Computer-based First-principles Kinetic
Model for Aqueous Phase Advanced
Oxidation Processes:
Proof of Reaction Pathway and
Byproducts Formation
Daisuke Minikata, Xin Guo, and
John Crittenden
Brook Byers Institute for Sustainable Systems
School of Civil and Environmental Engineering
Georgia Institute of Technology
1
Various AOPs Technologies 1/2
Proved
Advantages
AOPs
• Long stability and can be
preserved prior to use of
H2O2/
H2O2.
UV
• H2O2+UV → 2HO•
• Suitable for waters with
poor UV light transmission
• Special reactors
H2O2/
requirement for UV
O3
illumination2
• 2O3+H2O2 → 2HO•+3O2
TiO2/
UV
Disadvantages
• Poor UV absorption of H2O2
• Interface of UV with the water matrix
• Special reactors required for UV
illumination
• Residuals of H2O2
• Stripped volatile organics
• Expensive and inefficient to produce O3
• Residues of gaseous O3
• Difficulty of maintenance (O3/H2O2
dosages)
• Low pH is detrimental
• Seems to work with waters
that have poor UV light
• Catalyst fouling
transmission
• Recovery of TiO2 required for slurry
Lamps do not need cleaning
application
TiO2+UV → h++ecb
2
h++H2O → H++HO•
Various AOPs Technologies 2/2
Purifics (UV/TiO2)
0.5 MGD
Photo-Cat Purification
Low Pressure
UV system
(UV/H2O2)
Trojan Tech
HiPOx (O3/H2O2)
Applied Process Technologies, Inc
3
A Computer-based First-principles Kinetic Model in AOPs


Why so important?
 Experimentally determined kinetic models do not fully predict
intermediates and byproducts.
 Unknown rate constants determined by fitting with
experimental observations do not represent mechanisms.
 We now know the general reaction mechanisms that HO•
initiates in the aqueous phase AOPs based on a number of
experimental studies.
 We have a large number of datasets for reaction rate
constants in the aqueous phase AOPs (e.g., HO•).
 Concern about emerging contaminants
 Greater than 70 million chemical compounds registered in
CAS and commercially available
Is it feasible?
 We now have robust tool (e.g., computational chemistry) and
computational resources.
 We can make it feasible!
4
“All models are wrong but some are
useful.”
-- George E.P. Box
“Let’s develop some useful ones and
make good use of them.”
-- an optimistic modeler
5
A Computer-based First-principles Kinetic Model in AOPs

Reaction Pathway
Generator (Graph
theory)
Input of Parent Contaminant as SMILES C(=C(Cl)Cl)(Cl)Cl
Cl2C=CHCl
HO·
Cl·
Cl2C·CH(OH)Cl
Cl2C·CHCl2
O2
-HCl
·
·
Cl2C -CHO
O2


Reaction Rate
Constant
Predictor
(Quantum
mechanical
calculations and
Group Contribution
Method)
Ordinary
Differential
Equations (ODEs)
Generator and
Solver
·
·
OCCl2CHCl2
- Cl·
OO-Cl2C-CHO
C(O)Cl2 + ·CHCl2
·
OCl2C-CHO
·
H 2O
H2O2
HO·
CO2 + CO2·-
Cl2CHCOOH
O2
·
·
O2
CCl2COOH
C(O)Cl2 C(O)Cl2
·
+H2O2 +(OH)CHCl2 + OCHCl2
·
OOCCl2COOH
CO2 + HCl
H2O
HC·(OH)2
OOCH(OH)2
HC(O)Cl
HCOOH + HO2·
CO + HCl
HCOOH
HOOCCOOH
OOCHCl2
CHO +C(O)Cl2
H2O
HOOCCHO
H2O
Cl2CHC(O)Cl
O2
·
Cl(O)C-CHO
+ Cl·
Reaction Pathway Generator
OOCCl2CHCl2
·
·
C(OH)Cl2
OOC(OH)Cl2
HO·
CO2·O2
H2O2
CO2 + O2·- CO2 + HO·
C(O)Cl2 +·HO2
·
Reduction of Reaction Pathways
OCCl2COOH
C(O)Cl2+
·
COOH
ClC(O)COOH
O2
HOOCCOOH
·
OOCOOH
lnk
CO2 + HO2·
Reaction Rate Constant Predictor
dCi/dt = ∑-kijCiCj
ODE Generator and Solver
∆Gactaq,calc
…
C
Validation and
Time-dependent
Accuracy Analysis
Concentration Profiles
t
Toxicity
Estimator
Profile of Relative Toxicity
Flow Conditions in Reactors
Design and Optimization of Processes
Group Contribution Method (GCM)
E
for predicting kHO•
k = Ae RT
a

Essence is same as the GCM for the gaseous phase developed by Dr.
Roger Atkinson (EPA software AOPWIN)
Hypothesis
 Observed experimental reaction rate constant for a given organic
compound is the combined rate of all elementary reactions involving
HO•, which can be estimated using Arrhenius kinetic expression.

The Ea consists of two parts based on Benson’s thermochemical
additivity concept (Benson, 1976):
(1) Base part from main reaction mechanisms (i.e., H-atom
abstraction, HO• addition to alkenes and aromatic compounds
and HO• interaction with S, N, or P-atom-containing compounds).
(2) Functional group contribution partially from neighboring and/or
next nearest neighboring functional group.
7
GCM Results -overall 1/2Species
# of
compounds
calibrated
# of data
within EG (%)
S.D.
from
calibration
# of
compounds
predicted
# of data
within EG (%)
S.D.
from
calibration
(1)
H-atom abstraction
by HO•
Alkanes, Alcohols,
Ethers, Carbonyls,
Aldehydes, Esters,
Carboxyl compounds,
Alkyl halides,
cyclo compounds
84
71 (85%)
0.42
60
35 (58%)
1.1
(2)
HO• addition
to alkenes
Unsaturated alkenes
28
22 (79%)
0.4
0
0
n.a.
(3)
HO• addition to
aromatic compounds
Benzene, Pyridine,
Furan, Imidazole,
Triazine derivatives
120
106 (88%)
0.73
46
33 (72%)
0.53
(4)
HO• interaction with
S, N, P-containing
compounds
• S (Sulfide, Disulfide,
Sulfoxide, Thiol)
• N (Nitrile, Nitro,
Amide, Amine,
Nitroso, Nitramine)
• Phosphorus • Urea
89
58 (74%)
1.5
18
8 (44%)
2.2
310
257 (83%)
0.92
124
76 (62%)
1.2
Reaction
mechanisms
Total
• Error Goal (EG) = 0.5 ≤ kcal/kexp ≤ 2.0
• 66 group rate constants and 80 group contribution factors were
calibrated. The degrees of freedom was 164.
2
1 N
• Sample deviation (S.D.) =


(
k
k
)
/
k
8
 exp,i cal,i exp,i 
N - 1 i 1 
1.E+11
11
10
Ideal fit
1.E+10
1010
kexp/kcal = 0.5 and 2.0
k cal (M-1s-1)
1.E+09
109
H-atom abstraction (Calibrated)
HO addition to alkene (Calibrated)
1.E+08
108
HO interaction with N,S,P (Calibrated)
HO addition to aromatic compounds
(Calibrated)
1.E+07
107
H-atom abstraction (Prediction)
HO interaction with N, S, P (Prediction)
6
10
1.E+06
HO addition to aromatic compounds
(Prediction)
5
10
1.E+05
1.E+05
105
1.E+06
106
1.E+07
107
1.E+08
108
1.E+09
109
k exp (M-1s-1)
1.E+10
1010
1.E+11
1011
Minakata, Li, Westerhoff,
and Crittenden. ES&T
2009, 43, 6220-6227
• 257 rate constants (83% of 310 data) from calibration were within
0.5 ≤ kcalc/kexp ≤ 2.0.
• The sample deviation was 0.92. Degree of Freedom was 164
• 76 rate constants (62% of 124) from prediction were within 0.5 ≤
9
k /k ≤ 2.0.
GCM has been widely used and accepted well for various community
“The wide application range in combination with the user-friendliness
makes it probably the best currently available estimation tool for
OH radical reactions in aqueous solution. Overall, the method of
Minakata et al. is currently the most broadly usable method for the
prediction of OH radical reaction rates in aqueous solution.”
Professor Hartmut Herrmann in ChemPhysChem, 2010, 11, 37963822.
GCM provides Microsoft Excel spreadsheet and Compiled Fortran
Program as Supplemental Materials in Minakata, Li, Westerhoff,
Crittenden, 2009 ES&T 43, 6220-6227, so that any users can
download and calculate the HO· rate constants for a compound of
interest.
We are now working on updating the current GCM
Challenges Still Remain for Rate Constant Predictions
GCM is limited because:
1. Many datasets are required to calibrate parameters
2. Only deal with molecules for which all required group rate constants
and group contribution factors have been calibrated before.
3. Limited number of literature-reported experimental rate constants
for other reaction mechanisms than HO•
4. Assumed that a functional group has approximately the same
interaction properties under a given molecule, so that the GCM
disregards the changes of the functional properties that can arise
from the intramolecular environment by electronic push-pull effects,
hydrogen bond formation, or by steric effects.
 Application of computational chemistry (quantum
mechanical calculations) for predicting reaction rate
constants in aqueous phase
 Looking at reaction energies (transition states)
11
A concept of a Linear Free Energy Relationship (LFER)
 According to a linear free energy relationship (LFER), for the same
reaction mechanism, the difference in the free energy of activation of
transition states I and R is proportional to the difference in free energy
change of the reaction for species I and a reference species R to produce
the products, C and D.
A + R → transition state(s) → C + D
E  E    E  E0 
ln kI - ln kR  -
E
Ea,A*I
RT

A + I → transition state(s) → C’ + D
Ea,A*R
RT
 - m '[ Ea,A*I - Ea,A*R ]
A*I
∆E (∆E ≠)
Reference, A*R
∆E (∆E0)
Reaction coordinate

ln k Ri - ln k R0  -  G

rxn, Ri

rxn, R0
- G

 K a, Ri
 m  ln
 K a, R
0





12
Correlation between Reaction Rate Constants and
Aqueous Phase Free Energy of Activation

ln k  - aGrxn,aq
b
From transition state theory (TST)

Grxn,aq
 Gaq - Greactants,aq
G≠aq is a quasithermodynamic quantity, kcal/mol, that indicates the free
energy of the transition state
Greactants,aq is the molar free energy of reactants, kcal/mol

rxn,aq
G

rxn,gas
 G

rxn,solvation
 G
where


 ,0
0
Grxn,solvation
  Gsolvation
- Gsolvation
 - Greactants,solvation - Greactants,solvation

And ∆G≠aq contains an extra portion of free energy; Gextra = –RT lnγ(T),
where γ(T) is a transmission coefficient that represents the effect of 13
tunneling at temperature T at the transition state
Linear Free Energy Relationships for H-atom abstraction
from a C-H bond by HO·
25
25
8
20
10
y = -0.418x + 21.271
r² = 0.681
20
15
4
12
14
1
6
11
15
9
15
ln k
ln kH
y = -1.386x + 27.723
R² = 0.849
Neutral
Ionized
10
Neutral (G3 + COSMO-RS)-ref.16
10
Ionized (G4 + SMD) - This study
5
5
0
0
0.0
2.0
4.0
6.0
8.0
∆GactI , kcal/mol
10.0
-6
-4
-2
0
2
∆ Gactaq,calc,
based on experimental ∆G≠
4
6
8
10
12
kcal/mol
based on calculated ∆G≠
Calibration (N=26)
13 out of 14 from prediction was within
difference of factor of 5
Minakata and Crittenden, ES&T 2011, 45, 3479-3486
14
Minakata, Song, and Crittenden, ES&T, 2011, 45, 6057-6065
Summary of LFERs for Reaction Rate Constant Predictions
Reaction mechanisms
Genetic representations of
reaction mechanisms
Compound groups
Coefficient for correlation
(lnk =ρΔGactaq,calc + σ)
(N=number of data)
Hydrocarbons (alkanes)
Oxygenated compounds (Alcohols, Diol,
Ether, Ester, Aldehyde, Ketone,
Carboxylic)
Alkyl halides
H-atom abstraction
CHR3 + HO• → •CR 3 + H2 O
HO• addition to alkenes
R2 C=CR'2 + HO• → R 2 (HO)C-•CR'2
Unsaturated alkenes
HO• addition to
aromatic compounds
C6 H5 R + HO• → HO-C6 H5 R
Single functional group
multi-functional groups (2,3,4,5,6)
O2 addition to
C-centered radical
•CR 3 + O2 → •OOCR 3
Alkyl carbon centered radicals
R
HO C O O·
R’
Peroxyl radical
reaction mechanisms
Aromatic carbon centered radicals
ρ = - 0.476
σ = 21.6
N = 56
ρ = - 0.383, σ = 23.2, N = 10
To be obtained
ρ = - 0.063, σ = 20.9, N = 36
ρ = - 0.407, σ = 13.6, N = 9
R
C O + HO2· (O2·- + H+)
R’
R2 CHOO• + •OOCHR'2 → R2 CHOOOOCHR'2
Uni-molecular decay
(Elimination of HO2•-/O2•-)
ρ = - 0.472, σ = 14.5, N = 14
Alkyl peroxyl radical (Aliphatics)
ρ = - 0.451, σ = 22.1, N = 9†
R 2 C=O + R 2 CHOH + O 2
R 2 CHOO-OOCHR 2
2R 2 C=O + H 2 O 2
2R 2 CHO · + O 2
Bi-molecular
To be obtained
R 2 CHOOCHR 2 + O 2
β-scission, 1,2-H shift,
Oxygen-transfer
R-C(O)O• → R• + CO2
CR3 CR'2 O• → •CR 3 + R'2 C=O
HR2 CO• → •C(OH)R 2 + R2 C=O
Alkoxyl radicals
To be obtained
Hydrolysis
R-CHO→R-CH(OH)2 →RC•(OH)2
→R-(OH)2 C-OO•→R-COOH
Aldehyde, ketones
To be obtained
*R, R'=alkyl or H; n.a. = not available; "-" = not clear; a.v.=available, ¶ = unpublished data, † = preliminary investigation
Next Step
Input of Parent Contaminant as SMILES C(=C(Cl)Cl)(Cl)Cl
Cl2C=CHCl
HO·
Cl·
Cl2C·CH(OH)Cl
Cl2C·CHCl2
O2
-HCl
·
Cl2C·-CHO
O2
·
·
OCCl2CHCl2
- Cl·
OO-Cl2C-CHO
C(O)Cl2 + ·CHCl2
·
OCl2C-CHO
·
·
H2O
OOCH(OH)2
HC(O)Cl
HCOOH + HO2·
CO + HCl
HCOOH
HOOCCOOH
HO·
CO2 + CO2·-
Cl2CHCOOH
·
O2
CCl2COOH
C(O)Cl2 C(O)Cl2
·
+H2O2 +(OH)CHCl2 + OCHCl2
HC·(OH)2
·
OOCCl2COOH
CO2 + HCl
O2
HOOCCHO
H2O2
OOCHCl2
CHO +C(O)Cl2
H2O
H 2O
H2O
Cl2CHC(O)Cl
O2
·
Cl(O)C-CHO
+ Cl·
Reaction Pathway Generator
OOCCl2CHCl2
·
·
C(OH)Cl2
OOC(OH)Cl2
HO·
CO2·O2
CO2 + O2
H2O2
·-
CO2 + HO·
C(O)Cl2 +·HO2
·
Reduction of Reaction Pathways
OCCl2COOH
C(O)Cl2+
ClC(O)COOH ·COOH
O2
HOOCCOOH
·
OOCOOH
lnk
CO2 + HO2·
Reaction Rate Constant Predictor
dCi/dt = ∑-kijCiCj
ODE Generator and Solver
∆Gactaq,calc
…
C
Validation and
Time-dependent
Accuracy Analysis
Concentration Profiles
t
Toxicity
Estimator
Profile of Relative Toxicity
Flow Conditions in Reactors
Design and Optimization of Processes
Reaction Pathway Generator
• Graph Theory Approach (Euclid 1735) – Mathematic Abstraction
• Chemical structures are intrinsically graphs that consist of two sets:
a set of nodes or vertices), and a set of edges that connect those
nodes.
Name ID
Valence
Weight
Pointer to the Parent
Pointer Array to the Children
Array of Bond Types
Node
Edge
Logics of pathway generator
H H H
Cl C C C Cl
O H H
H
C
H
H
C
C
Cl H H Cl H O
H
A Chemical Tree
Linear Notation (SMILES)
Reactant Graph
Match Patterns
Products Graph
Reaction
Rules
Products Analysis
Take Unique Products as New Reactants
Reactant/Product relationships
17
Li and Crittenden, 2009 ES&T 43, 2931-2837
General Reaction Rules in AOPs
General reaction mechanisms that HO• initiates based on past
experimental studies
decay
Stefan and Bolton, 1998; 1999; 1999; Stefan et al., 1996; 2000; Cooper et
al., 2009; Li et al., 2004; 2007; von Sonntag and Schuchmann, 1984;
Schuchmann and von Sonntag, 1979
Reduction of Pathway using Directed Relation Graph method
 Directed relation graph (DRG) that represents chemical reaction
mechanisms, in which species are represented by nodes (e.g., A,
B, C) and reactions are represented by arrows (→).
k1
B
A
k2
C
Step 1

k3
D
k4
E
Step 2
Ci = Concentration of species, i
(i = A, B, C…..)
kj = Reaction rate constant for
reaction j (j = 1,2….)
At step1, we calculate direct interaction coefficients (DIC)
that represents an importance of a reaction A→B.
DIC =
k1 × CA
k1 × CA + k2 × CA
 If the DIC is smaller than the criteria that is set by an user,
the reaction mechanism (A→B) will be eliminated and (A→C)
remains. This elimination process is performed incrementally at
every single step.
 For example, the 0.1% of criteria removes reactions that
have < 0.1% of overall reaction rate of reactant of
interest from the full reaction mechanism.
19
Case Study: Acetone
Experimentally Determined Pathway
Important
byproducts
CH3COCH3
HO•
•CH2COCH3
O2
•OOCH2COCH3
•OCH2COCH3
•CH2OOH + CH2CO
O2
HCHO
H2O
HCOOH
HO•
CO2 + H2O + HO2•
•COCH3
O2
CH3COOH
H2O
CH3COOH
HCOOH
HO•
HO•
HOOCCOOH + HCOOH
HO•
HO•
HOOCCOOH + HCOOH
HO•
CO2 + H2O + HO2•
CO2 + H2O + HO2•
20
Stefan, Hoy, Bolton, 1996 ES&T 30, 2382-2390
Graph Theory Predicted Pathway
Important
byproducts
CH3COCH3
HO•
Manually added
reactions
•CH2COCH3
O2
•OOCH2COCH3
•OCH2COCH3
OHCCOCH3
HCHO + •COCH3
H2O
O2
HCOOH
HO•
•CH(OH)COCH3
O2
HOOCCOCH3
•OOCH(OH)COCH3
•OOCOCH3
CO2-•
•OCOCH3
O2/H2O2
HO•
•CH2COCOOH
O2
OHCCOCH3
•OOCH2COCOOH
CO2 + HO2•
•OCH2COCOOH
HCHO + •COCOOH
O2
H2O
OHCCOCOOH
•CH(OH)COCOOH
O2
H2O
H2O
•CH2OOH + CH2CO
H2O
O2
OHCCOCH3 + CH3COCH2OH
HO•
HO•
•CH(OH)COCH3
O2
•CH2COCH2OH
H2O
•OOCH(OH)COCH3
O2
HCOOH
•CH2COOH
O2
•OOCH2COCH2OH
OHCCOCH3
•OOCH2COOH
HOCH2COCH2OH + OHCCOCH2OH
OHCCOCOOH + HOCH2COCOOH
•OCH2COCH2OH
HO•
H2O
HO•
•CH(OH)COCH OH
HO
OHCCOOH + HOCH2COOH
2
2
O2
•CH(OH)COCOOH
O
HOOCCOCOOH
2
•OOCH(OH)COCH2OH
•OOCOCOOH •OOCH(OH)COCOOH
•OOCH(OH)COCOOH
HCOOH
H2O
H2O
OHCCOCH2OH
•OCOCOOH
OHCCOCOOH
OHCCOCOOH
HOOCCOCH2OH
•CH(OH)COCH2OH HOOCCOOH
O2
•OCH2COOH
HO•
HCHO
H2O
•CH(OH)COOH
CO2 + HO2•
O2
HCOOH
•OOCH(OH)COCH2OH
•OOCH(OH)COOH
•COOH + CO2
OHCCOCH2OH
O2
•OOCOOH
CO2 + HO2•
CH3COOH
HO•
OHCCOOH
Type of mechanism
Number of
species
Number of
reactions
Full mechanism
285
3639
Reduced mechanism
60
62
HOOCCOOH
21
Simplified Graph Theory Predicted Pathway
Type of mechanism
Number of
species
Number
of
reactions
Full mechanism
285
3639
Reduced mechanism
60
62
Important
byproducts
CH3COCH3
Manually added
reactions
HO•
•CH2COCH3
O2
•OOCH2COCH3
Beta scission 1, 2 shift
•OCH2COCH3
OHCCOCH3
H2O
HCHO
H2O
•COCH3 HOOCCOCH3
O2
HCOOH •OOCOCH
3
HO•
CO2 + HO2• •OCOCH3
OHCCOCH3 + CH3COCH2OH
•CH2OOH + CH2CO
O2
H2 O
HO•
HCOOH CH3COOH
HO•
HO•
HOOCCOCOOH + HCOOH
H2O
HO•
HOOCCOOH + HCOOH
HO•
CO2 + HO2•
HO•
CO2 + HO2•
22
Reduction of Acetone Degradation Mechanism in UV/H2O2 AOP
Comparison of concentration profiles of major species for experimental data (Stefan,
Hoy, Bolton, 1996 ES&T 30, 2382-2390) and reduced mechanism
1.6
[Major species](mM)
1.4
H2O2/10 exp
Initial concentration of acetone
1.02 mM
acetone exp
Initial concentration of O2
2.2 mM
Initial concentration of H2O2
15.6 mM
acetic acid exp
Initial pH
5.9
H2O2/10 reduced
Wave length of UV
200~300 nm
Light intensity
7.79×10-6
Einstein/L·s
Reactor type
Completely
mixed batch
reactor
Reaction time
80 min
2 × formic acid exp
1.2
oxalic acid exp
1
acetone reduced
0.8
2 × formic acid reduced
0.6
oxalic acid reduced
acetic acid reduced
0.4
0.2
0
0
10
20
30
40
50
60
70
Time(min)
Type of mechanism
Number of
species
Number of
reactions
Full mechanism
285
3639
Reduced mechanism
60
62
The criteria for DRG method is 0.1%.
80
Reaction rate constants are obtained from group contribution
method (Minakata et al., 2009 ES&T 43, 6220-6227) and
literature including:
Nate et al., 1990 J. Phys. Chem. Ref. Data. 19, 413-513.
Nate et al., 1995 J. Phys. Chem. Ref. Data. 25, 7091050.
Buxton et al., 1988 J. Phys. Chem. Ref. Data. 17, 513886.
von Sonntag et al., 1991 Angeuz. Chem. Int. Ed. Engl. 30,
1229-1253.
Li et al., 2009 ES&T 43, 2831-2837.
Stefan et al., 1996 ES&T 30, 2382-2390
The rearrangement and beta-scission of peroxyl radical and ketene hydrolysis were added manually because it is not
included in this version of the pathway generator. (OOCH2COCH3 = •CH2OOH + CH2CO, CH2CO + H2O = CH3COOH)
Reduction of Methane Degradation Mechanism in
UV/H2O2 Process
Comparison of concentration profiles of major species for full mechanism and
reduced mechanism
1.2
CH4 full
[Major Species](mM)
1.1
CH2O full
1.0
CH3OH full
0.9
HCOOH full
CO2 full
0.8
CH4 reduced
Initial concentration of CH4
1.1 mM
Initial concentration of O2
2.2 mM
Initial concentration of
H2 O2
14 mM
0.7
CH2O reduced
Initial pH
7.56
0.6
CH3OH reduced
Wave length of UV
254 nm
Light intensity
7.8×10-6
Einstein/L·s
Reactor type
Completely
mixed batch
reactor
Reaction time
30 min
HCOOH reduced
0.5
CO2 reduced
0.4
0.3
0.2
0.1
0.0
0
2
4
6
8
10 12 14 16 18 20 22 24 26 28 30
Time(min)
Type of mechanism
Number of
species
Number of
reactions
Full mechanism
64
182
Reduced mechanism
20
18
The criteria for DRG method is 0.1%.
Reaction rate constants are obtained from group contribution
method (Minakata et al., 2009 ES&T 43, 6220-6227) and
literature including:
Nate et al., 1990 J. Phys. Chem. Ref. Data. 19, 413-513.
Nate et al., 1995 J. Phys. Chem. Ref. Data. 25, 7091050.
Buxton et al., 1988 J. Phys. Chem. Ref. Data. 17, 513886.
von Sonntag et al., 1991 Angeuz. Chem. Int. Ed. Engl. 30,
1229-1253.
Li et al., 2009 ES&T 43, 2831-2837.
Reduction of Trichloroethene (TCE) Degradation
Mechanism in UV/H2O2 Process
Comparison of concentration profiles of major species for experimental data (Li,
Stefan, Crittenden, 2007 ES&T 41, 1696-1703) and reduced mechanism
TCE exp
Formic acid exp
oxalic acid exp
20 × DCA exp
TCE reduced
formic acid reduced
oxalic acid reduced
20 × DCA reduced
H2O2 exp
H2O2 reduced
[Intermediates](mM)
1.0
0.8
0.6
12.0
Initial concentration of TCE
1.08 mM
10.0
Initial concentration of O2
2.2 mM
Initial concentration of H2O2
10.4 mM
Initial pH
5.9
Wave length of UV
200~300 nm
Light intensity
7.79×10-6
Einstein/L·s
Reactor type
Completely
mixed batch
reactor
Reaction time
30 min
8.0
6.0
0.4
4.0
0.2
2.0
0.0
[H2O2](mM)
1.2
0.0
0
5
10
15
Time(min)
20
25
30
The reason for the inconsistency between experimental and
calculated concentration profiles for formic acid and DCA is that
the pathway generator does not include the photolysis of TCE
and other byproducts.
Type of mechanism
Number of
species
Number of
reactions
Full mechanism
120
344
Reduced mechanism
41
47
The criteria for DRG method is 0.01%.
Reaction rate constants are obtained from group contribution
method (Minakata et al., 2009 ES&T 43, 6220-6227) and
literature including:
Nate et al., 1990 J. Phys. Chem. Ref. Data. 19, 413-513.
Nate et al., 1995 J. Phys. Chem. Ref. Data. 25, 7091050.
Buxton et al., 1988 J. Phys. Chem. Ref. Data. 17, 513886.
von Sonntag et al., 1991 Angeuz. Chem. Int. Ed. Engl. 30,
1229-1253.
Li et al., 2009 ES&T 43, 2831-2837.
Li et al., 2007 ES&T 41, 1696-1703.
Final Step
Input of Parent Contaminant as SMILES C(=C(Cl)Cl)(Cl)Cl
Cl2C=CHCl
HO·
Cl·
Cl2C·CH(OH)Cl
Cl2C·CHCl2
O2
-HCl
·
Cl2C·-CHO
O2
·
·
OCCl2CHCl2
- Cl·
OO-Cl2C-CHO
C(O)Cl2 + ·CHCl2
·
OCl2C-CHO
·
·
H2O
OOCH(OH)2
HC(O)Cl
HCOOH + HO2·
CO + HCl
HCOOH
HOOCCOOH
HO·
CO2 + CO2·-
Cl2CHCOOH
·
O2
CCl2COOH
C(O)Cl2 C(O)Cl2
·
+H2O2 +(OH)CHCl2 + OCHCl2
HC·(OH)2
·
OOCCl2COOH
CO2 + HCl
O2
HOOCCHO
H2O2
OOCHCl2
CHO +C(O)Cl2
H2O
H 2O
H2O
Cl2CHC(O)Cl
O2
·
Cl(O)C-CHO
+ Cl·
Reaction Pathway Generator
OOCCl2CHCl2
·
·
C(OH)Cl2
OOC(OH)Cl2
HO·
CO2·O2
CO2 + O2
H2O2
·-
CO2 + HO·
C(O)Cl2 +·HO2
·
Reduction of Reaction Pathways
OCCl2COOH
C(O)Cl2+
ClC(O)COOH ·COOH
O2
HOOCCOOH
·
OOCOOH
lnk
CO2 + HO2·
Reaction Rate Constant Predictor
dCi/dt = ∑-kijCiCj
ODE Generator and Solver
∆Gactaq,calc
…
C
Validation and
Time-dependent
Accuracy Analysis
Concentration Profiles
t
Toxicity
Estimator
Profile of Relative Toxicity
Flow Conditions in Reactors
Design and Optimization of Processes
Aquatic Toxicity Assessment of AOPs Byproducts
- Relative Toxicity of TCE Degradation Byproducts • 96-hr Green Algae ChV: most sensitive indicator among the acute
and chronic endpoints for fish, daphnia, and green algae (EPA 2005)
• TQ(Toxicity Quotient): {Concentration ÷ 96-hr Green Algae ChV}
∑TQ = Relative toxicity of a mixture
TQ or ∑TQ ≥ 1 : Toxic and unacceptable (Belden et al. 2007)
TCE
Formic acid
Green Algae
96-hr ChV (mg/L)
2.9
83.0
Oxalic acid
1050.0
MCA
DCA
529.0
556.0
*MCA: monochloroacetic acid
**DCA: dichloroacetic acid
Relative Toxicity
(Concentration/ChV)
50
Composite of TCE,
formic acid, oxalic acid,
MCA, and DCA
40
30
20
10
0
0
10
20
Time (min)
30
27
Non-carcinogenic Effect Assessment
- TCE Degradation Byproducts • DWEL (Drinking Water Equivalent Level): A lifetime exposure
concentration protective of adverse non-cancer health effects
from drinking water
• Relative toxicity = ∑[concentration / DWEL]
TCE
MCA
DCA
DWEL
(mg/L)
0.25
0.35
0.14
Relative toxicity
(Concentration/ DWEL)
700
600
Composite of TCE,
MCA, and DCA
500
400
300
200
100
0
0
5
10
15
20
Time (min)
*Oxalic acid and Formic acid are not included in the drinking water criteria.
25
30
28
Carcinogenic Effect Assessment
- TCE Degradation Byproducts -
Chemical
TCE
MCA
DCA
10-4 Cancer Risk
(mg/L)
0.003
0.007
Relative toxicity
(Concentration / cancer risk) x 10000
• Cancer classification
TCE : sufficient evidence in animals and inadequate or no evidence in
human (B2)
MCA : inadequate information to assess carcinogenic potential (I)
DCA : likely to be carcinogenic to humans (L)
• 10-4 Cancer risk concentration:
6
The concentration of a chemical
5
in drinking water corresponding
4
to an excess estimated lifetime
cancer risk of 1 in 10,000. (EPA 2006)
3
2
1
0
0
5
10
15
Time (min)
20
25
29
30
Concluding Remarks
 The computer-based first-principles kinetic model can predict timedependent concentration profiles of parent and major intermediates and
byproducts in consistent with experimental results. (Need to add 1,2 shift
and beta scission and photolysis reactions)
 Simplification of reaction mechanisms predicted by the reaction pathway
generator is required in order to reduce number of predicted species and
reactions.
 The DRG approach has successfully reduced full reaction mechanisms and
concentration profiles of parent and major intermediates and byproducts
in the reduced mechanisms are found almost identical to those that are
predicted by the full reaction mechanisms.
 The current pathway generator needs to include special reactions for
some parent compounds (e.g., photolysis and re-arragement and betascission of peroxyl radicals and ketene hydrolysis) in order to predict
experimentally observed byproducts (e.g., acetate)
 There are challenges for predicting rate constants for bi-molecular decays
of peroxyl radicals. Both experimental and theoretical studies should be
coupled to elucidate reaction mechanisms.
Acknowledgement
• National Science Foundation (NSF): 0607332 and
0854416
• Georgia Tech College of Computing, Office of
Information Technology and CEE IT Services for high
performance computational resources.
• High Tower Chair and Georgia Research Alliance at
Georgia Tech
• Brook Byers Institute for Sustainable Systems
(BBISS)
• University of Notre Dame Radiation Center and
Department of Energy
• Our collaborators: Ke Li (UGA), Stefan Mihaela
(Calgon Carbon Corp.), Steve Mezyk (California State
Univ. Long Beach), and Weihua Song (Fudan Univ.)
31
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33