Modeling the Kinetics of Hydrogen Formation by Zerovalent Iron

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Remediation and Control Technologies

Modeling the Kinetics of Hydrogen Formation by Zerovalent Iron: Effects of Sulfidation on Micro- and Nano-Scale Particles Hejie Qin, Xiaohong Guan, Joel Z Bandstra, Richard L. Johnson, and Paul G. Tratnyek Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b04436 • Publication Date (Web): 01 Nov 2018 Downloaded from http://pubs.acs.org on November 4, 2018

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Environmental Science & Technology

Modeling the Kinetics of Hydrogen Formation by Zerovalent Iron: Effects of Sulfidation on Micro- and Nano-Scale Particles

1 2 3

Hejie Qin1, 2, Xiaohong Guan1, 2#, Joel Z. Bandstra3, Richard L. Johnson4, and Paul G. Tratnyek4*

4 5 6 1

7 8 9 10

2

State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China

Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, P.R. China

11 12 13 14 15 16 17

3

Department of Mathematics, Engineering, and Computer Science, Saint Francis University, P.O. Box 600, Loretto, PA 15940 4

OHSU-PSU School of Public Health, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239

18 19 20 21 22

*Corresponding author: Paul G. Tratnyek Email: [email protected], Phone: 503-346-3431, Fax: 503-346-3427 # Co-corresponding author: Xiaohong Guan Email: [email protected], Phone: +86-21-65980956

23 24 25

Keywords: Hydrogen Evolution Reaction, Corrosion, Passivation, Fe(0), Bimodal kinetics, Global Fitting, Sulfidation

26 27 28

Environmental Science & Tecnology 10/31/18

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ACS Paragon Plus Environment


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Qin | Tratnyek (2018)

30

Abstract

31

The hydrogen evolution reaction (HER)

32

that generates H2 from the reduction of

nZVI

200

H2O by Fe0 is among the most fundamental H2 (µmol)

33

300

Global fitting with: A(1−e−k1t) + B(1−e−k2t) A(1−e−kt)

100

34

of the processes that control reactivity in

35

environmental systems containing

36

zerovalent iron (ZVI). To develop a

37

comprehensive kinetic model for this

38

process, a large and high-resolution data

39

set for HER was measured using five types of ZVI pretreated by acid-washing and/or sulfidation

40

(in pH 7 HEPES buffer). The data were fit to four alternative kinetic models using nonlinear

41

regression analysis applied to the whole data set simultaneously, which allowed some model

42

parameters to be treated globally across multiple experiments. The preferred model uses two

43

independent reactive phases to match the two-stage character of most HER data, with rate

44

constants (k’s) for each phase fitted globally by iron type and phase quantities (S’s) fitted as fully

45

local (independent) parameters. The first, faster stage was attributed to a reactive mineral

46

intermediate (RMI) phase like Fe(OH)2, which may form in all experiments during

47

preequilibration, but is rapidly consumed, leaving the second, slower stage of HER, which is due

48

to reaction of Fe0. In addition to providing a deterministic model to explain the kinetics of HER

49

by ZVI over a wide range of conditions, the results provide an improved quantitative basis for

50

comparing the effects of sulfidation on ZVI.

20

nZVI + S Alfa

10

Alfa + S

0 0

Time (h)

120

51 52

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53

Introduction

54

The formation of H2 from decomposition of water is a process of great importance,1 which is

55

commonly known as the hydrogen evolution reaction (HER), especially when it occurs by

56

heterogeneous catalysis on noble metals like palladium2 or by reduction on readily-oxidized

57

metals like iron.3 In both of these systems, the mechanism of the hydrogen evolution reaction

58

(HER) involves formation of atomic H species at the metal surface (i.e., Hads), followed by

59

combination of the intermediate species and desorption of H2.4, 5 In the case of HER by Fe0, the

60

reduction of water is coupled to oxidation of Fe0, resulting in the overall redox reactions:

61

Fe# + 2H' O → Fe'* + 2OH+ + H'

(1)

62

Fe# + 2H * → Fe'* + H'

(2)

63

under circum-neutral/alkaline or acidic pH conditions, respectively.6, 7 As this reaction proceeds,

64

precipitation of Fe(OH)2 may become favorable, and then Fe(OH)2 may disproportionate to

65

magnetite and dihydrogen according to the Schikorr reaction:7, 8

66

3 Fe(OH)' → Fe1 O2 + H' + 2 H' O

67

if the conditions are suitable for this reaction to be significant.9-11 The sum of these reactions is: 3Fe# + 4H' O → Fe1 O2 + 4H'

68 69 70

(3)

(4)

so the overall stoichiometry of H2 from Fe0 could vary from 1:1 (eq 1 and 2) to 4:3 (eq 4). HER by the above reactions has been studied extensively, mainly due to its importance in

71

corrosion of ferrous metals,12, 13 but more recently because of its relevance to H2 fuel

72

production14, 15 and water-treatment processes that employ Fe0 for removal of contaminants. The

73

most common water-treatment application of Fe0 involves emplacement into the subsurface for

74

remediation of contaminated groundwater,16, 17 but these ZVI permeable reactive barriers (PRBs)

75

can be ineffective if HER results in enough accumulation of gaseous H2 in pore spaces to

76

obstruct groundwater flow.18 This mostly geotechnical concern has motivated several studies of

77

HER under water-treatment conditions,19, 20 but most studies have focused on HER for more

78

chemical-process related reasons. One such reason is that the formation of H2 provides a

79

relatively direct and efficient measure of the reactivity of Fe0—or, conversely, its passivation—

80

in environmental media. A second reason is that the reaction shown in eq 1 can be the 10/31/18


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Qin | Tratnyek (2018) 81

stoichiometrically-dominant reaction during anaerobic corrosion, in which case it will largely

82

control changes in solution and surface chemistry over time. A third reason is that HER

83

consumes ZVI that otherwise might contribute to reduction of contaminants, and this

84

competition is the main issue that limits the overall electron efficiency of (or selectivity for)

85

contaminant reduction during water treatment processes with ZVI.21, 22

86

Most of these chemical-process aspects of HER under conditions relevant to groundwater

87

remediation were first addressed in a series of studies by Reardon et al.11, 23-25 using various types

88

of granular iron, simulated groundwater conditions, and a unique method of measuring and

89

modeling changes in pressure to determine the rate of H2 formation. In these experiments, the

90

dose of Fe0 was high (1 kg L−1) and the length of the experiments was long (up to several

91

months), so dissolution of H2 into the Fe0 was significant. After correction for this effect, the rate

92

of HER showed a characteristic trend of increasing for about 50 h and then gradually declining

93

over about 1 month. The initial increase was attributed to breakdown of the aged, air-formed

94

oxide, passivation layer upon wetting of the materials, and the subsequent decrease was due to

95

accumulation of reaction products (iron oxides and other precipitates, OH−, and H2) that inhibit

96

corrosion of the Fe0 over longer time periods. Combining these processes into a model that fully

97

describes the asymmetric peak in the HER rate data was beyond the scope of Reardon’s studies,

98

but portions of the data were fit to give corrosion rate constants that were used to model the

99

potential accumulation of H2 gas under field-scale conditions.24

100

All of the above work was performed with micron- or larger-sized ZVI, which generally

101

does not become fully oxidized under conditions relevant to water treatment, so depletion of Fe0

102

does not contribute to plateauing in the HER data. In contrast, nano-scale ZVI (nZVI) can be

103

fully oxidized by H2O (eq 1) within days, which can contribute to plateauing in HER vs. time

104

data as the reaction approaches completion and allows calculation of the original Fe0 content of

105

nZVI from the final yield of H2. Both of these effects are of practical interest because they are

106

the converse of the factor that often limits field-scale application of nZVI: its limited capacity

107

and longevity as a reductant of contaminants.22 These issues, and the chemical-process aspects of

108

HER in water treatment applications of (n)ZVI noted above, have motivated a now significant

109

body of work done by measuring accumulation of H2 in the headspace of closed, well-mixed

110

batch reactors containing (dilute) suspensions of nZVI.25-35 Despite the considerable range of

111

conditions employed in these studies, they all report H2 concentration vs. time data that suggest 10/31/18


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gradually decreasing rates of HER, so the kinetics are likely to be controlled by one or more

113

processes that are common across the whole range of these systems. To diagnose the process(es) that control HER by suspensions of (n)ZVI, it would be

114 115

useful to have a kinetic model that can describe the whole range of H2 formation data. Most of

116

the kinetic data that have been reported to date either were not modeled (e.g. 29, 34) or were

117

divided into subsections and fitted as simple (pseudo) zero- or first-order processes.25, 27, 30, 31, 35,

118

36

119

described with simple models, as can be seen in the examples summarized in Figure S1 of the

120

Supporting Information. This tendency suggests additional processes must be considered to fully

121

describe the degree to which H2 production rates decrease with time. One study showed that this

122

could be done by modeling the HER reaction as reversible,30 which causes the apparent rate of

123

HER to decrease as H2 accumulates in the reactor headspace. However, the assumption of HER

124

reversibility is inconsistent with the high temperature and H2 partial pressure conditions that

125

usually are required to make the reverse of eqs 1, 2, or 3 significant.23, 37, 38

126

In most of these cases, there was significantly more curvature in the data than could be

Alternative processes that could account for the characteristic shape of the HER data

127

shown in Figure S1 include inhibition by other effects of corrosion on solution conditions (e.g.,

128

increased pH36) and alterations to the ZVl surface that result in lower reactivity (i.e., passivation

129

by a variety of mechanisms). The evaluation and selection among these possibilities was a major

130

objective of this study, with the overall goal of developing a kinetic model that is (i) flexible

131

enough to describe all of the major aspects of HER kinetics over the whole range of relevant

132

conditions, and yet (ii) deterministic enough to be useful in diagnosing the mechanisms that

133

control the kinetics of HER and related corrosion reactions, including the reduction of

134

contaminants. To ensure the generality of the resulting model, we calibrated it with a new and

135

extensive set of high-resolution kinetic data for HER kinetics by ZVI of five types (ranging from

136

freshly-prepared nZVI to highly-aged commercial micro-sized ZVI) with several types of

137

pretreatments (including acid-washing and sulfidation by three common sulfidation agents).

138

The range of conditions we used in this study were selected to compliment other on-

139

going work on two related and priority aspects of ZVI reactivity: (i) the quantification of

140

selectivity among competing oxidation reactions22, 31, 35, 39 and (ii) the influence of sulfidation on

141

that selectivity.31, 35, 40-42 Most work on the selectivity of ZVI for reduction of water vs. reduction

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of contaminants has quantified electron efficiency from data obtained at a single time point, even

143

though it is expected that electron efficiency will evolve over time. To quantify electron

144

efficiency in a more general way, kinetic models are needed for each competing oxidation

145

reaction, such as the kinetic models for HER developed in this study. The other priority issue is

146

to better understand recent results showing that sulfidation of nZVI can inhibit HER without

147

significantly slowing contaminant reduction, thereby improving the electron efficiency—and

148

potentially the longevity—of nZVI during remediation applications.40, 41 The consequences of

149

this effect of sulfidation could be significant enough to alter the scope of application of other

150

types of ZVI in water treatment, but the kinetic data on HER has been insufficient to make

151

comparisons between different types of ZVI.

152

To determine the kinetic model that is most consistent with (i) the whole range of new

153

and old experimental data, (ii) a reasonable conceptual model for the controlling chemical

154

processes, and (iii) best statistical practices for fitting and selection among complex models, we

155

fit all of the data simultaneously to four alternative kinetic models using global, nonlinear

156

regression analysis. Advantages of global fitting were illustrated in a recent study where we

157

demonstrated that one kinetic model could fully describe a very complex system involving azo

158

dye reduction in aerobic suspensions of ZVI.43 In this study, we extend the use of global fitting

159

analysis even further, by using it to systematically compare alternative models of varying

160

complexity.

161

Experimental

162

Materials. Five types of ZVI were used, including three that are micro-sized (Alfa, Beijing, and

163

Hepure) and two that were nano-sized (Toda and CMC-nZVI). The first four were used as

164

received from the identified source and the fifth was prepared as we have done previously.44

165

Additional details on these materials and methods are given in the SI. Other reagents that were

166

used are reported in the SI. All solutions were prepared with deoxygenated deionized (DO/DI)

167

water in an anaerobic chamber, unless specified otherwise. DO/DI water was prepared by

168

sparging DI water with N2 for at least 0.5 h and left in the anaerobic chamber (100% N2, O2 < 0.8

169

ppm) overnight.

170

Methods. All experiments were performed in well-mixed, anaerobic, batch reactors. In some

171

cases, micro-sized ZVI (mZVI) was acid washed before use. The ZVI was treated with solutions 10/31/18


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containing several concentrations of sulfidation reagent (dithionite, sulfide or thiosulfate) for 12

173

h. In control experiments, 40-mL DO/DI water was used instead of sulfidation reagent. After

174

sulfidation/aging treatment, the ZVI samples were crimp sealed and mixed with 2 mM HEPES

175

buffer (pH 7.0) in 60-mL bottles on a rotator. At each sampling time, 2 mL of nitrogen was

176

injected into the vial, the vial was hand-mixed for 10 s, and then 2 mL of headspace was

177

withdrawn and injected directly into the gas chromatograph to determine H2 concentration in the

178

headspace. The measured H2 concentration was converted to quantity of H2 (as described in the

179

Supporting Information) and a correction was applied for the quantity of H2 lost due to serial

180

sampling. These calculations, and other experimental details, are elaborated in the Supporting

181

Information.

182

Results and Discussion

183

Kinetics and Stoichiometry of Hydrogen Evolution. Figure 1A shows a typical set of

184

concentration vs. time data for H2, dissolved Fe2+, and pH measured using a closed batch-reactor

185

containing one-type of ZVI, with and without pretreatment by acid-washing or sulfidation. For

186

all four treatment combinations, the HEPES buffer was sufficient to prevent any significant

187

changes in pH. However, both H2 and Fe2+ increased along similarly-shaped profiles suggesting

188

a gradual shift from faster to slower reactions controlled by a set of shared rate-controlling

189

process. The coupling between H2 and Fe2+ appearance is shown in Figure 1B and the slope of

190

the segments in these correlations represent the apparent, overall stoichiometry of HER in these

191

experiments. The data suggest two stages: a steeper segment during first 8 hr, followed by a

192

segment of shallower slope until the end of the experiments. Comparison between the four

193

treatment combinations (Figure 1B) reveals that the slope consistently decreased with acid

194

washing (circles vs. squares) and increased with sulfidation (red vs. blue).

195

The later segment in Figure 1B includes slopes 1.1±0.3 and 1.5±0.4, which are consistent

196

with the 1:1 stoichiometry predicted by eqs 1-2. The similarity among these values suggests that

197

HER—on the time scale of 10’s of hours and under the conditions of this study—is

198

predominantly due to conversion of Fe0 to dissolved Fe2+ (i.e. that there is little net precipitation

199

of new iron phases). While the data in Figure 1B are for micro-scale ZVI, which is not

200

significantly dissolved during these experiments, eqs 1-2 should also apply to experiments with

201

nZVI, which can be more completely dissolved. In our previous work with CMC-nZVI,31 the 10/31/18


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stoichiometry of eq 1 was assumed for calculations of Fe0 content from formation of H2 after

203

acidification, but this assumption was not tested or used on the data for CMC-nZVI in this study.

204

Other work with the less-labile Toda nZVI27 assumed that the controlling reaction in closed

205

batch reactors over 100’s of days is eq 3 (i.e. formation of Fe3O4) and used the stoichiometry of

206

this reaction with measurements of H2 to characterize the decrease in Fe0 content of nZVI during

207

aging. The earlier, steeper segment in Figure 1B includes slopes 2.4±0.7 to 6.9±1.0, which are

208 209

considerably greater than can be explained by the stoichiometries of eqs 1-4. This result requires

210

that there be one or more additional mechanisms of producing Fe2+ that are not coupled to H2

211

formation, and the most likely explanation for this is desorption and/or (reductive) dissolution of

212

Fe2+ from preexisting iron oxides on the ZVI. This explanation is consistent with the higher Fe2+

213

concentration and Fe2+/H2 stoichiometry obtained with ZVI that was not pretreated by acid

214

washing (circles in Figure 1B), because acid-washing is well known to remove labile iron

215

oxides.45, 46 The effect of sulfidation on these data (blue markers in Figure 1B) is likely not due

216

to increased release of Fe2+, but rather decreased formation of H2, as expected from prior work31,

217

42

and discussed further below. w/ acid wash 40 7

25

6 H2 (+AW, −S) H2 (+AW, +S) Fe2+ (+AW, −S) Fe2+ (+AW, +S) pH (+AW, −S) pH (+AW, +S)

20 15

5 4

10

2 0

222 223

1.1±0.3

20 5.7±0.5 5.1± 0.3 −AW −S −AW +S +AW −S +AW +S

20

40

60

80

100

120

2.4±0.7 0 0

2

4

6

8

10

12

14

16

H2 (µmol)

Time (h)

218

221

6.9±1.0

10

0

220

1.5±0.4

3

5

219

B

30

Fe2+ (µmol)

30

pH

H2 or Fe2+ (µmol)

A

Figure 1. Representative time series data for Alfa ZVI with/without pretreatment by acid washing or sulfidation with sulfide. (A) Total H2 and Fe2+ (left axis) and pH (right axis) vs. time. (B) Stoichiometry of Fe2+ vs. H2. Reaction conditions: [ZVI] = 5.0 g/L, DIW with 2 mM HEPES with pHinit = 7.0, anoxic, room temperature. Solid lines in B are from regression of pooled data for the slow and fast portions of HER, and the slopes of these lines are annotated on the figure.

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The break in slope of Fe2+, H2, and Fe2+/H2 vs. time data shown in Figure 1A is too sharp

225

to be consistent with simple first-order appearance kinetics (Figure S3), but rather suggests a

226

transition from faster to slower HER due to a change in the rate controlling process(es). A

227

similar transition is evident in the data for all four treatment combinations shown in Figure 1,

228

most other treatment combinations shown in Figure S3-S6, and in much of the data on HER by

229

ZVI in batch systems that has been published previously (Figure S1). This transition could arise

230

from a variety of mechanisms, including the reversibility hypothesis that was discussed and

231

deemed unlikely in the introduction. Another possibility is that corrosion during the

232

preequilibration stage used in this study could result in accumulation of H2 within the ZVI,

233

which is released after replacement of the aqueous phase with fresh buffer. Desorption of H2

234

from ZVI has been observed in experiments done at higher pressures and over longer time

235

periods,47 but does not appear to have been significant under the conditions of this study.

236

Evidence for this conclusion includes the results shown in Figures S5 and S6, which do not

237

show the initial increase in H2 that would be expected if desorption of pre-formed H2 were

238

significant. A third possibility is that corrosion (by eqs 1-2) causes an increase in pH that

239

supressed the rate of further HER.27, 36 This effect is unlikely to be significant in well-buffered

240

systems—such as those used in this study (Figure 1A)—but it might contribute under other

241

conditions.

242

A fourth possible explanation for the characteristic shape observed in most HER data

243

involves replacement of more reactive surface phase by less reactive surface phase. A plausible

244

candidate for this transient reactive phase is amorphous ferrous oxyhydroxide that forms during

245

the preequilibration period, but then is consumed during the early fast stage of HER after the

246

aqueous phase was replaced by fresh buffer. In this study, no attempt was made to directly

247

characterize this transient phase—by electron microscopy or surface spectroscopy—but evidence

248

is available from other studies that Fe(OH)2 forms on ZVI under relevant conditions.7, 11, 48 That

249

Fe(OH)2 could contribute significantly to HER (by eq 3) was confirmed by control experiments

250

with freshly-synthesized Fe(OH)2, which are described in the SI (Figure S7). A transient,

251

reactive species like Fe(OH)2 is assumed in the modeling described below, but its exact

252

composition need not be specified for modeling the kinetics.

253

Modeling the Kinetics of Hydrogen Evolution. To accomplish the overall goal of obtaining an

254

optimal and general kinetic model for HER, we measured a large set of H2 vs time data using 5 10/31/18


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types of ZVI (at appropriate doses: 5 g/L for mZVI and 0.5 g/L nZVI), two primary treatment

256

variables (with and without acid washing or sulfidation), and two secondary treatment variables

257

(different types and doses of sulfidation agent), but otherwise consistent experimental variables

258

(buffer and pH, reactor configuration, mixing, etc.). All of these new time series data are shown

259

in Figures S8-S12, and a representative selection of the results is given in Figure 2. The range

260

of results seen in this new dataset covers the diversity of HER kinetics seen in previously

261

published data (Figures 1, S1, S3-S6), but the new data are more suitable for global fitting

262

because all of the experimental variables are systematically arranged and controlled. The

263

example results in Figure 2 show the global fit to our preferred kinetic model, which treats some

264

of the model parameters as optimized independently for all experiments (local variables), and

265

other parameters fitted to a single value for groups of experiments (global variables). The

266

agreement between the model and data in Figure 2 appears satisfactory, but other kinetic models

267

and combinations of local and global variables were considered, and four representative and

268

significant cases are discussed below. Alfa +0 µM Alfa +20 µM S Alfa +3000 µM S Model 4 Fits

14 12

120

A

100 80

H2 (µmol)

H2 (µmol)

10 8 6

0

0 0

269

272 273

60

20

2

271

B

40

4

270

Toda +0 µM Toda +3000 µM S Toda +9000 µM S Toda +18000 µM S Model 4 Fits

20

40

60 80 Time (h)

100

120

0

20

40

60 80 Time (h)

100

120

Figure 2. Representative time series data and fits to Model 4 (Global). (A) Alfa and (B) Toda, with sulfidation using sulfide at several doses. Markers show average and average deviation from duplicate experiments. Dashed curves are from global fitting.

The four kinetic models for HER were selected based on prior work, most of which is

274

summarized in the introduction, and derivations from physico-chemical considerations, which

275

are presented in Supporting Information. In this study, all the model derivations are based on the

276

hypothesis that the decrease in rate of HER is caused by a first-order decrease in quantity of Fe0

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available for reaction with H2O (S, eq 5) and the overall rate law for HER is assumed to be

278

pseudo-first order in S (eq 6). 45

279

46

= −𝑘 • 𝑆

4[>? ]

280

46

(5)

= 𝑘>? • 𝑆

(6)

281

where kH2 is the hydrogen evolution rate constant, k is the decay of reactive phase, and S0 is the

282

initial surface of S. The subscripts (1 or 2) indicate the parameters corresponding to Phase 1 or 2,

283

respectively. In Model 1, it was assumed that one reactive phase is responsible for HER, whereas the

284 285

other three models reflect the hypothesis that HER arises from reaction of two phases on the

286

ZVI. In Model 2, it was assumed that the more reactive phase produces the initial fast stage of

287

HER, a less reactive phase gives the slower stage of HER, and Phase 1 is replaced by Phase 2

288

according to a first-order rate law. Model 3 is similar to Model 2, but includes decay of Phase 2

289

to products that do not contribute to HER. Model 4 assumes the two phases act independently

290

(e.g., as Fe0 and FeII sites on ZVI with pits in an oxide coating49), the quantities of which decay

291

by independent first order processes (using the same formulation as eq 5). Therefore, for Phase 1

292

and 2,

293

𝑆B = 𝑆B,# e+DE6

(7)

294

𝑆' = 𝑆',# e+D?6

(8)

295

The rate law for HER was assumed to be pseudo-first order in S1 and S2 (eq 9). The composition

296

of the phases was assumed to be constant for each ZVI type across all of the conditions tested,

297

with the differences in observed HER kinetics being due to changes in the quantities of the two

298

reactive phases (S1, and S2). Therefore, 4[>? ]

299

46

= 𝑘>?,B • 𝑆B + 𝑘>?,' • 𝑆'

(9)

300

Combining and solving these terms gives equations for Model 4. The six physical parameters (k1,

301

k2, S1, S2, kH2,1, kH2,2) were arranged into four fitting parameters (k1, A, k2, B), so the model could

302

be fit using eq 10, with A and B defined by eqs 11-12. [H' ] = 𝐴 • (1 − e+DE6 ) + 𝐵 • (1 − e+D?6 )

303

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11


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𝐴=

305

𝐵=

DL? ,E •5E,M DE

DL? ,? •5?,M D?

(11)

(12)

Major characteristics of the models above are summarized in Table 1, and further

306 307

qualitative discussion of their relevance is given below. For Models 2 and 3, fitting all the

308

parameters independently resulted in large uncertainties despite the constraint provided by

309

globally fitting the whole dataset. This was due to covariance among some combinations of

310

parameters, which results in χ2 minima that follow contours between the parameter combinations

311

rather than defining unique optimal values of the individual parameters. Much like we did in a

312

previous study,50 this challenge was overcome by fitting combinations of related parameters. The

313

combinations that gave the best results are shown in Table 1 and Figures S13-S18.

314

Table 1. Summary of kinetic models for HER.

315

1

316

2

317 318 319

Model

Format1

Parameters2

Statistics3

1. Uniform passivation

Single first-order exponential appearance term (eq S7)

k, A

45 (243)

Misses curvature due to stage transition. (Figure S8)

2. Replacement of one reactive phase with another

Sum of a zero-order linear appearance term and a first-order exponential appearance term (eq S14)

k S1,0•kH2,2 kH2,1/kH2,2

7.2 (238)

Fits transition between stages, but misses the curvature in Stage 2. No failures. (Figure S9)

3. Advanced phase replacement model

Sum of three first-order exponential appearance terms which share some parameters (eq S18)

k1, k2 S1,0•kH2,2 kH2,1/kH2,2

5.3 (233)

Fits transition and curvatures in both stages. No failures. (Figure S10)

4. Independent changes in two reactive phases

Sum of two independent first-order exponential appearance terms (eq 7 or S24)

k1, A, k2, B

L: 1.6 (207) G: 3.8 (211)

Fits transition and curvatures in both stages. No failures. (Figure S11-S12)

Results

Equation numbers refer to the model descriptions in Supporting Information. For the minimally-global fits, parameters in bold were grouped by iron type. 3 Reduced chi-square = total chi-square/(degrees of freedom). Degrees of freedom (number of points – number of fitted parameters) given in parenthesis. For model 4, the minimally-global fit is L, the more fully global fit is G.

320

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Once the kinetic models were selected, they were fitted to the whole HER dataset,

322

initially with all model parameters fully local (i.e., independently optimized to the results of each

323

experimental time course), and then using those results for initial guesses while fitting with

324

various combinations of variables treated as global (i.e., optimized to a common value across

325

multiple experiments). The fitting results were evaluated based on (i) visual comparison of the

326

data and fits (e.g., Figures S8-S12), (ii) inspection of the uncertainties in each fitting parameter

327

(e.g., S13-S18), and (iii) the overall reduced chi-square (Tables S1). For all four models, a

328

baseline best fit was chosen that is “minimally global” in that parameters were grouped only

329

where this resulted in a large decrease in degrees of freedom and is strongly supported by

330

physico-chemical considerations. In most cases, this compromise resulted in globally fitting

331

some of the model parameters by iron type and fitting the other parameter(s) as local. The exact

332

assigment of global vs. local variables in all of the reported fits can be determined from Figures

333

S13-S18 and Tables S2-S6. Model 1 does not appear to have been used previously, but a first order dependence on S

334 335

is embedded in the model for HER kinetics described by Liu and Lowry.27 In this study, fitting

336

Model 1 to our whole dataset—using the minimally-global constraint that k should be constant

337

for each type of Fe0—also did not fully capture the shape (bimodal character) or degree of

338

curvature in our HER data (Figure S8). The inability of Model 1 to describe the bimodal

339

curvature of the data for nZVI, especially CMC-nZVI, is clearly evident in the H2 vs time plots

340

(Figure S8) and consistent with the relatively large reduced chi-square value (Table 1).

341

However, the systematic lack of fit to Model 1 is not reflected in the fitting coefficient

342

uncertainties, which appear to be uniformly acceptable across the whole data set (Figures 3 and

343

S13).

344

For Model 2, these constraints were implemented by fitting k and kH2,1/kH2,2 as global

345

within each iron type and S1,0•kH2,2 as local to each experimental condition, based on

346

considerations described in the Supporting Information. In general, Model 2 describes the data

347

well, including the overall degree of curvature and the position and sharpness of the break in

348

slope that divides the first and second stages (Figure S9), with acceptable uncertainties in all the

349

fitting parameters (Figure S14). Despite having more parameters than Model 1, Model 2 had

350

fewer degrees of freedom due to fitting the parameters in three groups with one local and two

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global by iron type, which resulted in a greatly improved reduced chi-square (Table 1).

352

However, most of the fits slightly underrepresent the degree of curvature in stage 2 (esp. for

353

nZVI) and the model is too constrained to represent the intersecting time-courses for the few

354

treatment combinations that exhibit this complication (e.g., Beijing ZVI + sulfide, Beijing +

355

dithionite, and Toda + sulfide). Most of these differences between the model fits and the data can

356

be explained by the lack of a mechanism to decrease the rate of HER during stage 2, which is

357

included in Model 3.

358

For model 3, as with Model 2, the composition of the two phases was assumed to be

359

constant for each ZVI type across all of the conditions tested, with the differences in observed

360

HER kinetics being due to changes in the quantities of the two reactive phases (S1, and S2).

361

Therefore, k1, k2, and kH2,1/kH2,2 were fit as global within each iron type and only S1,0•kH2,2 was

362

fit as local to each experimental condition. As expected, Model 3 fits the curvature in stage 2

363

better than Model 2, but it does not reproduce the crossing of time-courses in the three treatment

364

combinations noted above (Figure S10). The uncertainties in the fitting parameters for Model 3

365

are similar to those of Model 2, except for Beijing and Hepure ZVI where k2 is poorly defined

366

because there was not enough curvature during stage 2 of HER (Figure S15). Compared with

367

Model 2, Model 3 has one more (global) fitting parameter, slightly fewer degrees of freedom,

368

and a slightly smaller reduced chi-square (Table 1). Since the overall advantages of Model 3 are

369

modest, it was concluded that they do not justify its greater mathematical complexity and

370

potential for overparameterization (especially with smaller data sets).

371

For Model 4, the intial fitting was performed with the same arrangement of global and

372

local parameters as the previous models (global k’s and local S’s, designated 4L), and the results

373

are shown in Figures S11 and S16. All features in the dataset are fully described when the data

374

were fit this way—including the crossing of time-courses in the three treatment combinations

375

that were outliers above—and the reduced chi-square is the smallest of all the models tested in

376

this study. However, the fitting parameters for Beijing ZVI are poorly defined (best seen in the

377

large RSDs in Figure S16), again because there was not enough curvature in the data during

378

stage 2 (as noted for Model 3). Furthermore, the nearly perfect fit to the outlier time-courses that

379

challenged the previous models may reflect unwarranted statistical flexibility in Model 4L,

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because the model does not contain any new physical process(es) that would explain these

381

results.

382

Considering the overall success and specific limitations of Model 4L, we tried further

383

constraining the fitting such that k1 was global across the whole data set (designated Model 4G).

384

The main reason for this choice was to test the hypothesis that the reactive phase during stage 1

385

might have the same rate constant for HER because it was the same material, formed in situ

386

under conditions common to all the experiments in this dataset. The resulting fits (Figures S12

387

and S17) describe the full range of results nearly as well as Model 4L, but slightly less well for

388

the more extreme outlier cases (esp. Toda with 0-3000 uM sulfide) and with a reduced chi-square

389

between that of Models 3 and 4L (Table 1). The most significant aspect of this result is

390

verification that the rate of HER during stage 1 can be described with a single rate constant,

391

which is consistent with this phase being controlled by a common species like Fe(OH)2. In

392

addition, the surprisingly small uncertainty in the global value of k1 seems to have constrained

393

the fitting in a way that produces a more balanced distribution of relative standard deviations for

394

the other fitting parameters (cf. Figures S16 and S17). Considering the diversity of materials

395

and conditions included in the dataset for this study, the results obtained with Model 4G should

396

be robust, however less controlled conditions could result in more diverse results during stage 1.

397

Interpretation of the Modeling Results. To further evaluate the alternative models included in

398

this study, and to advance the interpretation of the modeling results with respect to the physico-

399

chemical processes controlling HER, we performed correlation analysis on the fitting parameters

400

between models or within each model (Figure S18). Many of the fitting parameters involving

401

quantities of reactive phases (S0, S1,0, or S2,0) show strong correlations between the models (red

402

markers in Figure S18), so we rearranged the results for models 2 and 3 into the form kH2•S0/k

403

for correlation analysis versus the similarly-defined parameters A and B in Models 1 and 4

404

(Figure S19).

405

There are strong correlations between kH2•S0/k for different models but equivalent stages

406

(i.e., stage 1 vs 1 and 2 vs 2), all of which are shown with color backgrounds in Figure S19, and

407

two representative examples are shown in Figure 3. For the correlations between Models 1, 2,

408

and 3 (yellow background, Figure 3A), the data for each iron type fall almost exactly on the 1:1

409

contours because the parameterization of these models is very similar. The correlations involving

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Model 4L or 4G (pink background, Figure 3B) exhibit moderately strong but variable degrees of

411

clustering along the 1:1 contours because the independence of the two stages in Model 4 allows

412

variance in the data to partition freely between the stage parameters. In addition to differences in

413

the strength of these correlations, these data form diagonal clusters that distribute along different

414

1:1 contours for the different ZVIs. Beijing and Hepure deviate from 1x only because—as noted

415

above—the lack of curvature in Stage 2 for these two ZVIs results in non-unique minima for the

416

Phase 2 fitting parameters. Despite the seperation of some data along different 1:1 contours, the

417

overall trend across all the kH2•S0/k correlations (Figure 3 and S19) suggests clusters by iron

418

type that distribute from lower-left to upper-right in the order Alfa < Beijing < Hepure < Toda ~

419

CMC-nZVI, which corresponds to increasing overall rate of HER. 10

A

10

5

4

B

10 x

Model 3 (kH2,2•S1,0/k2)

Model 2 (kH2,2•S1,0/k)

6 4 2

1x

1 6 4 2

10

4

100x 10 10 10

3

2

1x

1

0.1 6

10 6

422 423 424 425

426

2

4 6

1

2

4 6

10

2

2

Model 3 (kH2,2•S1,0/k1)

420 421

0.1

0 4

6 8

10

2

4

6 8

100

2

4

Model 4L (kH2,2•S2,0/k2 (B))

Figure 3. Representative correlations between kH2•S0/k (μmol) for combinations of stage and model. (A) Model 2 vs. Model 3; and (B) Model 3 vs. Model 4L for stage 2. Parts A and B correspond to Figure S19.08 and S19.20, respectively. Marker color represents iron type, as defined in Figure S19. Dose of the sulfidation agent is represented by markers: triangles = none, squares = low, diamonds = medium, circles = high. Sulfidation agent type is not shown.

Using the preferred model (Model 4), the results obtained for A and B (kH2•S0/k for stage

427

and phase 1 and 2, respectively) can be used to compare the influence of treatments on HER

428

kinetics. In Figures S16-S17, sulfidated materials usually gave lower A and B values, consistent

429

with slower overall HER due to this treatment. The fitted values of A and B also related to the

430

limiting value of H2 generation for each stage (e.g., as t → ∞, eq 10 becomes [H2] = A + B), so,

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sulfidation evidently decreased the capacity for H2 production. These trends are consistent with,

432

but not as easily identified in, the original time series data such as in Figure 2. Other treatment

433

factors (sulfidation agent, concentration, and/or acid washing) do not show strong effects on the

434

time series data (Figures S11-S12) or the fitting coefficient values (Figures S16-S17). These

435

results are consistent with previous work showing that different sulfidation agents have similar

436

effects,42 but suggest that sulfidation might be effective at doses lower than has been used in

437

most previous work. In addition, sulfide at high concentration (3 mM) gave less inhibition of

438

HER unless is was previously acid washed, suggesting that the benefit of sulfidation by sulfide

439

might be limited by its alkalinity, which might be an advantage of using dithionite and

440

thiosulfate to passivate ZVI with respect to HER.

441

Since A and B include information about both the quantity and rate of reaction of the two

442

phases, it may be possible to simulate the whole range of observed HER time-series shapes using

443

only these two parameters. Figure 4 shows simulations using Model 4 and representative values

444

of the model parameters starting from the fitted values in Table S5. The shapes of the time-series

445

simulations in Figure 4 covers the whole range of results seen in this study and in prior work

446

(Figures S1). In Figure 4, the shapes have been subjectively classified into sharp, medium, and

447

smooth, based on the acuteness of the transition between stages 1 and 2 of the reaction.

448

Comparison of the simulation results included in Figure 4 also reveals an interesting

449

characteristic of this model: the overall shape of the HER curves is not controlled just by A and

450

B, but it also depends on A/B. Relating this characteristic of the model to mechanistic aspects of

451

HER during corrosion of ZVI is one example of how this model might be useful in future work.

452

Model 4 and the simulations in Figure 4 may also be applicable to other cases where appearance

453

kinetics is controlled by two phases with limited capacity (e.g., desorption in multiphase systems

454

subject to aging or fouling, or biotransformation in mixed cultures of competing degraders).

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25

Sharp

(A, B, A/B) 15, 15, 1.0 1.6, 2.8, 0.57 9, 20, 0.45

Medium 1, 2, 0.5 1.8, 7, 0.26 4, 23, 0.17

H2 (arbitrary scale)

Smooth 0.5, 1.8, 0.28 2, 13, 0.15 2.2, 20, 0.11

20

15

10

5

0 0

20

40

456 457 458

60

80

100

120

Time (h)

455

Figure 4. Simulations using Model 4, values of A and B in the legend, and representative values of k from this study.

While Model 4 accurately describes HER by ZVI over a considerable range of

459

conditions, and is suitable for a variety of diagnostic and predictive applications, there are

460

several possible limitations to the model that cannot be fully addressed due to limitations in the

461

scope of this study. First, the time scale for the collected data was limited to several days, so

462

prediction of HER kinetics, or capacity over longer time periods, or to complete consumption of

463

Fe0, would involve extrapolation beyond the conditions for which the model has been validated.

464

Second, all of the fitted data were obtained using buffered media where pH changes were

465

negligible. Clearly, corrosion of Fe0 in less-well buffered systems favors increased pH, which

466

should inhibit the primary HER reactions (eqs 1-2), and this possible feedback was not

467

incorporated in the models used in this study. Third, this study—like most prior studies of HER

468

kinetics by ZVI for environmental applications—was done using well-mixed batch reactors, and

469

a variety of other processes (spatial gradients, gas bubble formation, etc.) might become

470

important under column or field conditions.

471

Implications for Water Treatment. The evolution of H2 from reduction of H2O is a prominent

472

aspect of the corrosion of Fe0, whereas abiotic “geochemical” routes of H2 formation are less

473

favorable or well characterized.51 This study shows that the overall kinetics of HER in batch

474

reactors containing ZVI exhibit an early stage of fast H2 accumulation, which probably is the

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result of an authigenic, secondary, and high-reactive phase that is derived from Fe0. This phase is

476

likely to consist of Fe(OH)2 or related Fe(II) oxyhydroxides, which are weaker reductants than

477

Fe0 thermodynamically, but can be more effective kinetically due to their high and labile reactive

478

surface area. Such iron-based “reactive mineral intermediate” phases (RMIs) play a prominent

479

role in determining the structure and reactivity of oxide passive films during corrosion52-54 and a

480

wide range of biogeochemical processes (e.g.,55-59). The possibility that RMIs contribute to in

481

situ contaminant reduction has been investigated using green rust,60 FeS,61 Fe3O4,62 and most

482

recently Fe(OH)2.62 The latter is particularly relevant to this study because it provides speciation

483

modeling and spectroscopic evidence that active formation of Fe(OH)2 is required for TCE

484

reduction by magnetite.62

485

This study was able to resolve and model the contribution of RMIs to HER because the

486

experimental conditions (preequilibrated closed batch reactors, etc.) allowed decay of the RMI

487

phase responsible for the initial rapid stage of HER, leaving the slower but more sustained HER

488

by Fe0. It is likely that this transition from initial-phase (RMI) to second-phase (Fe0) controlled

489

kinetics also applies to other reactions, and this undoubtedly is one reason for the deviations

490

from first-order disappearance of contaminants seen in many studies of contaminant reduction by

491

Fe0 in batch reactors.63 Under column or field conditions, the processes that form and consume

492

RMIs may become balanced, resulting in steady-state conditions where measured reaction rates

493

(HER, contaminant reduction, iron dissolution, etc.) are the sum of the contributions by all

494

reactive phases. However, columns and field sites might still exhibit zones of different reactivity

495

when the reactive phases are non-uniformly distributed along the flow path.

496

Finally, while this study demonstrates that measurements of H2 formation can be an

497

efficient and powerful approach to characterizing the reactivity of ZVI, we did not investigate

498

whether rates of contaminant reduction follow the same trends. So, for example, while this work

499

confirms that HER is increased by acid-washing and decreased by sulfidation, it does not address

500

whether these treatments have more or less effect on the selectivity of ZVI for HER vs.

501

contaminant reduction.

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502

Acknowledgements

503

This material is based on work supported by the Strategic Environmental Research and

504

Development Program of the U.S. Department of Defense, Award Numbers ER-2308, ER-2620,

505

and ER-2621. The author Hejie Qin thanks the support from the program of China Scholarships

506

Council, and National Natural Science Foundation of China (Grants 21777117, 21522704, and

507

51478329). This report has not been subject to review by any sponsor and therefore does not

508

necessarily reflect agency views and no official endorsements should be inferred. Miranda J.

509

Bradley contributed the surface area measurements.

510

Supporting Information Available:

511

Supporting Information is available free of charge at the ACS Publications website (DOI:

512

10.1021/acs.est.xxxxxxx), including: method details, model derivations, primary data and fitting

513

results, and fitting coefficient values.

514

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515

References

516

1.

Lubitz, W.; Tumas, W., Hydrogen (100th Thematic Issue). American Chemical Society: Chemical Reviews, 2007; Vol. 107 (10), p 3899-4435.

2.

Lasia, A., Hydrogen evolution reaction. In Handbook of Fuel Cells, John Wiley & Sons, Ltd: 2010.

3.

Safizadeh, F.; Ghali, E.; Houlachi, G., Electrocatalysis developments for hydrogen evolution reaction in alkaline solutions – A Review. Int. J. Hydrogen Energy 2015, 40, (1), 256-274; DOI 10.1016/j.ijhydene.2014.10.109.

4.

Bockris, J. O. M.; Khan, S. U. M., Surface Electrochemistry. A Molecular Level Approach. Plenum: New York, 1993; p 1014.

5.

Angelo, A. C. D., Electrocatalysis of hydrogen evolution reaction on Pt electrode surfacemodified by S−2 chemisorption. Int. J. Hydrogen Energy 2007, 32, (5), 542-547.

6.

Odziemkowski, M. S.; Simpraga, R. P., Distribution of oxides on iron materials used for remediation of organic groundwater contaminants - Implications for hydrogen evolution reaction. Can. J. Chem. 2004, 82, (10), 1495-1506; DOI 10.1139/V04-120.

7.

Filip, J.; Karlický, F.; Marušák, Z.; Lazar, P.; Černík, M.; Otyepka, M.; Zbořil, R., Anaerobic reaction of nanoscale zerovalent iron with water: Mechanism and kinetics. J. Phys. Chem. C 2014, 118, (25), 13817-13825; DOI 10.1021/jp501846f.

8.

Beverskog, B.; Puigdomenech, I., Revised pourbaix diagrams for iron at 25–300 °C. Corros. Sci. 1996, 38, (12), 2121-2135; DOI 10.1016/S0010-938X(96)00067-4.

9.

Shipko, F. J.; Douglas, D. L., Stability of ferrous hydroxide precipitates. J. Phys. Chem. 1956, 60, 1519-1523.

10.

Schrauzer, G. N.; Guth, T. D., Hydrogen evolving systems. 1. The formation of H2 from aqueous suspensions of Fe(OH)2 and reactions with reducible substrates, includiing molecular nitrogen. J. Am. Chem. Soc. 1976, 98, (12), 3508-3513; DOI 10.1021/ja00428a019.

11.

Odziemkowski, M. S.; Schuhmacher, T. T.; Gillham, R. W.; Reardon, E. J., Mechanism of oxide film formation on iron in simulating groundwater solutions: Raman spectroscopic studies. Corros. Sci. 1998, 40, (2-3), 371-389; DOI 10.1016/S0010-938X(97)00141-8.

12.

Mizuno, T.; Enyo, M., Sorption of hydrogen on and in hydrogen-absorbing metals in electrochemical environments. Modern Aspects of Electrochemistry 1996, 30, 415-503.

13.

Vigdorovich, V. I.; Tsygankova, L. E., Various forms of adsorbed hydrogen and their role in the hydrogen evolution reaction and hydrogenation of metallic phases in electrolyte solutions. Prot. Met. 2007, 43, (7), 628-636; DOI 10.1134/S003317320707003X.

517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548

10/31/18


21


Page 22 of 26


14.

Azad, A.-M.; Kesavan, S.; Al-Batty, S., A closed-loop proposal for hydrogen generation using steel waste and a prototype solar concentrator. Int. J. Energy Res. 2009, 33, (5), 481498.

15.

Chen, K.-F.; Li, S.; Zhang, W.-X., Renewable hydrogen generation by bimetallic zero valent iron nanoparticles. Chem. Eng. J. 2011, 170, (2–3), 562-567; DOI 10.1016/j.cej.2010.12.019.

16.

Tratnyek, P. G.; Scherer, M. M.; Johnson, T. J.; Matheson, L. J., Permeable reactive barriers of iron and other zero-valent metals. In Chemical Degradation Methods for Wastes and Pollutants: Environmental and Industrial Applications, Tarr, M. A., Ed. Marcel Dekker: New York, 2003; pp 371-421.

17.

Tratnyek, P. G.; Johnson, R. L.; Lowry Gregory, V.; Brown, R. A., In situ chemical reduction for source zone remediation. In Chlorinated Solvent Source Zone Remediation, Kueper, B. H.; Stroo, H. F.; Vogel, C. M.; Ward, C. H., Eds. Springer: New York, 2014; Vol. 7, pp 307-351.

18.

Ruhl, A. S.; Jekel, M., Degassing, gas retention and release in Fe(0) permeable reactive barriers. J. Contam. Hydrol. 2014, 159, 11-19.

19.

Bokermann, C.; Dahmke, A.; Steiof, M. In Hydrogen evolution from zero-valent iron in batch systems, International Conference on Remediation of Chlorinated and Recalcitrant Compounds, 2nd, Monterey, CA, May 22-25 2000; Monterey, CA, 2000; pp 433-439.

20.

Reardon, E. J., Capture and storage of hydrogen gas by zero-valent iron. J. Contam. Hydrol. 2014, 157, 117-124; DOI 10.1016/j.jconhyd.2013.11.007.

21.

Liu, H.; Wang, Q.; Wang, C.; Li, X. Z., Electron efficiency of zero-valent iron for groundwater remediation and wastewater treatment. Chem. Eng. J. 2013, 215-216, 90-95.

22.

Fan, D.; O'Carroll, D. M.; Elliott, D. W.; Xiong, Z.; Tratnyek, P. G.; Johnson, R. L.; Nunez Garcia, A., Selectivity of nano zerovalent iron in in situ chemical reduction: Challenges and improvements. Remediation J. 2016, 26, (4), 27-40; DOI 10.1002/rem.21481.

23.

Reardon, E. J., Anaerobic corrosion of granular iron: measurement and interpretation of hydrogen evolution rates. Environ. Sci. Technol. 1995, 29, (12), 2936-2945; DOI 10.1021/es00012a008.

24.

Reardon, E. J., Zerovalent irons: Styles of corrosion and inorganic control on hydrogen pressure buildup. Environ. Sci. Technol. 2005, 39, (18), 7311-7317.

25.

Reardon, E. J.; Fagan, R.; Vogan, J. L.; Przepiora, A., Anaerobic corrosion reaction kinetics of nano-sized iron. Environ. Sci. Technol. 2008, 42, (7), 2420-2425; DOI 10.1021/es0712120.

550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582

10/31/18


22

Page 23 of 26



26.

Liu, Y.; Majetich Sara, A.; Tilton Robert, D.; Sholl David, S.; Lowry Gregory, V., TCE dechlorination rates, pathways, and efficiency of nanoscale iron particles with different properties. Environ. Sci. Technol. 2005, 39, (5), 1338-1345; DOI 10.1021/es049195r.

27.

Liu, Y.; Lowry, G. V., Effect of particle age (Fe0 content) and solution pH on nZVI reactivity: H2 evolution and TCE dechlorination. Environ. Sci. Technol. 2006, 40, (19), 6085-6090; DOI 10.1021/es060685o.

28.

Liu, Y.; Phenrat, T.; Lowry, G. V., Effect of TCE concentration and dissolved groundwater solutes on NZVI-promoted TCE dechlorination and H2 evolution. Environ. Sci. Technol. 2007, 41, (22), 7881-7887; DOI 10.1021/es0711967.

29.

Sarathy, V.; Tratnyek, P. G.; Nurmi, J. T.; Baer, D. R.; Amonette, J. E.; Chun, C.; Penn, R. L.; Reardon, E. J., Aging of iron nanoparticles in aqueous solution: effects on structure and reactivity. J. Phys. Chem. C 2008, 112, (7), 2286-2293; DOI 10.1021/jp0777418.

30.

Velimirovic, M.; Carniato, L.; Simons, Q.; Schoups, G.; Seuntjens, P.; Bastiaens, L., Corrosion rate estimations of microscale zerovalent iron particles via direct hydrogen production measurements. J. Hazard. Mater. 2014, 270, 18-26; DOI 10.1016/j.jhazmat.2014.01.034.

31.

Fan, D.; O’Brien Johnson, G. S.; Tratnyek, P. G.; Johnson, R. L., Sulfidation of nano zerovalent iron (nZVI) for improved selectivity during in-situ chemical reduction (ISCR). Environ. Sci. Technol. 2016, 50, (17), 9558–9565; DOI 10.1021/acs.est.6b02170.

32.

Xin, J.; Tang, F.; Zheng, X.; Shao, H.; Kolditz, O.; Lu, X., Distinct kinetics and mechanisms of mZVI particles aging in saline and fresh groundwater: H2 evolution and surface passivation. Water Res. 2016, 100, 80-87; DOI 10.1016/j.watres.2016.04.074.

33.

Tang, F.; Xin, J.; Zheng, T.; Zheng, X.; Yang, X.; Kolditz, O., Individual and combined effects of humic acid, bicarbonate and calcium on TCE removal kinetics, aging behavior and electron efficiency of mZVI particles. Chem. Eng. J. (Amsterdam, Neth.) 2017, 324, 324-335; DOI 10.1016/j.cej.2017.04.144.

34.

Kim, H.-J.; Leitch, M.; Naknakorn, B.; Tilton, R. D.; Lowry, G. V., Effect of emplaced nZVI mass and groundwater velocity on PCE dechlorination and hydrogen evolution in water-saturated sand. J. Hazard. Mater. 2017, 322A, (136-144); DOI 10.1016/j.jhazmat.2016.04.037.

35.

He, F.; Li, Z.; Shi, S.; Xu, W.; Sheng, H.; Gu, Y.; Jiang, Y.; Xi, B., Dechlorination of excess trichloroethene by bimetallic and sulfidated nanoscale zero-valent iron. Environ. Sci. Technol. 2018, ASAP; DOI 10.1021/acs.est.8b01735.

36.

Velimirovic, M.; Auffan, M.; Carniato, L.; Micić Batka, V.; Schmid, D.; Wagner, S.; Borschneck, D.; Proux, O.; von der Kammer, F.; Hofmann, T., Effect of field site hydrogeochemical conditions on the corrosion of milled zerovalent iron particles and their

584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618

10/31/18


23


Page 24 of 26


dechlorination efficiency. Sci. Total Environ. 2018, 618, 1619-1627; DOI 10.1016/j.scitotenv.2017.10.002.

619 620 621

37.

Pineau, A.; Kanari, N.; Gaballah, I., Kinetics of reduction of iron oxides by H2: Part I: Low temperature reduction of hematite. Thermochim. Acta 2006, 447, (1), 89-100; DOI 10.1016/j.tca.2005.10.004.

38.

Barde, A. A.; Klausner, J. F.; Mei, R., Solid state reaction kinetics of iron oxide reduction using hydrogen as a reducing agent. Int. J. Hydrogen Energy 2016, 41, (24), 10103-10119; DOI 10.1016/j.ijhydene.2015.12.129.

39.

Schöftner, P.; Waldner, G.; Lottermoser, W.; Stöger-Pollach, M.; Freitag, P.; Reichenauer, T. G., Electron efficiency of nZVI does not change with variation of environmental parameters. Sci. Total Environ. 2015, 535, 69-78; DOI 10.1016/j.scitotenv.2015.05.033.

40.

Fan, D.; Lan, Y.; Tratnyek, P. G.; Johnson, R. L.; Filip, J.; O'Carroll, D. M.; Garcia, A. N.; Agrawal, A., Sulfidation of iron-based materials: A review of processes and implications for water treatment and remediation. Environ. Sci. Technol. 2017, 51, (22), 13070–13085; DOI 10.1021/acs.est.7b04177.

41.

Li, J.; Zhang, X.; Sun, Y.; Liang, L.; Pan, B.; Zhang, W.; Guan, X., Advances in sulfidation of zerovalent iron for water decontamination. Environ. Sci. Technol. 2017, 51, (23), 13533-13544; DOI 10.1021/acs.est.7b02695.

42.

Han, Y.; Yan, W., Reductive dechlorination of trichloroethene by zero-valent iron nanoparticles: Reactivity enhancement through sulfidation treatment. Environ. Sci. Technol. 2016; DOI 10.1021/acs.est.6b03997.

43.

Xu, C.; Zhang, B.; Wang, Y.; Shao, Q.; Zhou, W.; Fan, D.; Bandstra Joel, Z.; Shi, Z.; Tratnyek , P. G., Effects of sulfidation, magnetization, and oxygenation on azo dye reduction by zerovalent iron. Environ. Sci. Technol. 2016, 50, (21), 11879-11887; DOI 10.1021/acs.est.6b03184.

44.

Fan, D.; Chen, S.; Johnson, R. L.; Tratnyek, P. G., Field deployable chemical redox probe for quantitative characterization of carboxymethylcellulose modified nano zerovalent iron. Environ. Sci. Technol. 2015, 49, (17), 10589-10597; DOI 10.1021/acs.est.5b02804.

45.

Heron, G.; Crouzet, C.; Bourg, A. C. M.; Christensen, T. H., Speciation of Fe(II) and Fe(III) in contaminated aquifer sediments using chemical extraction techniques. Environ. Sci. Technol. 1994, 28, (9), 1698-1705.

46.

Kwon, M. J.; Yang, J.-S.; Shim, M. J.; Boyanov, M. I.; Kemner, K. M.; O’Loughlin, E. J., Acid extraction overestimates the total Fe(II) in the presence of iron (hydr)oxide and sulfide minerals. Environ. Sci. Technol. Lett. 2014; DOI 10.1021/ez500152h.

622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652

10/31/18


24

Page 25 of 26



47.

Liu, Q.; Atrens, A. D.; Shi, Z.; Verbeken, K.; Atrens, A., Determination of the hydrogen fugacity during electrolytic charging of steel. Corros. Sci. 2014, 87, 239-258; DOI 10.1016/j.corsci.2014.06.033.

48.

Jeong, H. Y.; Anantharaman, K.; Hyun, S. P.; Son, M.; Hayes, K. F., pH impact on reductive dechlorination of cis-dichloroethylene by Fe precipitates: An X-ray absorption spectroscopy study. Water Res. 2013, 47, (17), 6639-6649; DOI 10.1016/j.watres.2013.08.035.

49.

Gaspar, D. J.; Lea, A. S.; Engelhard, M. H.; Baer, D. R.; Miehr, R.; Tratnyek, P. G., Evidence for localization of reaction upon reduction of CCl4 by granular iron. Langmuir 2002, 18, (20), 7688-7693; DOI 10.1021/la025798+.

50.

Agrawal, A.; Ferguson, W., J.; Gardner, B., O.; Christ, J., A.; Bandstra, J. Z.; Tratnyek, P. G., Effects of carbonate species on the kinetics of dechlorination of 1,1,1-trichloroethane by zero-valent iron. Environ. Sci. Technol. 2002, 36, (20), 4326-4333; DOI 10.1021/es025562s.

51.

Mayhew, L. E.; Ellison, E. T.; McCollom, T. M.; Trainor, T. P.; Templeton, A. S., Hydrogen generation from low-temperature water-rock reactions. Nat. Geosci. 2013, 6, (6), 478-484; DOI 10.1038/ngeo1825.

52.

Scherer, M. M.; Balko, B. A.; Tratnyek, P. G., The role of oxides in reduction reactions at the metal-water interface. In Mineral-Water Interfacial Reactions: Kinetics and Mechanisms, Sparks, D. L.; Grundl, T. J., Eds. American Chemical Society: Washington, DC, 1998; Vol. 715, pp 301-322.

53.

Díez-Pérez, I.; Sanz, F.; Gorostiza, P., In situ studies of metal passive films. Curr. Opin. Solid State Mater. Sci. 2006, 10, (3-4), 144-152; DOI 10.1016/j.cossms.2007.01.002.

54.

Bae, S.; Collins, R. N.; Waite, T. D.; Hanna, K., Advances in surface passivation of nanoscale zerovalent iron (NZVI): A critical review. Environ. Sci. Technol. 2018; DOI 10.1021/acs.est.8b01734.

55.

Matamoros-Veloza, A.; Cespedes, O.; Johnson, B. R. G.; Stawski, T. M.; Terranova, U.; de Leeuw, N. H.; Benning, L. G., A highly reactive precursor in the iron sulfide system. Nature Comm. 2018, 9, (1), 3125; DOI 10.1038/s41467-018-05493-x.

56.

Wan, M.; Schröder, C.; Peiffer, S., Fe(III):S(-II) concentration ratio controls the pathway and the kinetics of pyrite formation during sulfidation of ferric hydroxides. Geochim. Cosmochim. Acta 2017, 217, 334-348; DOI 10.1016/j.gca.2017.08.036.

57.

Raiswell, R.; Benning, L. G.; Davidson, L.; Tranter, M.; Tulaczyk, S., Schwertmannite in wet, acid, and oxic microenvironments beneath polar and polythermal glaciers. Geology 2009, 37, (5), 431-434; DOI 10.1130/G25350A.1.

654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687

10/31/18


25


Page 26 of 26


58.

Tufano, K. J.; Benner, S. G.; Mayer, K. U.; Marcus, M. A.; Nico, P. S.; Fendorf, S., Aggregate-scale heterogeneity in iron (hydr)oxide reductive transformations. Vadose Zone J. 2009, 8, (4), 1004-1012; DOI 10.2136/vzj2008.0090.

59.

Olowe, A. A.; Bauer, P.; Genin, J. M. R.; Guezennec, J., Moessbauer effect evidence of the existence of green rust 2 transient compound from bacterial corrosion in marine sediments. Corros. Sci. 1989, 45, (3), 229-235; DOI 10.5006/1.3577848.

60.

O'Loughlin, E. J.; Kelly, S. D.; Cook, R. E.; Csencsits, R.; Kemner, K. M., Reduction of uranium(VI) by mixed iron(II)/Iron(III) hydroxide (green rust): Formation of UO2 nanoparticles. Environ. Sci. Technol. 2003, 37, (4), 721-727; DOI 10.1021/es0208409.

61.

He, Y. T.; Wilson, J. T.; Wilkin, R. T., Impact of iron sulfide transformation on trichloroethylene degradation. Geochim. Cosmochim. Acta 2010, 74, (7), 2025-2039.

62.

Culpepper, J.; Scherer, M.; Robinson, T.; Neumann, A.; Cwiertny, D.; Latta, D., Reduction of PCE and TCE by magnetite revisited. Environ. Sci. Proc. Impacts 2018, ASAP; DOI 10.1039/c8em00286j.

63.

Johnson, T. L.; Scherer, M. M.; Tratnyek, P. G., Kinetics of halogenated organic compound degradation by iron metal. Environ. Sci. Technol. 1996, 30, (8), 2634-2640; DOI 10.1021/es9600901.

689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705

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Modeling the Kinetics of Hydrogen Formation by Zerovalent Iron

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