned across the detector slit. The beams are
separated by the use of a high-resolution entrance slit (narrow slit width). The detector slit width spans the mass range from -15 to 15 on the horizontal scale. First the
lighter ion species A enter the detector slit to form the first plateau section of the peak while the heavier ions species B is clipped at the high mass side of the detector slit.
The second plateau is created when both ion beams (A + B) enter the detector simultaneously. Then, ion beam A is clipped on the low mass side of the detector slit and
species B forms the third plateau section (Weyer & Schwieters, 2003).
36
Figure (3.6) shows the practical mass resolution of the pseudo high-resolution
peak, which is called the resolving power, a measure of how large the interference-free
partition of the peak is. The resolving power of a mass spectrometer 𝑅𝑝𝑜𝑤𝑒𝑟 , is calculated
as:
𝑅𝑝𝑜𝑤𝑒𝑟 =
𝑚𝑚
𝑚𝑚(95%) − 𝑚𝑚 (5%)
Where 𝑚𝑚 is the mass of the peak and 𝑚𝑚 (5%) is the mass at which single intensity is 5%
of the peak height and 𝑚𝑚(95%) is the mass where the signal intensity is 95% of the peak
height.
37
3.3 Mass Bias on MC-ICP-MS
The isotope amount ratio measurement obtained through the use of MC-ICP-MS
can deviate from the true value by 3 % per mass unit (Craddock at el., 2008). For
example, a n(34S) / n(32S) isotope amount ratio for IAEA-S-1 of approximately
0.049004544 would be measured by MC-ICP-MS while the true ratio is 0.0450045. This
phenomenon within ICP-MS instruments is caused by space charge effects in the
interface region (Meija et al., 2012 & Kleine et al., 2009 & Ellison et al., 2000). Figure
(3.7) shows the interface region of the MC-ICP-MS, which consists of sample and
skimmer cones. When the positive ions are transferred through the cones, a positive
charge is created in the space between the cones. This affects the ion beam, causing ions
to be scattered away from the central beam and the orifice of the skimmer cone. The
electrons diffuse out of the ion beam because of the operating (Thomas, 2013). In
addition, the electrons diffuse further from the beam than the ions because the small size
of the electrons relative to the positive ions (Thomas, 2013). The positively ion beam
passing through the skimmer cone is enriched in the heavier isotopes because lighter ions
are more easily scattered due to their lower mass (Heumann, Gallus, & Vogl, 1998).
38
Figure 3.7: Schematic representation of the mass bias occurs on the MC-ICP-MS in the interface region
consisting of sample and skimmer cones. In the interface the heavier ions are transmitted more efficiently
(Albarede & Beard, 2004).
39
Another complicating factor is that the mass bias in MC-ICP-MS varies in time,
presenting a significant challenge for accurately determining isotope amount ratios over a
long measurement session. A correction for instrumental mass bias is thus required.
Several different mass bias correction methods have been used (Alberede at al., 2004).
So-called sample standard bracketing is one method that was successfully used for mass
bias corrections and was used in the present study. The mass bias model in MC-ICP-MS
that is most commonly used is the exponential fractionation law given as:
𝑅𝑚 = 𝑅𝑡 �
𝑚𝑚1 𝑓
�
𝑚𝑚2
Where R m is a measured isotope amount ratio, for example n(34S) / n(32S), R t is
the true isotope amount ratio, 𝑓 is a fractionation factor, and m1 and m2 are the atomic
masses of the measured isotopes. To correct for mass bias, a fractionation factor needs to
be determined. When using sample standard bracketing for mass bias correction, the
measurement of a sample is bracketed by measurements of a standard immediately before
and after the sample. The fractionation factor can be determined for the standards and
then be used to find R t of the sample. The isotopic composition of the standard is known.
Therefore, the average of the measurements of the isotope amount ratios of the standard
is used to calculate the delta value of the sample using the delta notation (see equation 1.1
and 1.2).
40
4 The Analytical Method used to Meaure Sulfur
Isotope Amount Ratios
In this chapter, each aspect of the sulfur isotope extraction and measurement
methods used for analysing sulfur isotope ratios will be explained. There are three steps
for isotopic analysis. First, the sample matrix must be destroyed, which can be achieved
by combustion of the samples (e.g. using Parr Bomb) or by chemical techniques (e.g.
microwave digestion). The next step is the isolation of sulfur from sample and this is
accomplished using ion exchange methods. Finally, the sulfur from the sample must be
prepared in a form appropriate for introduction to the MC-ICP-MS (10 ppm S in 3 %
HNO3) for the precise measurement of sulfur isotope amount ratios.
4.1 Sample Preparation
It is necessary to separate the element of interest from the sample matrix prior to
measuring isotopic composition by mass spectrometry. This separation can be achieved
using off-line methods such as Parr Bomb or microwave digestion followed by ion
exchange. These methods convert liquid or solid samples or into soluble forms that are
suitable for isotope analysis. Combustion of sulfur samples with oxygen in a sealed Parr
bomb is a very effective and reliable method for preparing hydrocarbon compounds and
carbonaceous materials for analysis. However, when working with microgram quantities,
other methods must be used to maintain the concentration of the analyte. For the MCICP-MS instrument, the organic material must be converted to a suitable solution with no
loss of the sulfur.
41
4.1.1 Parr Bomb vs. Microwave Digestion of Hair Samples
Combustion with oxygen in a sealed Parr bomb is the standard method for
converting solid and liquid combustible samples into soluble forms for isotope analysis.
Ten milligrams of sulfur material is weighed accurately into a combustion capsule. The
bomb head is placed in a holder and a 10 cm length of fuse wire connected to the
electrodes. The combustion capsule is then placed in the loop holder while the fuse wire
is bent down as close as possible to the surface of the sample. One mL of methyl or ethyl
alcohol is pipetted into the combustion cup. The bomb is slowly filled to 35 atmospheres
of oxygen. The ignition wires are attached to the bomb connecters; pressing the ignition
button fires the charge. The valve knob of the bomb is opened to release the gas pressure
once the bomb has been left in water for a minimum of three minutes. The bomb head
and combustion capsule are rinsed in the bomb chamber with approximately 100 ml of
MilliQ (MQ) water and then transferred to a 200 ml flask. Despite the effectiveness of
this method, it requires relatively large volumes of water for rinsing the inner contents of
the Parr bomb, which make it unsuitable for the analysis of small amounts of raw
material. Microwave digestion of hair samples is very effective and reliable method
compared to the Parr bomb method because it quickly and conveniently digests samples
using much smaller volumes of reagents (i.e ~2 ml vs ~100 ml) within a closed system
without losing any of the sample or its combustion products. The details of the
microwave digestion method are discussed in section 4.2.
42
4.1.2 Isolation of Sulfur by Ion exchange
Polyethylene frit
Figure 4.1: Ion exchange Columns used for Ion Exchange.
The polyethylene frit is indicated at the bottom of the column.
The first attempt in this project at ion exchange separation was tested using an
anion exchange protocol reported by Das at el., 2012 with seawater samples to extract the
SO 4 2- cations from the solution. About 2 ml of resin (EiChrom Anion 100-200 mesh)
were loaded in the ion exchange column as shown in Figure (4.1). The resin was cleaned
by rinsing it two times; the first time with 5 ml of 1M HCl and second with 5 ml of 1M
HNO 3 . Half a gram of the seawater sample was taken in clean Teflon vials and dried at
70 ºC in order to obtain 500 μg of S (the sulfur concentration in the seawater sample was
approximately 0.091%). The dried materials of the seawater samples were then dissolved
in 1 ml of 0.028M HNO 3 and run through the resin. The ion exchange columns were
rinsed with 5 ml of MQ water twice before the sample was eluted. The final step was
elution of samples by 3.5 ml 0.25M HNO 3 . The collected samples were dried under heat
lamps, and the dried materials were dissolved in 3 % HNO 3 for isotopic analysis through
MC-ICP-MS.
43
The ion exchange technique for the measurement of δ 34S values by MC-ICP-MS
P
was successfully applied to the seawater sample. In the case of the hair samples, the
sulfur must first be separated from the matrix by microwave digestion prior to ion
exchange. However, only a portion of the sulfur was eluted while the rest remained in the
resin. The reason for incomplete recovery of the sample was unknown. Therefore, a
second attempt to obtain sulfur from the hair sample was made and here the use of ion
exchange was omitted and instead only undigested particles were filtered from the
solution prior to analysis on the MC-ICP-MS. It was found that the porous (30 µm)
polyethylene frit in an empty column retain these fine particles and thus reduce clogging
of the introduction system of the MC-ICP-MS. With the digestion of samples by
microwave and fine filtering, there was no need to apply ion exchange separation
methods.
4.2 Preparation of Hair Samples
Thirty-two hair samples from wild animals were collected from three different
locations in west-central Alberta Figure 4.2. Redrock-Prairie Creek (Location 1), Jasper
(Location 2), and Little Smoky (Location 3) are three locations that are separated
geographically. The sample preparation protocol is summarized in Figure 4.3. Hair
samples were cleaned to remove any natural oils or dirt by rinsing the hair samples in
acetone three times and once in Milli-Q water. The cleaned hair samples were dried at <
70 °C to remove only the solvents and maintain the sulfur compounds. An agate mortar
and pestle was used to grind hair samples prior to reaction. To
facilitate the grinding process, liquid nitrogen was added and the ground hair samples
were then dried. Ten milligram of ground hair samples were weighed and placed in
44
Teflon beakers, 1.5 ml of concentrated HNO 3 (≈ 15.4 M) and 0.5 ml of H 2 O 2 were added
and the beakers sealed. Samples were digested in a closed vessel in a microwave twice
for 40 seconds at full power to achieve complete digestion without any loss of analyte.
The reactant material was cooled after each digestion to lower the pressure and avoid
losing the analyte. The digested materials were diluted with Milli-Q water to 10 mL (~ 33
ppm), and sulfur isotope amount ratios were measured by MC-ICP-MS.
45
Location 1
Location 3
Location 2
Figure 4.2: The map was supplied from Dr. Marco Musiani's Landscape Ecology Research Group at the
University of Calgary. The map indicates where hair samples were collected. Each yellow region represents the
caribou ranges designated by the Province of Alberta. Red points indicate specific areas from which samples
were collected.
46
Hair sample
- Wash once with acetone
- Wash three times with H2O
- Wash once with acetone
Dry at < 70 °C
Grind using agate mortar
with liquid nitrogen
Place 10 mg of dried
sample in 1.5 ml 3% HNO3
+ 0.5 ml HCl
Digest the sample using
microwave, 2 x 40s at low
power
Filter and
dilute in H2O
Run on the Neptune
Figure 4.3: The sample preparation protocol. The hair samples
measured directly after filtering the analyte without using the ion
exchange separation method.
47
4.3 Preparation of Sulfur Standards
In this study, the isotopic measurements were calibrated against three
International Atomic Energy Agency (IAEA) standards (IAEA S-1, S-2, and S-3). The
reason for using these reference materials was because they cover a wide range from -32
‰ to +22 ‰, and they are widely available. Solutions of the IAEA standards were made
using the protocol reported by Craddock et al., 2008. However, ion exchange separation,
was not employed because of the purity of these reference materials. Fifty mg of each
sulfur reference materials IAEA-S-1, S-2, and S-3 were accurately weighted into 15 ml
Teflon beakers, which were pre cleaned in concentrated HCl and dried. Samples were
dissolved in 5 ml of 3 % HNO 3 and dried on hot plate at 70 °C. The dried material was
redissolved in 3 ml of concentrated HNO 3 and 2 ml of 4 M HCl, the result of which was
dried again at 70 °C. The remaining white crystalline solids were dissolved with 4 ml of 3
% of HNO 3 then transferred to a centrifuge tube. Insoluble solids were separated from
the solution by centrifugation, and the final solution was adjusted with the required
amount of 3 % of HNO 3 to yield a final stock solution containing of 10 ppm S ready for
isotope analysis.
48
Inlet System
Cool Gas
15 L/min
Aux Gas
0.80-1.00 L/min
Sample Gas
1.000-1.010 L/min
RF power
1200 W
Nebulizer
0.05 and 0.1 ml/min
Spray Chamber
20 mL cyclonic quartz spray
chamber
Cup Configuration
L3
32 S
C
33 S
H3
34 S
Zoom Optics
Focus Quad
-1.10
Dispersion Quad
-1.00
Resolution
High
Acquisition method
Acquisition
25 Blocks
Sample flow rate
200 μg
Integration time
8s
Wash time
230 s
Uptake time
240 s
Table 4.1: The operating parameters for the Neptune (MC-ICP-MS) used for the measurement of sulfur isotope
ratios in IAEA reference materials and hair samples.
49
4.4 Measurement Conditions on the MC-ICP-MC
The measurement of sulfur isotope amount ratios was performed using the MCICP-MS (Thermo Fisher Scientific) working in high resolution to resolve isobaric
interferences. Table (4.1) shows the typical operating parameters for the MC-ICP-MS.
Prepared samples were introduced as SO 4 2- in 3% HNO 3 through the MC-ICP-MS
introduction system using a Glass Expansion Nebulizer with a flow rate of 0.1 ml/min
and a Glass Expansion 20 ml spray chamber. The nebulizer converted the liquid
(including sulfur materials) into an aerosol, which was then directed by the spray
chamber into the inductively coupled plasma (ICP).
The MC-ICP-MS was tuned at the beginning of each run to optimize the
sensitivity, increase the signal stability, and minimize the interferences. The signal
intensities on the MC-ICP-MS of sulfur isotopes were optimized by adjusting the torch
position, gas flow, ion focusing, and magnet field settings before sulfur isotope amount
ratios measurement. First, the torch position (e.g. X, Y, and Z positions of the torch) was
adjusted to optimize the sensitivity, and stability of the signal. Next, the gas flow
(auxiliary gas and sample gas) and ion the potentials of the zoom optics parameters were
optimized to obtain the highest signal intensity possible and to have ideal peak
coincidence, followed by an optimization of the peak shapes. The optimal position of the
center cup to measure sulfur isotope ratios is determined on the peak of the interferencefree shoulder. A scan of the sulfur mass range while aspirating 3 % HNO 3 allows one to
select the optimal magnetic field position for interference-free measurement of sulfur
isotopes, in this case 32.912 u with respect to the fixed axial detector, Figure (4.4). The
plot (a) shows the signals of the measured sample, while the signals of the interferences
50
only without sulfur are shown in plot (b). The low mass shoulder of the peak shows
clearly that no interferences are present where the signal intensities of the sulfur
isotopes 32S, 33S, and 34S are measured (i.e. where the dashed line is positioned).
51
a)
Signal Intensity (v)
IAEA S-1 in 3% HNO3
6.0
5.0
4.0
3.0
H3*20
2.0
C*119
L3
1.0
0.0
32.90
32.91
32.92
32.93 32.94 32.95 32.96
Mass in Center Cup (u)
32.97
32.98
32.97
32.98
b)
Signal Intensity (V)
background (3% HNO3)
6.0
5.0
4.0
3.0
2.0
1.0
0.0
32.90
32.91
32.92
32.93 32.94 32.95 32.96
Mass in Center Cup (u)
Figure 4.4: Mass scan using high-resolution mode with aspirating 10 ppm IAEA S-1 in 3% HNO 3 (a) and 3%
HNO 3 only (b) introduced into MC-ICP-MS using cyclonic spray chamber. Three Faraday cup collectors L3, C,
and H3 are arranged to measure 32S, 33S, and 34S, respectively. All three sulfur isotopes are measured where they
are free from spectral interferences, i.e. at a magnetic field setting of 32.912 u relative to the axial detector 33S.
The plot (a) shows the signal of the measured sample free from interferences on the low mass shoulder of the
peak. The plot (b) shows clearly that no interferences are present where the signal intensities of the sulfur
isotopes 32S, 33S, and 34S are measured (i.e. where the dashed line is positioned).
52
10.0
9.0
16O16O+
Signal Intensity (v)
8.0
7.0
H3*20
C*119
L3
16O17O+
6.0
5.0
4.0
3.0
32S1H+
2.0
1.0
0.0
32.90
16O18O+
32.91
32.92
32.93
32.94
32.95
32.96
Mass in Center Cup (u)
32.97
32.98
32.99
Figure 4.5: Peak shape for sulfur isotopes species. Green line is 32S, the red line is 33S, and the blue line is 34S for 10 ppm S solution. Beams are collected simultaneously
on three Faraday cups: L3 (32S), C (33S), and H3 (34S). Significant interferences occur on all sulfur masses but the plateau on the lower mass indicates resolved
interferences where sulfur isotopes could be detected.
53
Figure (4.5) shows the high-resolution peak shape for 10 ppm of sulfur standard
introduced to MC-ICP-MS. The three sulfur isotopes were simultaneously detected by
MC-ICP-MS in three different collectors: L3 [32S (green)], C [33S(red)], and H3 [34S(light
blue)]. The main interferences were 16O 2 + on 32S (green line) and 16O18O on 34S (light
blue line). Because interfering ions are heavier than sulfur ions, S+ ions of the interest can
be distinguished from interferences on the low mass side of the peak. These interferences
were resolved and left a 0.013u and 0.023u flat shoulder to measure the interference
free 32S and 34S signal, respectively. The red line represents the 33S signal peak, which
has two interferences: the 32S1H+ interference on the right of the peak and the 16O17O+ on
the middle of the peak. Both these interferences have been resolved, leaving a 0.005u flat
shoulder to measure the interference free 32S, 33S, and 34S signals, which allowed δ34S and
δ33S values to be obtained at high precision. Figure (4.6) shows how resolving power was
calculated on the MC-ICP-MS for sulfur analysis at the University of Calgary. The
resolving power that is required to resolve 33S+ from its interference is larger than any
other sulfur isotope because the small mass differences between 33S+ and 32S1H+. Thus
only the position on the 33S+ peak will be considered.
54
Signal Intensity (V)
2.50
2.00
Mass at Max. Signal Intensity =32.909
m(95%)=32.9081
1.50
R=m/(m(95%)-m(5%))=32.909/0.0022≈15000
1.00
0.50
0.00
32.904
m(5%)=32.9059
32.906
32.908
32.910
32.912
32.914
Mass in Center Cup (u)
32.916
32.918
32.920
Figure 4.6: Calculation of the resolving power for high resolution on the MC-ICP-MS in the University of Calgary. The calculated
resolving power is sufficient to resolve all the isotope of sulfur from their respective interferences.
55
The optimal position of the center cup was confirmed by collecting sulfur isotope
ratios at different positions on the interference-free shoulder. The best position was
chosen based on the evaluation of the Δ33S values, where Δ33S is 0 ± 0.13 ‰ to ensure no
interference effects on the collected masses. Figure (4.7) shows a mass scan of the
different mass positions on the low mas shoulder for sulfur isotopes in a standard (IAEAS-1). The measurements along the low mass shoulder were bracketed by sulfur isotopic
compositions measured using 32.926 as the center. Table (4.2) indicates sulfur delta
values that were evaluated by measuring signal intensities on masses at a different
reference position on the signal intensity peak. A small range ≈ 0.005u produced δ34S and
δ33S values within the expected analytical uncertainty. Therefore, δ34S values could be
obtained with high precision and accuracy using positions 3 to 8 as demonstrated in
Figure (4.7). During the measurement, the peak position should not shift from this small
mass range in which accurate sulfur delta values were obtained. Delta values calculated at
position 5 ensure that the drift of the magnetic field strength through the measurement did
not affect the results.
56
Signal Intensity (V)
2.5
2.0
2
1.5
1.0
4
5
6
7
8
9
32.921
11
H3*20 (34S)
C*119 (33S)
L3 (32S)
1
0.5
0.0
32.919
3
10
32.923
32.925
32.927
Mass in Center Cup (u)
32.929
32.931
32.933
Figure 4.7: Mass scan of the low mass shoulder for 32S, 33S, and 34S in the standard. Sulfur data values were calculated for 11 positions from 32.922 to
32.932 between each position and another is 0.001u. Sulfur isotopic ratios were calculated for each position using position 5 as the reference. A small
range, 0.005u, produces delta values within expected analytical uncertainty. Therefore, measurement of sulfur isotope ratios should be performed between
positions 3 to 8.
57
# Position
Mass
δ34S
δ33S
δ33S=0.515*δ34S
1
32.922
29.25
33.76
15.06
Δ33S values
2
32.923
23.96
19.17
12.34
6.83
3
32.924
22.76
11.08
11.72
-0.64
4
32.925
22.55
11.47
11.62
-0.15
5
32.926
22.46
11.44
11.57
-0.13
6
32.927
22.69
11.13
11.68
-0.55
7
32.928
22.54
11.23
11.61
-0.38
8
32.929
22.57
11.89
11.62
0.27
9
32.93
22.50
9.06
11.59
-2.53
10
32.931
22.57
20.60
11.63
8.97
11
32.932
22.67
30.37
11.67
18.7
18.7
Table 4.2: Sulfur isotope compositions in different mass positions on sulfur masses.
58
Signal Intensity (v)
16O18O
16O16O+
32S+
L3
33S+
32S1H+
C
(fixed)
10.0
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
H3*20
C*119
0.005u
L3
Mass in Center Cup (u)
32S+
17O17O+
H3
Figure 4.8: The signal intensities of sulfur isotopes were selected free from isobaric interferences on the low mass shoulder of the peak on L3 for 32S signal, C for 33S
signal, and H3 for 34S signal. All collected signals of sulfur isotopes were within very small area of the free-interference plateau, which is 0.005u wide.
59
The signal peak of 34S and 32S are lined up on the low mass shoulder of the sulfur
plateau and 33S is slightly offset. The relative position of the central cup (C for 33S signal)
is chosen in the middle of the interference free plateau of the 33S signal. As a result, the
signal for 33S was measured in the middle of the free-interference plateau (0.005u) as
well as the signals for 34S and 34S as shown in Figure (4.8).
The n(34S)/n(32S) and n(33S)/n(32S) isotope amount ratios were collected in 25
cycles of 8 seconds integration time. The signal intensity for a solution 10 ppm S
concentration was about 2 V for 32S+. Background signals were examined by measuring
the signal intensities on sulfur masses while aspirating a 3 % nitric acid (i.e. no sulfur
present). Sulfur intensities in the 3 % nitric acid were minor (40-60 mV) compared to the
sulfur intensities of standards and samples (1.7-2 V) on mass 32. Sulfur cross
contamination between consecutive samples was avoided by conducting a 4-minute wash
between solutions. The n(34S)/n(32S) and n(33S)/n(32S) isotope amount ratios of unknown
samples were determined using sample-standard bracketing technique. The true sulfur
isotope ratios were calculated by correction from instrumental mass bias by measuring a
known standard before and after the analyte samples. The n(34S)/n(32S) and n(33S)/n(32S)
ratios were measured and presented in delta notation to determine the δ34S and δ33S
values (equation 1.1 and 1.2). The isotope compositions of standards IAEA-S-2 and
IAEA-S-3 were measured against reference material IAEA-S-1, which was used to
bracket unknown samples (see section 3.1).
Stability of the ion signal was found to affect the measured isotope amount ratios
(Yang, 2009). Practically, two factors were found to affect the stability of the ion signal
of sulfur isotopes: the source tunings of the mass spectrometer and the cleanliness of the
60
inlet system (spray chamber and the nebulizer). The source tunings on the mass
spectrometer include argon gas flows (auxiliary and sample gas), torch position, and the
voltages applied to the ion optic lenses were adjusted for high and stable signal intensity
on the measured ion beam. The optimal argon gas flow varied when changes were made
to the inlet system and also when the argon tank was changed. The source tunings of the
mass spectrometer were always optimized before any measurements session.
The lack of cleanliness of the inlet system was found to be a significant for many
reasons: causing unstable ion beams, varying mass bias, low signal intensities, high
backgrounds, and clogging of the nebulizer where the sample was blocked from
aspirating into the mass spectrometer. The inlet system was cleaned when large numbers
of samples were measured or when incompatible elements with sulfur such as copper,
iron, and molybdenum were measured. In this case, two separate inlet systems (spray
chamber glass and nebulizer) were used; one for sulfur and the other for copper, iron, and
molybdenum. The cyclonic spray chamber of 20 mL was used. The spray chamber was
cleaned by RBS 50 solvent in an ultrasonic bath, followed by MQ water. The nebulizer
was placed in 3 M HNO 3 and heated on a hot plate.
61
5 Results of δ34S and δ33S values Measurements by
MC-ICP-MS
This chapter discusses the results of the measurements of δ34S and δ33S values
with a focus on the precision of δ33S values. The chapter contain a summary of the results
with interpretations of the statistics provided. Additionally, the analysis of the sulfur in
hair samples of the wild animals will be discussed.
5.1 Mass Bias Evaluation on Sulfur Measurement
The standard-sample bracketing method was used to correct for instrumental mass
bias to determine delta values of unknown samples using the IAEA-S-1 standard. The
delta values of sulfur isotope amount ratios were calculated with a correction for mass
bias effects in the instrument (MC-ICPMS) using a linear interpolation between the
measurements of two neighbouring standard analyses. The stability of the instrument
mass bias is evaluated throughout the analytical sessions of the isotopic standard
measurements. The typical mass bias calculated for sulfur standard measurements was
4.4 % per atomic mass unit. However, it varied between analytical sessions. For an
individual session of 10 hours, mass bias varied only by 0.02 ‰ and from 0.0003 to
0.001 ‰ between two sequential standards (Figure 5.1). The high stability of the mass
bias in MC-ICP-MS allowed the sulfur isotope ratios to be determined with high
precision.
62
0.0505
IAEA S-2
34S/32S
a)
ratios
0.05
0.0495
IAEA S-1
0.049
0.0485
0.048
IAEA S-3
0.0475
b)
34S/32S
ratios
0.047
0.0491
IAEA S-1
0.04905
0.049
0.04895
0.0489
0:00
0:57
1:55
2:52
3:50
4:48
5:45
6:43
7:40
Time (hours)
Average 34S/32S=0.0489±0.0003
8:38
9:36
10:33
11:31
12:28
13:26
14:24
Figure 5.1: Section (a) shows the isotope amount ratios of measurements session (~ 10 hours) for IAEA S-1, S-2, and S-3 standards. Section (b) shows the small window
of the mass drift for IAEA S-1 replicate measurements and the isotope amount ratios were between 0.04902 and 0.04897 through the session. There was no significant of
mass drift through time but it is clear that the average measured values of the sulfur isotope amount ratio offset from the true value (see section 3.3).
63
5.2 Results and Discussion of the Standard Samples
The accuracy of the sulfur isotope composition measured for the standards was
verified by determining the agreement between isotopic compositions of certified
reference materials that were measured and the accepted quantity value of these
materials. Table (5.2) shows the accepted values of the sulfur reference materials used in
this study were: IAEA-S-1, IAEA-S-2, and IAEA-S-3 with reported δ34S values of -0.3,
22.67, and -32.30 ‰, respectively (Taylor et al., 2000; Ding et al., 2001). The values of
the analytical results for these reference materials, as determined by this study are
consistent with the accepted δ34S and δ33S values. The precision of the measured results
could enable one to distinguish between mass dependent and mass independent
fractionation for sulfur.
The average δ34S and δ33S values, and standard deviation of 13 replicates of the
measured isotopic ratios of S-1, S-2, and S-3 (using S-1 bracketing before and after S-2
and S-3) over the sequence of one analytical session are reported in Table (5.1). The
corrected data for sulfur standard isotopic ratios during this analytical session are shown
in three isotope plots in Figure (5.2). The linear relation between δ33S vs. δ34S is reported,
and the slope indicates mass dependent isotope fractionation. The measured sulfur delta
values are within the expected range of uncertainty for the mass dependent fractionation
line, which demonstrates that there is no significant 32S-1H impacting these samples,
which would cause the measured isotopic composition to deviate from the linear trend.
The reliability of the technique was evaluated by calculating the standard
deviation of the measurements at the 95.4 % confidence level. The δ34S values for IAEA
S-2 and IAEA-S-3 reference materials in this analytical session agreed with the accepted
64
values at uncertainties of ± 0.19 ‰ and ± 0.30 ‰, respectively. As well, δ33S values had
higher uncertainties ± 0.53 ‰ for sulfur reference materials because of lower signal
intensities for 33S. However, δ34S and δ33S values both agree within the uncertainty of the
accepted values of the reference materials, Table (5.2). As a result, distinction between
mass dependent and mass independent fractionation would be possible. The slope for
δ34S and δ33S values is consistent with mass dependent isotope fractionation process (see
section 2.2.1.4).
65
IAEA S-2
Analyze
δ S (‰)
34
δ S (‰)
33
IAEA S-3
0.515
Δ S (‰)
33
δ S (‰)
34
δ S (‰)
33
*δ34S (‰)
#
0.515
Δ33S (‰)
*δ34S (‰)
1
22.66±0.25
11.13±0.88
11.67
0.55
-32.15±0.27
-16.79±0.84
-16.56
0.23
2
22.50±0.22
11.30±0.90
11.59
0.29
-32.10±0.27
-16.80±0.84
-16.53
0.27
3
22.50±0.20
11.80±0.91
11.59
0.21
-32.84±0.28
-16.34±0.31
-16.91
0.57
4
22.50±0.22
11.21±1.02
11.59
0.38
-32.10±0.19
-16.70±0.86
-16.53
0.17
5
22.61±0.19
11.80±0.91
11.64
0.16
-32.06±0.19
-16.32±0.79
-16.51
0.19
6
22.60±0.23
11.80±1.03
11.64
0.16
-32.20±0.20
-16.61±0.79
-16.58
0.03
7
22.55±0.19
11.27±0.83
11.61
0.34
-32.04±0.18
-17.03±0.88
-16.50
0.53
8
22.66±0.22
11.48±0.89
11.67
0.19
-32.19±0.21
-16.88±1.01
-16.58
0.30
9
22.50±0.24
11.42±0.91
11.59
0.17
-32.20±0.20
-16.70±0.80
-16.58
0.12
10
22.50±0.16
11.90±0.93
11.59
0.31
-32.10±0.26
-16.70±0.90
-16.53
0.17
11
22.50±0.24
11.50±0.95
11.59
0.09
-32.20±0.26
-16.90±0.88
-16.58
0.32
12
22.58±0.18
11.57±1.14
11.63
0.06
-32.04±0.20
-16.43±0.83
-16.50
0.07
13
22.55±0.21
11.67±0.98
11.61
0.06
Average
22.56
11.53
11.62
0.09
-32.19
-16.68
-16.58
0.11
sd
0.06
0.25
0.03
0.26
0.21
0.22
0.11
0.28
2sd
0.12
0.50
0.06
0.54
0.42
0.44
0.22
0.56
Table 5.1: Sulfur isotope ratios determined by MC-ICP-MS analysis of IAEA S-1 and IAEA S-1 in 3% HNO 3 solution expressed in terms of δ34S and δ33S values. The
average delta values are reported within internal precision (1sd) for individual measurement. Reproducibility of sulfur isotopes in IAEA S-1 and IAEA S-2 reference
materials measured against IAEA S-1 during individual session is shown within 2sd. The Δ33S values expressed in term of linear δ34S and δ33S values (Δ33S= δ33S0.515*δ34S) for the investigation of mass dependent fractionation processes.
66
S-2 and S-3 vs. S-1
15.00
δ 33S (‰)
δ 33S (‰) = 0.515±0.002 * δ 34S (‰) - 0.096±0.055
10.00
S-2
5.00
-40.00
-30.00
S-3
-20.00
0.00
0.00
-10.00
-5.00
10.00
20.00
30.00
-10.00
-15.00
-20.00
δ 34S (‰)
Figure 5.2: Three-isotope plots of δ33S versus δ34S for IAEA S-1 and IAEA S-3 during an analytical section. The data are in good agreement with mass dependent
fractionation process because the slope of this plot is 0.515 that refers to mass fractionation laws (see section 2.2.1). In addition, the data confirmed that there was no
evidence of the spectral interferences on the results.
67
δ 34S (‰)
24.00
S-2
23.50
δ 34S = 22.49 ± 0.19 (‰)
23.00
22.50
22.00
21.50
δ 33S (‰)
21.00
14.00
δ 34S (‰)
δ 334S =11.47 ± 0.52 ‰
12.00
11.00
10.00
9.00
8.00
0
5
10
15
20
25
30
35
40
-31.00 0
5
10
15
20
25
30
35
40
-30.50
-31.50
S-3
δ 34S = -32.26 ± 0.30 (‰)
-32.00
-32.50
-33.00
-33.50
δ 33S (‰)
S-2
13.00
-15.00
S-3
δ 33S = -16.81 ± 0.53 (‰)
-17.00
-19.00
Figure 5.3: Reproducibility of sulfur isotopes for the reference materials S-2 and S-3 measured against S-1 over
different analytical sessions. The external reproducibility of S-2 and S-3 was within ± 0.30 ‰ (2sd) for δ34S
values and ± 0.53 (2sd) for δ33S values. For δ33S, the reproducibility is slightly larger than the reproducibility on
δ34S values because of lower 33S signal intensities but still within expected range (Table 5.3).
68
The variability of instrumental mass bias (drift through time) between analytical
sessions can increase the errors associated with applying mass bias corrections to
unknown samples and will compromise (decrease) analytical precision (Craddock et al.,
2008). The mass deviation of isotope ratios during the analytical session was not
significant, as shown in Figure (5.1). There was no evidence of drift over the duration of
replicate measurements. The precision of sulfur isotope analysis using standard-sample
bracketing method has been evaluated with the long-term reproducibility of IAEA S-1, S2 and S-3 standards solution that have been measured over multiple independent
analytical sessions. The delta values for IAEA S-2 and IAEA S-3 measured against IAEA
S-1 during four independent sections are plotted in Figure (5.3) and summarized in Table
(5.2) where illustrated that it was possible to determine the δ34S and δ33S values of IAEA
S-2 and IAEA S-2 with a external reproducibility of ±0.19, ±0.52, ±0.30, and ±0.53,
respectively, which is accepted based on the certified δ34S values precision of the
standards materials.
Samples
Delta
Accepted
Craddock et al.
Values
values (Ding et 2008
Das et al. 2012
Present study
al., 2001)
IAEA S-2
IAEA S-3
δ34S (‰)
+ 22.67 ± 0.26
+ 22.44 ± 0.43
+ 22.26 ± 0.42
+ 22.49±0.19
δ33S (‰)
+11.71 ± 0.55
N/A
N/A
+ 11.47±0.52
δ34S (‰)
- 32.30 ± 0.25
N/A
- 32.29 ± 0.45
- 32.26±0.30
δ33S (‰)
- 16.50 ± 0.32
N/A
N/A
-16.81±0.53
Table 5.2: The δ34S and δ33S values of IAEA-S-2 and IAEA-S-3 of accepted and the recent published values.
Delta values are reported in per mil (‰) and the uncertainties given in 2 sd.
69
The measured δ34S and δ33S values have a high level of precision (2sd=0.53 ‰).
Additionally, the measured delta values differ by 0.20 ‰ from their accepted values.
Table (5.2) shows δ34S and δ33S values of IAEA-S-1, IAEA-S-2, and IAEA-S-3 for
VCDT as well as the recently published values. Craddock et al., (2008) and Das et al.,
(2012) represented δ34S values of IAEA-S-2 with precisions of 0.43 ‰ and 0.42 ‰,
respectively. However, neither of them published the results of the δ33S values because
the contribution from 32S-1H was more than 10 % of the total signal at mass 33. This level
of contribution results in significant error in the measured isotope ratio and poor accuracy
of δ33S values of about 2-3 ‰ (Craddock et al. 2008). Paris et al., 2013 reported
improved techniques for measuring sulfur isotope amount ratios on the MC-ICP-MS
using a heated spray chamber coupled to desolvating membrane. They presented
development and application of trace sulfate analyses on the Neptune Plus and applied it
to seawater profiles. Their goal was to reduce the interferences on the Neptune
measurements for 33S and increase the precision of sulfur isotopic composition. Paris et
al., 2013 measured δ34S values of natural samples with a typical reproducibility of 0.080.15 ‰ (2sd). In the present study, the analytical technique reduced the interferences
on 33S and the precision obtained on the measured δ34S and δ33S values was high.
70
5.3 Results and Discussion of the Hair Samples
Using microwave digestion, selected animal hair samples from three different
locations in central Alberta were analyzed including. Ten samples were from Redrock
Prairie Creek collected from Two Lakes area. Nineteen samples were from Jasper
National Park collected from three different areas: Sunwapta, Signal, and Cavell. Three
samples were from Little Smoky (LSM), a highly industrialized area, with high emission
of SO 2 resulting in high delta values (Dubesky-Personal communication, 2014). All of
these samples were prepared using the methods described in section (4.2) and measured
by MC-ICP-MS in three separate sequences using IAEA S-1 as the reference standard
material measured before and after each sample to correct the instrumental mass bias.
The corrected data for the sulfur isotopic composition of hair samples during three
replicate sessions are summarized in Table (5.3) and plotted in Figure (5.4). A threeisotope plot was used to confirm that results were free of isobaric interferences. The data
should plot a straight line defined by mass dependent isotope fractionation processes (the
slope of the line consistent with mass dependent fractionation). Results that fall off this
curve indicate unresolved isobaric interferences were affecting the measured isotope
amount ratios. In the current study, the data from all hair samples are in good agreement
with mass dependent fractionation and there is no evidence of isobaric interferences
affecting the results.
71
Area
Two Lakes
Signal
Sunwapta
Location
Redrock Prairie Creek
Jasper National Park ( JNP)
JNP
Cavell
LSM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
LSM
N
Animal Species and
sample ID
Deer-mule (M)-W127
Deer-mule (M)-W127
Deer-mule (M)- W127
Deer-mule (M)- W127
Deer-mule (Un)- W127
Deer-wt (M)- W127
Deer-wt (M)- W127
Deer-wt (M)- W127
Moose (F)- W127
Moose (M)- W127
Deer- (Un)- J112
Deer- (Un)- J112
Deer- (Un)- J112
Deer- (Un)-J112
Deer- (Un)- J112
Moose- J112
Moose- J112
Moose (Un)- W112
Moose (Un)- W056
Moose (M)- W056
Caribou (F)- W112
Caribou (F)- W081
Elk- W081
Goat (Un)- W056
Sheep (M)- W110
Elk (M)- W110
Caribou- J120
Wolf-JNP
Wolf- W112
Wolf- LSM
Caribou
Caribou
IAEA S-2
IAEA S-2
IAEA S-3
Session 1
δ34S
(‰)
1.09
2.50
3.59
-2.37
-9.58
4.64
-1.38
-1.33
-3.57
-4.48
11.43
9.54
11.32
11.33
8.10
12.08
12.01
11.42
10.53
9.51
1.93
8.82
8.32
9.95
5.08
6.95
6.33
10.25
11.48
10.3
9.05
10.4
22.67
22.45
-32.09
Session 2
δ33S
(‰)
0.22
1.09
1.30
-1.55
-4.69
2.43
-0.92
-0.69
-2.17
-2.56
4.97
4.36
5.78
5.15
4.36
5.94
5.95
5.64
5.00
4.27
0.29
4.82
4.21
5.28
2.61
3.16
3.21
5.32
5.75
5.18
4.47
5.19
11.31
11.31
-16.72
δ34S
(‰)
1.07
2.19
3.52
-2.29
-8.82
4.78
-1.29
-1.28
-2.73
-4.59
11.87
9.24
11.36
11.31
8.66
12.14
12.60
10.70
10.71
9.53
1.67
8.88
8.44
9.73
5.01
6.78
6.11
10.28
11.28
10.54
9.69
10.76
22.52
22.49
-32.69
Session 3
δ33S
(‰)
0.38
0.82
1.68
-1.21
-5.32
2.59
-0.80
-0.78
-1.53
-2.34
5.44
4.76
4.69
6.46
4.64
5.67
6.03
5.26
4.95
4.46
0.56
4.36
4.22
4.94
2.34
3.36
3.15
5.28
5.80
5.60
4.98
5.53
11.93
11.47
-17.39
δ34S
(‰)
0.84
2.41
2.96
-2.55
-9.22
4.48
-1.64
-1.53
-3.24
-4.75
12.51
9.98
11.73
11.20
9.03
12.46
12.20
11.18
10.74
9.81
2.08
9.01
9.00
9.83
5.07
6.83
6.27
10.79
12.02
10.72
9.37
10.55
22.59
22.62
-32.07
δ33S
(‰)
0.77
1.01
1.96
-0.78
-4.42
3.01
-0.04
-0.29
-1.59
-2.26
6.44
5.42
6.33
6.18
4.16
6.36
6.94
5.34
6.29
5.32
1.06
3.99
5.19
4.90
2.90
2.82
3.43
4.72
6.60
5.69
4.32
5.22
11.53
11.83
-16.04
Table 5.3: The δ34S and δ34S values of the three sessions for the collected hair samples. The first 10 samples are
from Two lakes located in Sunwapta area, the 20 samples after are from three areas all located in Jasper, and
two samples from Litle Smoky area.
72
δ34S vs. δ33S of Hair Sample
10.00
δ33S (‰) = (0.51±0.01) δ34S (‰) - (0.04±0.05)
δ33S (‰ )
R² = 0.99678
8.00
6.00
4.00
2.00
-15.00
-10.00
-5.00
0.00
0.00
-2.00
5.00
10.00
15.00
-4.00
-6.00
-8.00
δ34S (‰ )
Figure 5.4: Three-isotope plot for sulfur measurements. Each point is the average of three separate measurements. A linear relationship was observed between δ33S and
δ34S values. The slope of the line is 0.51 ± 0.01, which indicated that the sulfur isotopes were affected by mass dependent fractionation. The slope (0.51±0.01) is the
exponent β, which refers to the mass dependent fractionation.
73
N
Location
Species
Avg. δ34S (‰)
2sd
Avg. δ33S (‰)
2sd
1.00
± 0.28
0.45
± 0.56
Deer-mule (M)- W127
2.37
± 0.32
0.97
± 0.28
Deer-mule (M)- W127
3.35
± 0.68
1.65
± 0.67
Deer-mule (M)- W127
-2.41
± 0.27
-1.18
± 0.77
Deer-mule (Un)- W127
-9.21
± 0.76
-4.81
± 0.92
Deer-wt. (M)- W127
4.63
± 0.30
2.68
± 0.59
Deer-wt. (M)- W127
-1.44
± 0.37
-0.59
± 0.95
Deer-wt. (M)- W127
-1.38
± 0.26
-0.59
± 0.53
9
Moose (F)- W127
-3.18
± 0.85
-1.76
± 0.71
10
Moose (M)- W127
-4.61
± 0.27
-2.39
± 0.31
11
Deer- (Un)- J112
11.98
± 1.08
5.69
± 1.47
12
Deer- (Un)- J112
9.59
± 0.75
4.85
± 1.07
13
Deer- (Un)- J112
11.6
± 0.49
5.89
± 0.79
14
Deer- (Un)-J112
11.48
± 0.75
5.93
± 1.37
15
Deer- (Un)- J112
8.6
± 0.93
4.39
± 0.47
16
Moose- J112
12.41
± 0.63
6.14
± 0.42
Moose- J112
12.27
± 0.60
6.49
± 1.00
Moose (Un)- W112
11.42
± 0.47
5.76
± 0.97
Moose (Un)- W056
10.81
± 0.63
5.65
± 1.28
Moose (M)- W056
9.87
± 0.78
4.98
± 1.24
Caribou (F)- W112
2.02
± 0.16
0.76
± 0.82
Caribou (F)- W081
8.9
± 0.20
4.39
± 0.82
Elk- W081
8.59
± 0.73
4.54
± 1.13
Goat (Un)- W056
9.84
± 0.22
5.04
± 0.43
Sheep (M)- W110
5.06
± 0.07
2.62
± 0.56
Elk (M)- W110
6.85
± 0.18
3.11
± 0.54
Caribou- J120
6.24
± 0.22
3.26
± 0.30
Wolf-JNP
10.44
± 0.60
5.11
± 0.67
Wolf- W112
10.52
± 0.42
5.49
± 0.55
Wolf- LSM
9.37
± 0.64
4.59
± 0.69
Caribou
10.57
± 0.36
5.32
± 0.38
Caribou
11.59
± 0.76
6.05
± 0.96
5
6
7
8
17
18
19
20
21
22
23
24
Sunwapta
4
Jasper National Park (JNP)
3
Two Lakes
Deer-mule (M)-W127
2
Redrock Prairie Creek
1
25
Signal
26
Cavell
27
JNP
28
29
32
LSM
31
LSM
30
Table 5.4: Summary of three replicates hair samples collected from three Redrock Prairie Creek, INP, and
LSM. The average δ34S and δ33S values is reported in (‰), and the uncertainty in 2sd.
74
Large variations in sulfur isotopic compositions of hair samples were observed
with δ34S values ranging from -9.58 to +4.64 ‰ in wild animals from Redrock Prairie
Creek. A different range in δ34S values was seen in wild animals collected from Jasper,
with δ34S values varying between +2.02 and +12.41 ‰. The results from hair samples
collected in the Little Smoky region (three samples) were on average + 10.51 ‰ with less
variation. The δ34S values of animals located in Jasper National Park were significantly
higher in delta values than those from the Redrock Prairie Creek location. Similar results
were observed for δ33S values. Results obtained from the Two Lakes areas indicate that
the majority samples were depleted in heavy isotope, 34S and 33S, relative to the δ34S
values of the standard.
As can be seen in Table (5.4), there are clear variations of sulfur isotope
compositions in both locations and small differences in the data of animal hair samples
from certain locations. For example, the average δ34S values of animal hair samples from
Two Lakes had low δ34S values (~ -1.10 ‰) compared to animal hair samples from
Jasper Park, which had high δ34S values (~ +10 ‰). The majority of hair samples from
different species had almost similar δ34S values, which could be a reflection of similar
diets.
Several factors, including food source, geographical location, and animal species,
could be responsible for these variations. Similar species were found to have similar
signatures of δ34S values in certain locations, with the exception of a few samples 1, 4, 5,
6, and 21. Though the data was insufficient for confirming the reasons for such
variations, it was enough to confirm the use of the method through the replicate
measurements of these samples. The difference between measured delta values observed
75
over three analytical sessions was less than 1.08 ‰ and 1.64 ‰ for δ34S and δ33S values,
respectively. Table (5.4) shows the uncertainty determined for the sulfur average delta
values. The external reproducibility on three measurements of hair samples was ± 0.45 ‰
and ± 0.75 ‰ (2sd) for δ34S and δ33S values, respectively. Figure (5.4) shows sulfur
isotopic composition of the selected data after isobaric interferences and mass bias effect
had been addressed. The measured sulfur delta values were within the expected
uncertainty level for the mass dependent fractionation line. The consistency in the results
of sulfur isotopic composition suggests that the method used was in fact successful. As a
result of this work, a comprehensive sampling campaign is being designed (C. Dubesky
communication, 2014).
76
6 Conclusions
A method was developed for precise and accurate δ34S and δ33S measurements by
MC-ICP-MS applicable for sulfur bearing materials. This method allowed measurements
of 32S, 33S, and 34S isotopic compositions of sulfur isotopes of sulfur reference materials,
IAEA S-1, IAEA S-2, and IAEA S-3 free from isobaric interference on the MC-ICP-MS.
Major isobaric interferences from oxygen (16O 2 +, 16O17O+, 18O16O+) on sulfur masses of
interest were resolved using high mass resolution. The δ33S values varied significantly
depending on the position of the 33S beam in the center cup. It was found that the setting
the cup position accurately was very important for precision and accuracy of δ33S values.
Instrumental mass bias was corrected using the standard-sample bracketing method. The
mass bias calculated by running IAEA S-1 standard immediately before and after the
unknown sample. No significant drift in mass bias occurred through time (i.e. over one
analytical session ~ 10 h.).
The reliability of the analytical method was evaluated by multiple determinations
of sulfur reference materials, IAEA S-1, IAEA S-2, and IAEA S-3 at sulfur concentrated
of 10 μg/mL. Sulfur isotopic composition (δ34S, δ33S) for reference materials determined
in this study show excellent agreement with the accepted values demonstrating the
accuracy of the method. The delta values for IAEA reference materials were δ34S = 22.49
± 0.19 ‰ and δ33S = 11.47±0.52 for IAEA S-2, and δ34S = - 32.26 ± 0.30 ‰ (2sd) and
δ33S = -16.81 ± 0.53 ‰ for IAEA S-3. The excellent agreement between sulfur isotope
ratios for the standards indicates there was no evidence of spectral interferences on the
sulfur masses. The analytical method presented in this study should enable precise and
accurate sulfur isotope measurement for sulfur bearing materials. This is now being
77
applied to the hair samples of wild animals in central Alberta to measure the isotopic
composition of sulfur to study their diet and the impact of the predatory behaviour of
wolves on animal populations. The analytical method to measure sulfur isotope amount
ratios in hair samples required: sample preparation using microwave digestion and
sample measurement using MC-ICP-MS. The hair samples were pre-cleaned to remove
any contamination and then digested in a microwave at low pressure to avoid loss of
sulfur during sample preparation. Digested hair samples were dissolved in solution and
filtered to remove any residual material that could cause clogging for the inlet system of
the MC-ICP-MS, without the need for ion exchange separation methodology. Sulfur
isotope amount ratios then measured both δ34S and δ33S values. It was determined that the
data were consistent with mass dependent fractionation laws. The ability to measure with
sufficient precision to eventually distinguish between mass dependent isotope
fractionation is an important outcome of this work. The precision calculated by replicate
measurement of animal hair samples was ±0.50 ‰ and ±0.80 ‰ (2sd) for δ34S and δ34S
values, respectively.
Higher δ34S and corresponding δ33S values were observed in wild animal hair
samples from Jasper National Park compared Redrock-Prairie Creek, suggesting that
their animal diets were derived from different sources. Moreover, similar species had
approximately similar δ34S values in the same locations and different values in the
different locations, which indicated that the two locations have resolvable variations in
the food source. The δ34S and δ33S values should enable sulfur isotopic compositions,
including 33S, to be used in an investigation at the University of Calgary to trace the
geographical movement of wild animals based on their diet.
78
Future work will involve the collection of hair samples from animals of different
geographical locations and different animal species, which could provide an opportunity
to investigate a potential correlation between animals and their diet. Further, the next
stage of the future work requires developed analytical method for converting the
vegetation samples to solution for measure sulfur isotopic composition in plant material.
Therefore, more sensitive sample preparation techniques are required. For example, the
direct coupling of an Elemental Analyzer to the MC-ICP-MS could achieve reliable δ33S
and δ34S values from nanogram quantities of sulfur.
79
References
Albarède, F., & Beard, B. (2004). Analytical methods for non-traditional isotopes.
Reviews in Mineralogy and Geochemistry, 55, 113–152.
Amrani, A., Sessions, A.L., & Adkins, J. F. (2009). Compound-specific δ34S analysis of
volatile organics by coupled GC/Multicollector-ICPMS. Analytical Chemistry, 81,
9027–9034.
Austin, S. R., (1970). Some patterns of sulfur isotope distribution in uranium deposits.
Earth Sci. Bull., 3, 5, 243-252.
Birck, J. L. (2004). An Overview of Isotopic Anomalies in Extraterrestrial Materials and their
Nucleosyntetic Heritage. Reviews in Mineralogy & Geochemistry, 55, 25-64.
Blackledge, R. D. (2007). Forensic Analysis on the Cutting Edge: New Methods for
Trace Evidence Analysis. In Ehleringer, J. R, Cerling, T. E., & West, J. B.,
Forensic science application of stable isotope ratio analysis. John Wiley & Sons,
Inc.
Clark, I. D., & Fritz, P. (1997). Environmental isotopes in hydrogeology. CRC Press/
Lewis, Boca Raton, Florida, USA, 328.
Coplen T. B., Krouse H. R. (1998). Sulphur isotope data consistency improved. Nature
392, 32, 1038-32080.
Coplen, T. B. (2011). Guidelines and recommended terms for expression of stable isotope
ratio and gas ratio measurement results: Rapid Communications in Mass
Spectrometry, 25, 2538–2560.
Craddock, P. R., Rouxel, O. J., Ball, L. A., & Bach, W. (2008). Sulfur isotope
measurement of sulfate and sulfide by high-resolution MC-ICP-MS. Chemical
Geology, 253, 102–113.
Criss R. E. (1999). Principles of stable isotope distribution. Oxford University Press,
New York.
Das, A., Chung, C. H., You, C. F., & Shen, M. L. (2012). Application of an improved ion
exchange technique for the measurement of δ34S values from microgram
quantities of sulfur by MC-ICPMS. Journal of Analytical Atomic Spectrometry,
27, 2088–2093.
Ding, T., Valkiers, S., Kipphardt, H., De Bievre, P., Taylor, P. D., Gonfiantini, R., &
Krouse, R. (2001). Calibrated sulfur isotope abundance ratios of three IAEA
80
sulfur isotope reference materials and V-CDT with a reassessment of the atomic
weight of sulfur. Geochimica et Cosmochimica Acta., 65, 2433–2437.
Ellison, S. L., Rosslein, M., & Williams, A. (2000). Eurachem/Citac Guide, Quantifying
Uncertainty in Analytical Measurement, second ed. Laboratory of the
Government Chemist, London.
Epov, V. N., Berail, S., Pécheyran, C., Amouroux, D., & Donard, F. X. (2012). Isotopic
Analysis via Multi‐Collector Inductively Coupled Plasma Mass Spectrometry in
Elemental Speciation. In F. Vanhaecke, & P. Degryse, Isotopic Analysis:
Fundamentals and Applications Using ICP-MS. Wiley-VCH.
Farquhar J., Savarino J., Airieau S., & Thiemens M. H. (2001). Observation of
wavelength sensitive mass-independent sulfur isotope effects during SO 2
photolysis: implications for the early atmosphere. J Geophys Res., 106, 1–11.
Farquhar, J., Bao, H., & Thiemens, M. (2000). Atmospheric influence of earth's earliest
sulfur cycle. Science, 289, 756–758.
Farquhar, J., Johnston, D. T., Wing, B. A., Habicht, K. S., Canfield, D. E., Airieau, S. A.
& Thiemens, M. H. (2003). Multiple sulfur isotopic interpretations of biosynthetic
pathways: implications for biological signatures in the sulfur isotope record.
Geobiology 1, 15–27.
Faure, G. (1977). Sulfur. In Principles of Isotope Geology. Wiley: New York; 523 – 552.
Fontaine, G. H. (2010). Fundamental Studies on Mass Bias Variability in Multi Collector
- Inductively Coupled Plasma Mass Spectrometry and the Use of Isotope Ratios in
Gem Authentication. Zürich: ETH.
Heumann, K. G., Gallus, S. M., Radlinger G. & Vogl, J. (1998). Precision and acuracy in
isotope ratio measurements by plasma source mass spectrometry. J. Anal. At.
Spectrom., 13, 1001-1008.
Hoefs, J. (2010). Stable Isotope Geochemistry - 6th Edition. Berlin: Springer.
Hoffmann,, E., & Stroobant, V. (2007). Mass Spectrometry: Principles and Applications
(3ed. Edition). England: John Wiley & Sons Ltd.
Horstwood, M., & Nowell, G. (2005). Principles of MC-ICP-MS design. In S. M. Nelms,
ICP Mass Spectrometry Handbook. Blackwell Publishing Ltd.
Hulston, J. R., Thode, H. G. (1965a). Cosmic-ray-produced in 36S and 33S in the metallic
phase of iron meteorites. J Geophys Res., 70, 4435-4442.
81
Hulston, J. R., Thode, H. G. (1965b). Variations in the 33S, 34S, and 36S contents of
meteorites and their relation to chemical and nuclear effects. J Geophys Res., 70,
3475-3484.
Jakubowski, N., Prohaska, T., Rottmann, L., & Vanhaecke, F., (2011). Inductively
coupled plasma- and glow discharge plasma-sector field mass spectrometry. J.
Anal. At. Spectrom., 26, 693-726.
Johnson, C. M., Beard, B. L., & Albarede, F. (2004a). Geochemistry of Non-Traditional
Stable Isotopes. Washington: Mineralogical Society of America.
Johnston, D. T., Farquhar, J., Wing, B. A., Kaufman, A. J., Canfield, D. E. & Habicht, K.
S. (2005b). Multiple sulfur isotope fractionation in biological systems. Am. J. Sci.
305, 645–660.
Katzenberg, M. A., & H. Roy Krouse, H. R. (1989). Application of Stable Isotope
Variation in Human Tissues to Problems in Identification. Canadian Society of
Forensic Science Journal. 22, 7-19.
Kleine, T., Voigt, C., & Leister, D. (2009). Plastid signalling to the nucleus: messengers
still lost in the mists? Trends Genet., 25, 185-190.
Košler, J., & Sylvester, P. J. (2003). Present Trends and the Future of Zircon in
Geochronology: Laser Ablation ICPMS , Reviews in Mineralogy and
Geochemistry, 53, 243 – 275.
Krouse, H. R., & Case, J. W. (1983). Sulphur isotope abundances in the environment and
their relation to long-term sour gas flaring, near Valleyview, Alberta. Prepared for
Alberta Environment, Research Management Division and Pollution Control
Division, Air Quality Control Branch by Department of Physics, University of
Calgary.
Krouse, H. R., & Herbert, H. K. (1988). Sulfur and carbon isotope studies of food webs.
In B. V. Kennedy, & G. M. LeMoine, Diet and Subsistence: Current
Archaeological Perspectives. University of Calgary Archaeology Association:
Calgary.
Krouse, H. R., Stewart, J. W. B., & Grinenko, V. A. (1991). Pedosphere and Biosphere.
In H. R. Krouse, & V. A. Grinenko, Stable Isotopes: Natural and Anthropogenic
Sulfur in Environment. Chichester, New York, Brisbane, Toronto, Singapore:
John Wiley & Sons Ltd.
Krouse, H., & Coplen, T. (1997). Reporting of relative sulfur isotope-ratio data. Pure and
Applied Chemistry, 69, 293–295.
82
Krupp, E., Seby, F., Martín-Doimeadios, R. R., Holliday, A., Moldován, M.,
Köllensperger, G., et al. (2005). Trace Metal Speciation with ICP-MS Detection.
In S. M. Nelms, Inductively Coupled Plasma Mass Spectrometry Handbook. Boca
Raton , Florida, USA: CRC Press LLC.
Krupp, L. B., Hyman, L. G., Grimson, R., Coyle, P. K., Melville, P., Ahnn, S., Dattwyler,
R. & Chandler, B. (2003). Study and treatment of post Lyme disease (STOPLD): a randomized double masked clinical trial. Neurology 60, 1923–1930.
Mann, J. L., Vocke, R. D., & Kelly, W. R. (2009). Revised delta S-34 reference values
for IAEA sulfur isotope reference materials S-2 and S-3. Rapid Comm. Mass
Spectrom. 23, 1116–1121.
Maréchal, C. N., Télouk, P., & Albarède, F. (1999). Precise analysis of copper and zinc
isotopic compositions by plasma-source mass spectrometry. Chemical Geology,
156, 251–273.
Meier-Augenstein, W. & Kemp, H. F. (2012). Stable Isotope Analysis: Hair and Nails.
Wiley Encyclopedia of Forensic Science.
Meija, J, Yang, L., Mester, Z., & Sturgeon, R. E. (2012). Correction of Instrumental Mass
Discrimination for Isotope Ratio Determination with Multi-Ccollector Inductively
Coupled Plasma Mass Spectrometery. In F. Vanhaecke, & P. Degryse, Isotopic
Analysis: Fundamentals and Applications Using ICP-MS. Wiley-VCH.
Nelms, S. M. (2005). Inductivley Coupled plasma mass spectrometry handbook. Blackwell
Publishing Ltd .
Ono, S., Wing, B., Johnston, D., Farquhar, J. & Rumble, D. (2006). Mass-dependent
fractionation of quadruple stable sulfur isotope system as a new tracer of sulfur
biogeochemical cycles. Geochim. Cosmochim. Acta 70, 2238–2252.
Pavlov, A. A., & Kasting, J. F. (2002). Mass-Independent Fractionation of Sulfur
Isotopes in Archean Sediments: Strong Evidence for an Anoxic Archean
Atmosphere. Astrobiology, 2, 27-41.
Peterson, B. J., & Fry, B. (1987). Stable isotopes in ecosystem studies. Annual Review of
Ecological Systems, 18, 293-320.
Richards, M. P., Fuller, B. T., Sponheimer, M., Robinson, T., & Ayliffe, L. (2003).
Sulphur isotopes in palaeodietary studies: A review and results from a controlled
feeding experiment. International Journal of Osteoarchaeology, 13, 37-45.
83
Robinson, B. W., & Kusakabe, M. (1975). Quantitative preparation of sulfur dioxide, for
sulfur-34/sulfur-32 analyses, from sulfides by combustion with cuprous oxide.
Analytical Chemistry, 47, 1179–1181.
Schauble, E. A. (2004). Applying Stable Isotope Fractionation Theory to New Systems.
Rev. Miner Geochim, 55, 65-111.
Sievert, S. M., & Kuever, J. (2000). Desulfacinum hydrothermale sp. nov., a thermophilic,
sulfate-reducing bacterium from geothermally heated sediments near Milos Island
(Greece). Int J Syst Evol Microbiol, 50, 1239-1246.
Tanner, S. D. (1992). Space charge in ICP-MS: calculation and implications.
Spectrochimica Acta Part B: Atomic Spectroscopy , 47, 809–823.
Taylor, B. E., Ding, T., Halas, S., Breas, O., & Robinson, B.W., (2000). Accurate
calibration of the V-CDT Scale: proposed d34S values for calibration and
reference materials and methods of correction for SO 2 -based analyses. Report
of Sulfur Isotope Working Group 8th Advisory Group Meeting on Future Trends
in Stable Isotope Reference Materials and Laboratory Quality Assurance, IAEA,
Vienna, Austria.
Thode, H. G. (1991). Sulfur Isotopes in Nature and the Environment: An Overview. In
Krouse, H. R., & Grinenko, V. A. Stable isotopes: natural and anthropogenic
sulphur in the environment. John Wiley and Sons Ltd.
Thode, H. G., Cragg, C. B., Hulston,J. R., & Rees, C.E. (1971). Sulfur isotope exchange
between sulfur dioxide and hydrogen sulfide. Geochim. Cosmochim. Acta, 35,
35-45.
Thode, H. G., Monster, J., & Dunford, H. B. (1961). Sulphur isotope geochemistry.
Geochimica et. Cosmochimica Acta 25, 159-174.
Thomas, R. (2008). Practical Guide to ICP-MS A Tutorial for Beginners: Second Edition.
Boca Raton, FL: CRC Press.
Thomas, R. (2013). Practical Guide to ICP-MS A Tutorial for Beginners: Third Edition.
Boca Raton, FL: CRC Press.
Thomas. R. (2001). Spectroscopy, 16, 28-29.
Urey, H. C. (1947). The thermodynamic properties of isotopic substances. Journal Chem.
Soc., 1947, 562-81.
Valkovic V. (1977). Trace elements in human hair, Garland Publishing Inc., USA.
84
Vanhaecke, F., & Kyser, K. (2012). The Isotopic Composition of the Element. In F.
Vanhaecke, & P. Degryse, Isotopic Analysis: Fundamentals and Applications
Using ICP-MS. Wiley-VCH.
Vanhaecke, F., Dams, R., & Vandecasteele, C. (1993). Zone model as an explanation for
signal behavior and non-spectral interferences in inductively coupled plasma
mass spectrometry. Journal of Analytical Atomic Spectrometry, 8, 433–438.
Vogl, J., & Pritzkow, W. (2012). Reference Materials in Isotopic Analysis. In F.
Vanhaecke, & P. Degryse, Isotopic Analysis: Fundamentals and Applications
Using ICP-MS. Wiley-VCH .
Weyer, S., & Schwieters, J. B. (2003). High precision Fe isotope measurements with high
mass resolution MC-ICPMS. International Journal of Mass Spectrometry, 226,
355–368.
Wieser, M. E., & Coplen, T. B. (2010). Atomic weights of the elements 2009 (IUPAC
Technical Report). Pure and Applied Chemistry. 83, 359-396.
Wieser, M. E., & Schwieters, J. B. (2005). The development of multiple collector mass
spectrometry for isotope ratio measurements. Journal of Analytical Atomic
Spectrometry, 242, 97-115.
Wieser, M., Schwieters, J., & Douthitt, C. (2012). Multi-Ccollector Inductively
Coupled Plasma Mass Spectrometery Multi-Collector. In F. Vanhaecke, & P.
Degryse, Isotopic Analysis: Fundamentals and Applications Using ICP-MS.
Wiley-VCH .
Yang, L. (2009). Accurate and precise determination of isotopic ratios by MC-ICP-MS:
A review. Mass Spectrometry Reviews, 28, 990-1011.
Young, E. D., & Galy, A. (2004). The isotope geochemistry and cosmochemistry of
magnesium. Reviews in Mineralogy & Geochemistry, 55, 197-230.
Young, E. D., Galy, A., & Nagahara, H. (2002). Kinetic and equilibrium mass-dependent
isotope fractionation laws in nature and their geochemical and cosmochemical
significance. Geochim Cosmochim Acta., 66, 1095-1104.
Zazzo, A., Monahan, F. J., Moloney, A. P., Green, S., & Schmidt, O. (2011). Sulfur
isotopes in animal hair track distance to sea. Rapid Commun Mass Spectrom, 25,
2371-2378.
85
Appendix
Permission for Figure 2.1
86
87
Permission for Figure 2.3
88
Permission for Figures 3.3 and 3.7
89
Permission for Figure 3.6
90
91
Permission for Figures 3.1 and 3.5
92
Permission for Figures 3.4
93