Unveiling Adsorption Mechanisms of Organic Pollutants onto Carbon

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Unveiling Adsorption Mechanisms of Organic Pollutants onto Carbon Nanomaterials by DFT Computations and pp-LFER Modeling Ya Wang, Jingwen Chen, Xiaoxuan Wei, Arturo J. Hernandez-Maldonado, and Zhongfang Chen Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02707 • Publication Date (Web): 11 Sep 2017 Downloaded from http://pubs.acs.org on September 11, 2017

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Environmental Science & Technology

1

Unveiling Adsorption Mechanisms of Organic Pollutants onto Carbon

2

Nanomaterials by DFT Computations and pp-LFER Modeling

3 4

Ya Wang,†,‡ Jingwen Chen,†, * Xiaoxuan Wei,‡

5

Arturo J Hernandez Maldonado,§ Zhongfang Chen ‡,*

6 7

†

8

Environmental Science and Technology, Dalian University of Technology, Linggong Road 2,

9

Dalian 116024, China

Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of

10

‡

Department of Chemistry, University of Puerto Rico, San Juan, PR 00931, USA

11

§

Department of Chemical Engineering, University of Puerto Rico, Mayagüez, PR 00681,

12

USA

13 14

Table of Contents Graphic

15 16

1

ACS Paragon Plus Environment


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ABSTRACT

18

Predicting adsorption of organic pollutants onto carbon nanomaterials (CNMs) and

19

understanding the adsorption mechanisms are of great importance to assess the environmental

20

behavior and ecological risks of organic pollutants and CNMs. By means of density

21

functional theory (DFT) computations, we investigated the adsorption of 38 organic

22

molecules (aliphatic hydrocarbons, benzene and its derivatives, and polycyclic aromatic

23

hydrocarbons) onto pristine graphene in both gaseous and aqueous phases. Poly-parameter

24

linear free energy relationships (pp-LFERs) were developed, which can be employed to

25

predict adsorption energies of aliphatic and aromatic hydrocarbons on graphene. Based on the

26

pp-LFERs, contributions of different interactions to the overall adsorption were estimated. As

27

suggested by the pp-LFERs, the gaseous adsorption energies are mainly governed by

28

dispersion and electrostatic interactions, while the aqueous adsorption energies are mainly

29

determined by dispersion and hydrophobic interactions. It was also revealed that curvature of

30

single-walled carbon nanotubes (SWNTs) exhibits more significant effects than the electronic

31

properties (metallic or semiconducting) on the gaseous adsorption energies, and graphene has

32

stronger adsorption abilities than SWNTs. The developed models may pave a promising way

33

for predicting adsorption of environmental chemicals onto CNMs with in silico techniques.

34 35

INTRODUCTION

36

Carbon nanomaterials (CNMs), including carbon nanotubes (CNTs)1 and graphene2, have

37

shown promising applications in many fields, such as sensors, electronic devices, catalysts,

38

composite materials, photochemotherapy, energy storage, and environmental remediation3-10

2


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due to their remarkable physical and chemical properties. The production of CNMs has been

40

increasing rapidly. Their global production capacity was 4,065 tons in the year 2010, and was

41

estimated to exceed 12,300 tons in 2015.11 With the tremendous increase in both production

42

and potential applications, CNMs can be inevitably released into the environment through its

43

production, transportation, use and disposal. In the environment, CNMs may adsorb organic

44

pollutants and impact the environmental behavior, bioavailability and toxicity of organic

45

pollutants, which can subsequently cause an adverse impact on the environment. 12 , 13

46

Therefore, it is necessary to investigate the adsorption of organic chemicals onto CNMs for

47

evaluating their environmental behavior and ecological risks.14

48

Previous studies have examined the adsorption of some compounds (including benzene

49

derivatives, polycyclic aromatic hydrocarbons, etc.) onto CNMs by experimental and

50

computational techniques.15-38 Adsorption kinetics and thermodynamics for these pollutants

51

onto CNMs have been investigated experimentally, mostly in aqueous phase.16-28 Meanwhile,

52

the adsorption mechanisms have been examined by different simulation methods, such as

53

quantum mechanics, molecular mechanics, molecular dynamics and Monte Carlo simulation,

54

mostly in gas phase.29-38 These experimental and theoretical studies indicate that different

55

adsorption mechanisms may occur simultaneously, i.e., electrostatic interactions, van der

56

Waals interactions, π-π stacking, hydrophobic interactions and hydrogen bonds. Nonetheless,

57

the relative contribution of different adsorption mechanisms to the overall adsorption is

58

ambiguous. Moreover, the differences in adsorption mechanisms between gaseous and

59

aqueous phases are not well understood.

60

Regarded as carbon nanotubes with an infinite diameter (D), graphene has different

3



61

adsorption ability when compared with CNTs. Though the impact of diameter and chirality

62

on the adsorption energy of some small molecules toward CNTs has been discussed,39-41 no

63

detailed study is available to examine the effects of chirality and curvature of CNTs on the

64

adsorption of organic pollutants.

65

Modern computational methods offer a powerful tool to obtain atomic-level details of

66

molecular interactions that would be difficult to obtain by experimental techniques only.

67

Considering the large amounts of organic pollutants in the environment, it is impractical, if

68

not impossible, to simulate the adsorption for all the organic pollutants onto CNMs, due to

69

the high cost and workload. Thus, it is of importance to develop high-throughput models to

70

predict the adsorption of different compounds onto CNMs. So far, quantitative

71

structure-activity relationships (QSARs) have been proved to be reliable for developing such

72

prediction models. There have been a few QSAR models including poly-parameter linear free

73

energy relationships (pp-LFERs) for predicting adsorption behavior of organic compounds

74

onto carbon nanotubes.42-50 However, QSAR models for predicting adsorption of organic

75

pollutants onto graphene are not available.

76

Herein we employed the density functional theory (DFT) computation to predict the

77

adsorption energies and unveil the adsorption mechanisms of 38 organic pollutants

78

(polycyclic aromatic hydrocarbons, aliphatic hydrocarbons and derivatives, benzene and

79

derivatives) onto pristine graphene in both gaseous and aqueous phases, and developed

80

pp-LFERs to estimate the relative contributions of different mechanisms to the overall

81

adsorption. We further explored the impact of curvature and chirality of single-walled carbon

82

nanotubes (SWNTs) on the gaseous adsorption energies by examining the adsorption of some

4


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most representative molecules onto SWNTs with different chirality and curvatures, and

84

extrapolated the adsorption on graphene from the fitting equations. These developed models

85

may pave a promising way for predicting the adsorption on CNMs with in silico techniques.

86 87

COMPUTATIONAL SECTION

88

Adsorbates and Adsorbent Models

89

In this study, 38 different aliphatic and aromatic compounds (Table 1) were chosen as the

90

adsorbate models. Their large variety of the functional groups allow us to investigate

91

substituent effects on the adsorption towards pristine graphene in gaseous and aqueous phases.

92

Besides pristine graphene, we also examined different nanotube adsorbent models, including

93

seven armchair SWNTs, namely, (4, 4), (5, 5), (6, 6), (7, 7), (8, 8), (9, 9) and (10, 10) SWNTs,

94

and seven zigzag SWNTs, namely, (6, 0), (7, 0), (8, 0), (9, 0), (10, 0), (11, 0) and (12, 0)

95

SWNTs, for investigating the impact of curvature and chirality of SWNTs on the gaseous

96

adsorption energies. In the computation, the graphene (8 × 8 × 1) supercell includes 128

97

carbon atoms; while the supercells of armchair and zigzag SWNTs contain seven and four

98

unit cells, respectively, with periodic boundary conditions.

99 100

Table 1. List of Organic Adsorbates together with Their Absolute Values of the

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Adsorption Energies (|Ead|) on Graphene in the Gaseous and Aqueous Environments No.

CAS No.

Name

|Ead| (kcal/mol)

Substituents

gaseous phase

aqueous phase

1

000075-07-0

acetaldehyde

-CHO

6.8

5.4

2

000141-82-2

malonic acid

-COOH

8.1

5.6

5



3

000078-79-5

isoprene

4

000110-82-7

cyclohexane

5

000108-87-2

methylcyclohexane

6

000071-43-2

benzene

7

000062-53-3

aniline

8

000108-88-3

9

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12.1

10.7

9.4

9.5

11.4

11.5

12.9

11.8

-NH2

14.9

12.0

toluene

-CH3

15.3

13.0

000108-95-2

phenol

-OH

14.5

12.4

10

000098-95-3

nitrobenzene

-NO2

16.2

14.3

11

000100-47-0

benzonitrile

-CN

15.6

13.8

12

000100-51-6

phenylmethanol

-CH2OH

15.4

14.1

13

000100-41-4

ethylbenzene

-CH2CH3

16.6

15.3

14

000098-86-2

acetophenone

-C(O)CH3

18.5

15.6

15

000122-79-2

phenyl acetate

-O-C(O)-CH3

15.5

12.5

16

000093-58-3

methylbenzoate

-C(O)OCH3

19.3

17.0

17

000060-12-8

2-phenylethanol

-CH2CH2OH

19.4

16.9

18

000103-65-1

propylbenzene

-CH2CH2CH3

19.2

17.7

19

000093-89-0

ethylbenzoate

-C(O)OCH2CH3

21.3

18.8

20

000528-29-0

1,2-dinitrobenzene

-NO2

17.6

15.4

21

000099-65-0

1,3-dinitrobenzene

-NO2

20.0

17.5

22

000100-25-4

1,4-dinitrobenzene

-NO2

21.0

19.1

23

000099-99-0

4-nitrotoluene

-NO2, -CH3

19.5

17.5

24

000106-42-3

p-xylene

-CH3

18.1

17.0

25

000108-39-4

m-cresol

-CH3, -OH

16.8

15.1

26

000371-41-5

4-fluorophenol

-F, -OH

15.3

13.3

27

000123-07-9

4-ethylphenol

-OH, -CH2CH3

19.3

16.8

28

000587-03-1

(3-methylphenyl) methanol

-CH3, -CH2OH

19.0

16.4

-C(O)OCH3

21.0

19.6

-CH3

-CH3,

29

000089-71-4

methyl-2-methylbenzoate

30

000108-68-9

3,5-dimethylphenol

-CH3, -OH

19.9

17.5

31

000121-14-2

2,4-dinitrotoluene

-NO2, -CH3

23.1

20.3

6


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32

000091-20-3

naphthalene

33

000092-52-4

biphenyl

34

000090-12-0

1-methylnaphthalene

35

000086-73-7

36

18.8

18.0

-C6H5

22.0

20.7

-CH3

22.2

19.9

fluorene

24.5

22.4

000085-01-8

phenanthrene

26.6

25.1

37

000120-12-7

anthracene

26.9

25.3

38

000129-00-0

pyrene

30.5

27.0

102 103 104

Computational Methods for Adsorption Energies All the density functional theory (DFT) computations were performed by the DMol3 51 , 52

105

program.

The

Perdew-Burke-Ernzerhof

generalized

gradient

approximation

106

(GGA-PBE)53 was used to describe the exchange-correlation term. The double-numerical

107

basis with polarization functions (DNP),54,55 which is comparable to Pople’s 6-31G** basis

108

set, 56 was adopted. To accurately describe the long-range electrostatic interactions, the

109

PBE+D2 method with the Grimme van der Waals (vdW) correction was utilized.57 A 4×4×1

110

and a 1×1×6 k-point sampling were used for graphene and SWNTs, respectively, and a

111

Methfessel-Paxton smearing of 0.005 Ha58 was applied for the Brillouin-zone integration.

112

The phenyl (or cyclohexane) planes of adsorbates were initially placed parallel to the surfaces

113

of carbon nanostructures for geometry optimizations.59,60

114

Due to the complexity of the realistic aqueous environment, it is impractical to include

115

thousands of solvent models explicitly in our DFT computations. Instead, we chose the

116

popular conductor-like screening model (COSMO) 61 to implicitly simulate the aqueous

117

environment (with the dielectric constant of water, 78.54) and the hexadecane environment

118

(with the dielectric constant of n-hexadecane, 2.06). In COSMO, the solvent is represented by 7



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119

a dielectric continuum, in which there is a cavity with the shape of solute molecule, and the

120

solute is put into the cavity. The surface charges of the cavity induced by the solvent can be

121

used to describe the electrostatic interactions between the solute and solvent. These surface

122

charges can also be determined directly by using the electrostatic potentials, which is superior

123

to many other solvent reaction field methods.62

124

The adsorption energy (Ead) was calculated as follows: Ead = TEG-X – TEG – TEX

125

(1)

126

where TE represents the total energy that consists of the kinetic energy, static potential energy,

127

Coulomb energy, exchange and correlation energies.63,64 The subscript G-X stands for the

128

graphene-compound complex system, while G represents graphene and X stands for the

129

adsorbates. The adsorption energies on SWNTs were defined similarly. For clarity, we used

130

the absolute values of the adsorption energy (|Ead|) for discussions. According to our

131

definition, the larger the |Ead| value, the stronger the interactions between the adsorbates and

132

graphene or SWNTs are.

133

Abraham Descriptors for pp-LFERs

134 135

pp-LFERs are multiple linear regression models using Abraham descriptors as predictor variables. The frequently used pp-LFERs established by Abraham et al. are as follows:65-70

136

logK = eE + sS + aA + bB + vV + c

(2)

137

logK = eE + sS + aA + bB + lL + c

(3)

138

where logK is logarithmic partition coefficients that are generally determined experimentally

139

and related with free energies; E, S, A, B, V and L are the Abraham descriptors: E, excess

140

molar refraction; S, dipolarity/polarizability parameter; A, hydrogen bond donating ability

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(acidity); B, hydrogen bond accepting ability (basicity); V, McGowan’s molar volume with

142

units of (cm3 mol-1)/100; and L, the logarithmic hexadecane-air partition coefficient. e, s, a, b,

143

v and l are fitting coefficients and c is a regression constant. Eq. 2 can be used for the

144

processes within two condensed phases; while Eq 3 can be applied for the partition between

145

gas and condensed phases. All the Abraham descriptor values used in this study are from the

146

UFZ-LSER database (https://www.ufz.de/index.php?en=31698).

147

The term aA represents the hydrogen bonding interactions between the H-donating solute

148

and H-accepting solvent; while bB represents the hydrogen bonding interactions between the

149

H-accepting solute and H-donating solvent. sS describes the specific interactions related to

150

polarity and polarizability. Nonspecific interactions include van der Waals interactions and

151

cavity formation.71 The terms vV in Eq. 2 and lL in Eq. 3 describe the dispersion interactions

152

and cavity formation. eE is a term accounting for the induction effects (i.e. π and n-electron

153

pairs interactions), which have some overlap with the interactions described by vV and lL.

154

Hence, the nonspecific interactions cannot be fully separated by pp-LFERs. Note that the

155

nature of π-π interactions are electrostatic and van der Waals interactions, 72 which are

156

included in the interactions described by the terms vV, lL, eE and sS.

157

There is a significant correlation between the computed adsorption energies and free

158

energies of organic pollutants on carbon nanotubes.60 Besides, the computed adsorption

159

energies approximate to the enthalpy changes (∆H),73 which are also linearly related with the

160

Abraham descriptors.74,75 pp-LFERs have

161

the partitioning processes as well.76-79 Thus, it is rational to establish pp-LFER models for

162

the adsorption energies.

been developed to describe ∆H associated with

9



163

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pp-LFERs Development and Characterization

164

|Ead| values for the 38 compounds were used for developing pp-LFER models. The

165

goodness of fit and robustness of the pp-LFERs were evaluated by the adjusted determination

166

coefficient (R2adj), root mean square error (RMSE), leave-one-out cross-validated Q2 (Q2LOO),

167

and bootstrap method (Q2BOOT) (1/5, 5000 iterations). Furthermore, Williams plot, which is

168

based on standardized residuals (δ*) and leverage values (hi),80 was used to characterize the

169

application domain (AD) of the predictive models.

170 171

RESULTS AND DISCUSSION

172

Optimized Structures and Adsorption Energies of Adsorbates on Graphene

173

The optimized structures of the 38 organic molecules adsorbed onto graphene are shown in

174

Table S1 of the Supporting information (SI), and Table 1 summarizes their adsorption

175

energies. Generally, the phenyl rings are parallel to the graphene plane, both the distance

176

between the weighted mass center of the molecules and graphene (ca. 3.0 to 3.9 Å) and the

177

adsorption energies (ca. -5 to -30 kcal/mol) are in the range of vdW interactions.

178

pp-LFER Models for Adsorption Energies on Graphene

179 180 181 182 183

The pp-LFER models on |Ead| were obtained as follows: Gaseous phase: |Ead| = 3.570 + 0.911E − 4.350S − 1.684A + 4.910B + 3.456L

(4)

n = 38, R2adj = 0.906, RMSE = 1.505, Q2LOO = 0.846, Q2BOOT = 0.761 Aqueous phase:

184

|Ead| = −0.951 + 2.486E − 0.450S − 0.668A + 0.609B + 14.638V

185

n = 38, R2adj = 0.917, RMSE = 1.341, Q2LOO = 0.858, Q2BOOT = 0.762 10


(5)

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186

As can be seen from Figure 1, the predicted |Ead| values of the pp-LFERs agree well with the

187

DFT computed values. These two models have also rather high goodness-of-fit and

188

robustness, as indicated by the R2adj, Q2LOO and Q2BOOT values, according to the benchmark

189

(R2 > 0.60, Q2 > 0.50).81 As it is rather time-consuming to obtain |Ead| values by DFT

190

computation, these pp-LFER models can serve for high-throughput estimation of adsorption

191

energies of organic compounds (i.e. aliphatic hydrocarbons, benzene and derivatives,

192

polycyclic aromatic hydrocarbons) onto graphene in both gaseous and aqueous phases.

193 194

Figure 1. Correlation of pp-LFER predicted |Ead| values (|Ead|_pre) vs DFT computed ones

195

(|Ead|_cal) in the gaseous and aqueous phases

196 197

As characterized by the Williams plot (Figure S1), all the organic compounds are located

198

in the applicability domains for the models on the gaseous phase |Ead| (Eq. 4) and aqueous

199

phase |Ead| (Eq. 5). Thus, there is no outlier. The applicability domains for the two pp-LFER 11



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200

models cover the aliphatic and aromatic compounds with the substituents -NH2, -CH3, -NO2,

201

-F, -CN, -OH, -CHO, -COOH, -CH2OH, -CH2CH3, -C(O)CH3, -CH2CH2OH, -C(O)OCH3,

202

-OC(O)CH3, -CH2CH2CH3, -C(O)CH2CH3 and -C6H5.

203

Contribution of Different Interactions to the Adsorption

204

The six terms eE, sS, aA, bB, vV and lL describe different types of interactions involved in

205

the adsorption. lL is the most influential term for the prediction of the adsorption energies in

206

the gaseous phase (Figure 2a). The relative contribution of lL to the total interactions is in the

207

range of 32% ~ 74%, thus the dispersion interactions contribute the most to the adsorption,

208

which is in line with Lazar et al’s suggestion that the adsorption on graphene is dominated by

209

London

210

polarizability/dipolarity term (in the range of 2% ~ 23% of the interactions) is also influential

211

for the gaseous adsorption energy. In fact, the sS term describes the specific interactions

212

related to polarity and polarizability and it mainly describes the electrostatic interactions

213

controlled by stable charge separation.82,83

dispersion

forces.29

Besides,

sS

in

Figure

2a

demonstrates

that

the

214

For the gaseous adsorption, the term bB (0 ~ 17% of the interactions) has a positive

215

contribution to the adsorption energies (Figure 2a), indicating that the compound with strong

216

proton-accepting ability can be adsorbed by graphene more easily. Note that the compounds

217

with strong proton-accepting ability usually have electronegative atoms (e.g., N, O and F),

218

the lone pair-π interaction between these electronegative atom and aromatic center of

219

graphene84,85 can further promote the adsorption of compound onto graphene. In contrast,

220

hydrogen bond donating ability (aA) has negative contributions (0 ~ 6% of the interactions)

221

to the adsorption (Figure 2a). Figure 3a exhibits that π and n-electron pairs interactions (eE)

12


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222

do a positive contribution (ranging from 1% to 6%) to the gaseous adsorption, indicating that

223

there exist attractive interactions between the π electrons of graphene and the functional

224

groups of the studied compounds with π or n-electrons. In brief, nonspecific intermolecular

225

interactions and electrostatic interactions prevail in the gaseous adsorption. To the best of our

226

knowledge, this is a first attempt to predict the adsorption energies on graphene by pp-LFERs

227

and apportion the relative contributions of different interactions to the overall adsorption on

228

graphene.

229 230

Figure 2. Box and whisker plots for the values of different interaction terms in pp-LFERs (a)

231

gaseous phase and (b) aqueous phase [The ends of the whiskers represent the minimum and

232

maximum values of each interaction terms. The bottom, the band inside the box and the top

233

of the box represent the first, second and third quartiles for each interaction term. The cross

234

(×) represents the outlier]

13



235

In aqueous phase, the adsorption is mostly determined by the term vV (72% ~ 90% of

236

the overall interactions) representing dispersion and hydrophobic interactions (Figure 2b). To

237

estimate the relative contribution of hydrophobic interactions to the adsorption in aqueous

238

phase, we further computed the adsorption energies in the non-polar organic solvent

239

hexadecane environment. Because hydrophobic interactions are not important for the

240

adsorption in non-polar organic solvents,86 the differences in the adsorption energies between

241

these two phases for the model compounds arise from hydrophobic interactions. DFT

242

computations showed that the relative contributions of hydrophobic interactions (from -3.2

243

kcal/mol to 0.8 kcal/mol, Table S2) to the overall interactions are in the range of ca. 1% to

244

14%. Note that in many cases, the adsorption energies for the studied compounds in aqueous

245

phase are weaker than those in hexadecane environment, indicating that the hydrophobic

246

interactions contribute negatively to the adsorption. This can be understood by the fact that

247

the OH/π interactions between H2O molecules and graphene are stronger than CH/π

248

interactions between the compounds and graphene,87 consequently H2O molecules are able

249

to compete for the optimal interaction positions on graphene. Due to the negative contribution

250

of the hydrophobic interaction for most of the compounds in aqueous phase, the distances

251

between the compounds and graphene are expected to be longer compared with those in

252

gaseous phase. However, we found that in some cases the adsorption distances are slightly

253

smaller (by 0.01 Å to 0.14 Å) in the presence of water. The reason may be that the π or n-

254

electron pairs interactions being involved in the aqueous adsorption can promote the

255

adsorption of compounds onto graphene, which has been validated by the term eE in aqueous

256

model (Eq. 5).

257

The term eE has a positive contribution (4% ~ 20% of the interactions) to the adsorption

258

(Figure 3b). Thus, the π and n-electron pairs interactions can to some extent promote the

259

adsorptions of the organic chemicals onto graphene in aqueous phase. The terms aA (0 ~ 5%

14


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260

of the interactions) and bB (0 ~ 3% of the interactions) have no substantial effect on the |Ead|

261

values in aqueous phase (Figure 2b), which indicate that the hydrogen bonding interactions

262

do not play an influential role in the aqueous adsorption on graphene. That is because the

263

water molecules have proton-donating and proton-accepting abilities, which can compete

264

with compounds for interacting with the graphene. Similarly, Apul et al.48 found that the

265

hydrogen bonding terms were insignificant in the linear solvation energy relationship for

266

aqueous adsorption on multi-walled carbon nanotubes. The sS term contribute negatively (0%

267

~ 5%) to the aqueous |Ead| values (Figure 3b), indicating that the compounds have weaker

268

dipole-dipole and dipole-induced dipole interactions with graphene than with water

269

molecules. Thus, the adsorption of the examined compounds on graphene in aqueous phase is

270

mainly governed by nonspecific interactions and hydrophobic interactions. Note that the

271

dispersion interactions play a dominant role in the adsorption no matter in gaseous or aqueous

272

phase, which can explain that the distances between the weighted mass center of the

273

molecules and graphene in both gaseous and aqueous phases are similar and within the

274

distance scope of dispersion interactions (which usually ranges from 2.5 Å to 6 Å).88

275

Interestingly, several pp-LFERs were developed to predict the adsorption of organic

276

compounds onto CNTs in aqueous phase (Table S3). Note that the molecular descriptors in

277

these pp-LFERs are not exactly the same. Especially, Models 3 and 6 used different

278

descriptors from ours: in Model 3 Apul et al.48 developed the prediction model without the

279

descriptor E, which represents excess molar refraction, while in Model 6 Hüffer et al.46 used

280

the descriptor L (the logarithmic hexadecane-air partition coefficient) replacing the descriptor

281

V. The applicabilities for these models are also different from ours. Most models are

282

developed to obtain the adsorption data on multi-walled carbon nanotubes, only three models

283

(namely Models 7, 9 and 11) are applicable for SWNTs. The number and category of the

284

compounds in these previous models also differ from what examined in this study. Regardless

15



285

of these differences, most of these pp-LFER models include the descriptor V as the most

286

influential term, which describes dispersion and hydrophobic interactions. Similarly, our

287

aqueous pp-LFER also demonstrates that dispersion and hydrophobic interactions prevail in

288

aqueous adsorption on graphene.

289

We further analyzed the effects of different functional groups on the adsorption by

290

comparing the adsorption energies of various benzene derivatives. First, we examined the

291

mono-substituted benzenes with zero or similar Hammett sigma meta parameters (σm) as well

292

as those with different electron-donating and electron-withdrawing groups (Table S4). The

293

case for phenylmethanol is enlightening: though the -CH2OH substituent is neither

294

electron-withdrawing nor donating as indicated by its zero σm value, compared to that of

295

benzene, the adsorption energy is enhanced by 2.5 and 2.3 kcal/mol in gaseous and aqueous

296

phase, respectively. Checking the compounds with similar σm values, namely toluene,

297

ethylbenzene and propylbenzene, revealed that the adsorption energies between the benzene

298

derivatives and graphene increase with increasing the volume of the functional groups. For

299

the adsorption of mono-substituted benzenes with different electron-donating and

300

electron-withdrawing groups, though these functional groups can significantly affect the

301

charge distribution (Table S5) of the compounds, all these benzene derivatives have stronger

302

adsorption energies than benzene. Experimentally, Yu et al.27 also found that the adsorption

303

energy of aniline is stronger than that for benzene towards graphene oxides. All these data on

304

the mono-substituted benzenes strongly demonstrate the important contribution of the

305

dispersive interaction between the substituents and graphene to the overall adsorption energy.

306

Furthermore, we examined the adsorption of di-substituted benzenes by taking

307

dinitrobenzenes (1,2-dinitrobenzene, 1,3-dinitrobenzene and 1,4-dinitrobenzene) as example. 16


Page 16 of 34

Page 17 of 34


308

With more functional groups, these compounds have stronger interaction with graphene than

309

those of benzene and nitrobenzene (Table 1). The location of the functional groups also has

310

an influence on the adsorption: with the same number of functional groups, 1,2-dinitrobenzene,

311

1,3-dinitrobenzene and 1,4-dinitrobenzene have different adsorption energies on graphene

312

(17.6, 20.0 and 21.0 kcal/mol, 15.4, 17.5 and 19.1 kcal/mol, respectively, in gaseous and

313

aqueous phases), and the relatively more stretched isomer has the stronger adsorption energy,

314

which can also be understood by the more vdW contacts in such configurations. In general,

315

the benzene derivatives with larger molar volume, or more contacts with the graphene surface,

316

have stronger adsorption energies due to their more favorable dispersive interaction between

317

the substituents and graphene.

318

Effects of Chirality and Curvature for SWNTs on Gaseous Adsorption

319

The aforementioned discussions have demonstrated that the differences between gaseous

320

and aqueous adsorption energies on graphene are insignificant, the same is expected for the

321

adsorptions on SWNTs. Thus, we only investigated the impact of curvature and chirality of

322

SWNTs on the gaseous adsorption energies. To probe the effects of chirality and curvature on

323

the adsorption of substituted benzenes by SWNTs, we chose benzene and its derivatives with

324

electron-withdrawing (nitrobenzene) and electron-donating groups (aniline, toluene and

325

phenol), and its fully hydrogenated form (cyclohexane) as model compounds, since they are

326

most representative and the trend obtained based on these molecules is expected to be valid

327

for other benzene derivatives. Note that all the armchair SWNTs are metallic, while the

328

zigzag SWNTs can be either metallic (when n = 3m) or semiconducting (when n ≠ 3m).

329

Figure 3 shows the relationships between the adsorption energies of these molecules on

17



330

SWNTs with different chirality and curvature in gaseous phase. For cyclohexane, its

331

adsorptions on both armchair and zigzag SWNTs are weaker than the benzene derivatives,

332

indicating that the CH-π interaction is not as strong as the π-π interaction between the

333

aromatics and SWNTs. For benzene, when the diameter reaches 5.55 and 8.82 Å, the

334

adsorption energy on (n, n) SWNTs is equal to that on (n, 0) SWNTs. When D is less than

335

5.55 Å or larger than 8.82 Å, the adsorption energy on armchair SWNTs is stronger than that

336

on zigzag SWNTs. For the benzene derivatives with electron-donating groups (aniline,

337

toluene and phenol), their adsorption energies on armchair SWNTs are also stronger than

338

those on zigzag SWNTs in different ranges (D(aniline) < 4.09 or > 10.52 Å, D(toluene)
9.55 Å, D(phenol) < 3.31 or > 9.62 Å). These findings may be rationalized that all

340

the armchair SWNTs are metallic and the more mobile π-electrons can enhance the

341

adsorption between these compounds and SWNTs. However, for nitrobenzene featuring with

342

the electron-withdrawing group, its adsorption energies on armchair SWNTs are weaker than

343

that on zigzag SWNTs (when the diameter is smaller than 4.33 or larger than 12.46 Å). Thus,

344

the electron-withdrawing or donating capability significantly affects the adsorption on

345

nanotubes with different chirality.

346

In terms of the metallic armchair SWNTs, the interaction energy between these six model

347

compounds and armchair SWNTs seem to reach a specific value when the diameter

348

approaches infinity despite the slight variation for cyclohexane and benzene on (10, 10)

349

SWNT. For zigzag SWNTs, the adsorption energy of the SWNTs with a median diameter for

350

semiconducting SWNTs (8, 0) and (10, 0) is weaker than that on metallic SWNT (9, 0) as to

351

cyclohexane, benzene, toluene, aniline and nitrobenzene, which indicates that the electronic

18


Page 18 of 34

Page 19 of 34


352

properties only slightly influence the adsorption on SWNTs. Note that exception occurs for

353

the adsorption of phenol. In this special case, the high electronegativity of oxygen atom in

354

-OH group induces stronger repulsive interactions between -OH group and metallic SWNT (9,

355

0). Overall, regardless the electronic properties of the SWNTs, metallic or semiconducting,

356

the adsorption energies of all the chosen molecules become stronger with the increase of the

357

nanotube diameter D. Note that the contact area between the adsorbate molecules and the

358

nanotube surface increases with increasing nanotube diameter, and these more dispersive

359

interactions lead to stronger adsorption interactions for larger nanotubes. Thus, the diameter

360

(or curvature) of SWNTs influences the adsorption more significantly than the electronic

361

properties of SWNTs. 9.0 (9,9)

7.5

(8,0)

7.0 6.5 6.0

(6,0)

(8,8)

(10,10)

(7,7)

(6,6) (5,5)

(7,0)

(8,8)

10.0 |Ead|(kcal/mol)

(11,0) (10,0) (9,0)

benzene

(4,4)

(12,0) (11,0) (10,0) (7,7) (9,0) (6,6) (8,0) (5,5) (4,4)

9.5 9.0

(7,0)

8.5

5.5

13.0 12.5 12.0 11.5 11.0 10.5 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0

4

5

6

7

(10,10)

(12,0) (11,0) (10,0) (7,7) (9,0) (8,0) (6,6) (7,0) (4,4) (5,5) (6,0)

10.5 10.0 9.5 9.0 3

362

4

5

6

7

10.5

3

8 9 10 11 12 13 14 15 D(angstrom)

(5,5)

8 9 10 11 12 13 14 15 D(angstrom)

(6,6)

(4,4)

4

5

6

7

8 9 10 11 12 13 14 15 D(angstrom)

(c) (10,10)

phenol (12,0)

(10,0)(11,0) (8,8) (9,0) (7,7) (8,0) (6,6) (5,5) (7,0) (6,0) (4,4)

nitrobenzene

13.0

(9,9)

(8,8)

12.5 |Ead|(kcal/mol)

(8,8)

11.0

(6,0)

13.5

|Ead|(kcal/mol)

|Ead|(kcal/mol)

(9,9)

toluene

11.5

(7,0)

11.0

(b)

13.0

12.0

11.5

9.0 3

(a) 12.5

12.0

(10,10)

9.5

8.0

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 D(angstrom)

12.5

10.0

(6,0)

5.0

(8,8) (12,0) (11,0) (10,0) (9,0) (7,7) (8,0)

13.0

(10,10)

(9,9)

aniline

13.5

(9,9)

|Ead|(kcal/mol)

(12,0)

8.0 |Ead|(kcal/mol)

14.0

10.5

cyclohexane

8.5

(7,7)

(6,6) 12.0

(5,5)

11.5

(10,10) (9,9)

(12,0)

(11,0) (10,0) (4,4) (9,0) (8,0) (7,0) (6,0)

11.0 10.5 10.0

2

3

4

5

6

7 8 9 10 11 12 13 14 15 D(angstrom)

(d)

(e)

3

4

5

6

7

8 9 10 11 12 13 14 15 D(angstrom)

(f)

363

Figure 3. Fitting curves for the adsorption of benzene and derivatives onto SWNTs with

364

different chirality and curvature in gaseous phase

365 366

In order to examine the relationships between the adsorption energies on SWNTs and

19



367

Page 20 of 34

those on graphene, we fitted the curves in Figure 3 with the following exponential function: |Ead (D)| = |Ead (graphene)| + u × e(-D/w)

368

(6)

369

where |Ead (D)| represents the absolute values of the adsorption energy for the compounds on

370

SWNTs with the diameter D, |Ead (graphene)| is the extrapolated absolute values of

371

adsorption energy on graphene for a compound among the six chosen molecules

372

(cyclohexane, benzene, aniline, toluene, phenol and nitrobenzene), u and w are the fitting

373

parameters. Table S7 lists the fitting parameters u, w and |Ead (graphene)|. The values of R2adj

374

(Table S7) indicate that the adsorption energies for the six chemicals toward SWNTs in

375

gaseous phase fit Eq. 6 well. The |Ead (graphene)| extrapolated from the adsorption data on

376

armchair SWNTs are different from that based on the adsorption data on zigzag SWNTs

377

(Table S7). However, the deviations are not significant (in the range of 0.3 to 3.4 kcal/mol).

378

In general, these fitted equations can predict reasonably well the adsorption of these

379

examined organic compounds on SWNTs with different chirality and curvatures.

380

All the examined molecules have stronger adsorption energies on graphene than the

381

SWNTs in gaseous phase. This finding can be rationalized by the larger contact area for the

382

adsorptions on graphene. Our computational results echo the very recent experimental finding

383

that graphene nanomaterials show stronger adsorption capability than CNTs.89 Note that in

384

this work, we only studied the adsorption on pristine CNTs, while functional groups, defects,

385

etc. exist on surfaces of realistic CNTs which may affect the adsorption and should be

386

investigated in further studies.

387

Implications

388

The pp-LFER models developed in this study can offer a time-efficient method to

20


Page 21 of 34


389

evaluate adsorption of organic pollutants onto graphene and SWNTs. With the models, the

390

contributions of different interactions to the overall adsorption have been quantified. By

391

means of DFT computations, it was also revealed that curvature of SWNTs exhibit more

392

significant effects than electronic properties (metallic or semiconducting) on the adsorption

393

energies, and graphene has stronger adsorption abilities than SWNTs.

394

As there are numerous chemicals of concern that can be released into the environment,

395

and there are many kinds of natural and artificial materials (e.g., minerals, black carbon,

396

dissolved or suspended organic matter, microplastics, as well as organic and inorganic

397

nanomaterials) that are components of the environment or can be released into the

398

environment, there have been ever-lasting efforts to investigate the interactions between the

399

chemicals and materials so as to evaluate the risks of both.3,7,12-14 Conventionally, the

400

investigation was implemented by experiment which is usually tedious, costly and laggard.

401

The current study, together with many previous studies,42-50,60 conjointly strives to achieve the

402

goal in computational environmental chemistry that was advocated by Prof. Sedlak, “it is

403

now possible to imagine a day when many of the constants that we carefully measure in the

404

lab will be accessible with the click of a mouse”.90

405 406

ASSOCIATED CONTENT

407

Supporting Information

408

Adsorption equilibrium configuration (Table S1 and Table S6), the adsorption energies (in

409

absolute values) on graphene (Table S2), prediction models for the adsorption on CNTs

410

(Table S3), relative interaction energies and Hammett parameters for 12 monosubstituted

411

benzene on graphene (Table S4), charges distributions of the 38 compounds (Table S5), 21



412

fitting parameters u, w and absolute values of adsorption energies |Ead| values for the

413

compounds on SWNTs (Table S7) and Williams plot (Figure S1). This material is available

414

free of charge via the Internet at http://pubs.acs.org.

415 416

AUTHOR INFORMATION

417

Corresponding Authors

418

*Jingwen Chen, Phone/fax: +86-411-84706269, e-mail: [email protected].

419

*Zhongfang Chen, Phone/fax: +1(787)764-0000 ext. 5919,

420

e-mail: [email protected].

421

Notes

422

The authors declare no competing financial interest.

423

ACKNOWLEDGEMENTS

424

The study was supported in China by the National Natural Science Foundation of China

425

(21325729, 21661142001) and in USA by NSF (Grant EPS-1002410). Ya Wang

426

acknowledges a fellowship from the China Scholarship Council.

427

REFERENCES

(1) Iijima, S. Helical microtubules of graphitic carbon. Nature 1991, 354 (6348), 56−58. (2) Novoselov, K. S.; Geim, A. K.; Morozov, S. V.; Jiang, D.; Zhang, Y.; Dubonos, S. V.; Grigorieva, I. V.; Firsov, A. A. Electric field effect in atomically thin carbon films. Science 2004, 306 (5696), 666−669. (3) Yang, K. and Xing, B. S. Adsorption of organic compounds by carbon nanomaterials in

22


Page 22 of 34

Page 23 of 34


aqueous phase: Polanyi theory and its application. Chem. Rev. 2010, 110 (10), 5989−6008. (4) Novoselov, K. S.; Fal’ko, V. I.; Colombo, L.; Gellert, P. R.; Schwab, M. G.; Kim, K. A roadmap for graphene. Nature 2012, 490 (7419), 192−200. (5) Chou, S. S.; De, M.; Luo, J. Y.; Rotello, V. M.; Huang, J. X.; Dravid, V. P. Nanoscale graphene oxide (nGO) as artificial receptors: Implications for biomolecular interactions and sensing. J. Am. Chem. Soc. 2012, 134 (40), 16725−16733. (6) Wang, Y.; Li, Z. H.; Hu, D. H.; Lin, C. T.; Li, J. H.; Lin, Y. H. Aptamer/graphene oxide nanocomplex for in situ molecular probing in living cells. J. Am. Chem. Soc. 2010, 132 (27), 9274−9276. (7) Hu, X. G. and Zhou, Q. X. Health and ecosystem risks of graphene. Chem. Rev. 2013, 113 (5), 3815−3835. (8) Shen, Y.; Fang, Q. L.; Chen, B. L. Environmental applications of three-dimensional graphene-based macrostructures: Adsorption, transformation, and detection. Environ. Sci. Technol. 2015, 49 (1), 67−84.

(9) Mudedla, S. K.; Balamurugan, K. and Subramanian, V. Computational study on the interaction of modified nucleobases with graphene and doped graphenes. J. Phys. Chem. C 2014, 118 (29), 16165−16174. (10) Guo, C. X.; Yang, H. B.; Sheng, Z. M.; Lu, Z. S.; Song, Q. L.; Li, C. M. Layered graphene/quantum dots for photovoltaic devices. Angew. Chem. Int. Ed. 2010, 49 (17), 3014−3017. ( 11 )

Global nano carbon production value to reach nearly $1.3 billion by 2015;

23



PRLog-Global Press Release Distribution: Stamford, 2011; http://prlog.org/11302585. (12) Zhao, J.; Wang, Z. Y.; White, J. C.; Xing, B. S. Graphene in the aquatic environment: Adsorption, dispersion, toxicity and transformation. Environ. Sci. Technol. 2014, 48 (17), 9995−10009. (13)Maynard, A. D.; Aitken, R. J.; Butz, T.; Colvin, V.; Donaldson, K.; Oberdörster, G.; Philbert, M. A.; Ryan, J.; Seaton, A.; Stone, V.; Tinkle, S. S.; Tran, L.; Walker, N. J.; Warheit, D. B. Safe handling of nanotechnology. Nature 2006, 444 (7117), 267−269. (14) Leszczynski, J. Bionanoscience: Nano meets bio at the interface. Nat. Nanotechnol. 2010, 5 (9), 633-634.

(15) Pan, B. and Xing, B. S. Adsorption mechanisms of organic chemicals on carbon nanotubes. Environ. Sci. Technol. 2008, 42 (24), 9005−9013. (16) Zuo, L. Z., Guo, Y.; Li, X.; Fu, H. Y.; Qu, X. L.; Zheng, S. R.; Gu, C.; Zhu, D. Q.; Alvarez, P. J. J. Enhanced adsorption of hydroxyl- and amino-substituted aromatic chemicals to nitrogen-doped multiwall carbon nanotubes: A combined batch and theoretical calculation study. Environ. Sci. Technol. 2016, 50 (2), 899−905. (17) Li, X. Y.; Gámiz, B.; Wang, Y. Q.; Pignatello, J. J.; Xing, B. S. Competitive sorption used to probe strong hydrogen bonding sites for weak organic acids on carbon nanotubes. Environ. Sci. Technol. 2015, 49 (3), 1409−1417.

(18) Zhao, G. X.; Li, J. X.; Ren, X. M.; Chen, C. L.; Wang, X. K. Few-layered graphene oxide nanosheets as superior sorbents for heavy metal ion pollution management. Environ. Sci. Technol. 2011, 45 (24), 10454−10462.

24


Page 24 of 34

Page 25 of 34


(19) Shen, Y. and Chen, B. L. Sulfonated graphene nanosheets as a superb adsorbent for various environmental pollutants in water. Environ. Sci. Technol. 2015, 49 (12), 7364−7372. (20) Tan, P.; Sun, J.; Hu, Y. Y.; Fang, Z.; Bi, Q.; Chen, Y. C.; Cheng, J. H. Adsorption of Cu2+, Cd2+ and Ni2+ from aqueous single metal solutions on graphene oxide membranes. J. Hazard. Mater. 2015, 297, 251−260.

(21) Wang, X. B.; Qin, Y. L.; Zhu, L. H.; Tang, H. Q. Nitrogen-doped reduced graphene oxide as a bifunctional material for removing bisphenols: Synergistic effect between adsorption and catalysis. Environ. Sci. Technol. 2015, 49 (11), 6855−6864. (22) Liu, F. F.; Zhao, J.; Wang, S. G.; Du, P.; Xing, B. S. Effects of solution chemistry on adsorption of selected pharmaceuticals and personal care products (PPCPs) by graphenes and carbon nanotubes. Environ. Sci. Technol. 2014, 48 (22), 13197−13206. (23) Wang, J.; Chen, Z. M.; Chen, B. L. Adsorption of polycyclic aromatic hydrocarbons by graphene and graphene oxide nanosheets. Environ. Sci. Technol. 2014, 48 (9), 4817−4825. (24) Chen, X. X. and Chen, B. L. Macroscopic and spectroscopic investigations of the adsorption of nitroaromatic compounds on graphene oxide, reduced graphene oxide, and graphene nanosheets. Environ. Sci. Technol. 2015, 49 (10), 6181−6189. (25) Zhao, J.; Wang, Z. Y.; Zhao, Q.; Xing, B. S. Adsorption of phenanthrene on multilayer graphene as affected by surfactant and exfoliation. Environ. Sci. Technol. 2014, 48 (1), 331−339. (26) Wang, J.; Chen, B. L.; Xing, B. S. Wrinkles and folds of activated graphene nanosheets as fast and efficient adsorptive sites for hydrophobic organic contaminants. Environ. Sci.

25



Technol. 2016, 50 (7), 3798−3808.

(27) Yu, S. J.; Wang, X. X.; Ai, Y. J.; Tan, X. L.; Hayat, T.; Hu, W. P.; Wang, X. K. Experimental and theoretical studies on competitive adsorption of aromatic compounds on reduced graphene oxides. J. Mater. Chem. A 2016, 9 (15), 5654−5662. (28) Zou, Y. D.; Wang, X. X.; Ai, Y. J.; Liu, Y. H.; Ji, Y. F.; Wang, H. Q.; Hayat, T.; Alsaedi, A.; Hu, W. P.; Wang, X. K. β-Cyclodextrin modified graphitic carbon nitride for the removal of pollutants from aqueous solution: Experimental and theoretical calculation study. J. Mater. Chem. A 2016, 4 (37), 14170−14179.

(29) Lazar, P.; Karlický, F.; Jurečka, P.; Kocman, M.; Otyepková, E.; Šafářová, K.; Otyepka, M. Adsorption of small organic molecules on graphene. J. Am. Chem. Soc. 2013, 135 (16), 6372−6377.

(30) Chen, J. L.; Zhou, G. Q.; Chen, L.; Wang, Y.; Wang, X. G.; Zeng, S. W. Interaction of graphene and its oxide with lipid membrane: A molecular dynamics simulation study. J. Phys. Chem. C 2016, 120 (11), 6225−6231.

(31) Sun, Y. B.; Yang, S. B.; Chen, Y.; Ding, C. C.; Cheng, W. C.; Wang, X. K. Adsorption and desorption of U(VI) on functionalized graphene oxides: A combined experimental and theoretical study. Environ. Sci. Technol. 2015, 49 (7), 4255−4262. (32) Jin, Z. X.; Wang, X. X.; Sun, Y. B.; Ai, Y. J.; Wang, X. K. Adsorption of 4-n -nonylphenol and bisphenol-A on magnetic reduced graphene oxides: A combined experimental and theoretical studies. Environ. Sci. Technol. 2015, 49 (15), 9168−9175. (33) Camden, A. N.; Barr, S. A.; Berry, R. J. Simulations of peptide-graphene interactions in

26


Page 26 of 34

Page 27 of 34


explicit water. J. Phys. Chem. B 2013, 117 (37), 10691−10697. (34) Katoch, J.; Kim, S. N.; Kuang, Z. F.; Farmer, B. L.; Naik, R. R.; Tatulian, S. A.; Ishigami, M. Structure of a peptide adsorbed on graphene and graphite. Nano Lett. 2012, 12 (5), 2342−2346. (35) Roos, M.; Künzel, D.; Uhl, B.; Huang, H. H.; Alves, O. B.; Hoster, H. E.; Gross, A.; Behm, J. Hierarchical interactions and their influence upon the adsorption of organic molecules on a graphene film. J. Am. Chem. Soc. 2011, 133 (24), 9208−9211. (36) Yu, S. J.; Wang, X. X.; Yao, W.; Wang, J.; Ji, Y. F.; Ai, Y. J.; Alsaedi, A.; Hayat, T.; Wang, X. K. Macroscopic, spectroscopic, and theoretical investigation for the interaction of phenol and naphthol on reduced graphene oxide. Environ. Sci. Technol. 2017, 51 (6), 3278−3286. (37) Comer, J.; Chen, R.; Poblete, H.; Vergara-Jaque, A.; Riviere, J. E. Predicting adsorption affinities of small molecules on carbon nanotubes using molecular dynamics simulation. ACS Nano 2015, 9 (12), 11761−11774.

(38) Gallouze, M.; Kellou, A.; Drir, M. Adsorption isotherms of H2 on defected graphene: DFT and Monte Carlo studies. Int. J. Hydrogen Energy 2016, 41 (12), 5522−5530. (39) Zhao, J. J.; Buldum, A.; Han, J.; Lu, J. P. Gas molecule adsorption in carbon nanotubes and nanotube bundles. Nanotechnology, 2002, 13 (2), 195−200. (40) Wang, C. H. and Jiang, Y. Interaction mechanism between serine functional groups and single-walled carbon nanotubes. J. Phys. Org. Chem. 2016, 29 (2), 69−76. (41) Munusamy, E. and Wheeler, S. E. Endohedral and exohedral complexes of substituted benzenes with carbon nanotubes and graphene. J. Chem. Phys. 2013, 139 (9), 094703.

27



(42) Ding, H.; Chen, C.; Zhang, X. Linear solvation energy relationship for the adsorption of synthetic organic compounds on single-walled carbon nanotubes in water. SAR QSAR Environ. Res. 2016, 27 (1), 31−45.

(43) Apul, O. G.; Zhou, Y.; Karanfil, T. Mechanisms and modeling of halogenated aliphatic contaminant adsorption by carbon nanotubes. J. Hazard. Mater. 2015, 295, 138−144. (44) Yu, X. Q.; Sun, W. L.; Ni, J. R. LSER model for organic compounds adsorption by single-walled carbon nanotubes: Comparison with multi-walled carbon nanotubes and activated carbon. Environ. Pollut. 2015, 206, 652−660. (45) Zhao, Q.; Yang, K.; Li, W.; Xing, B. S. Concentration-dependent polyparameter linear free energy relationships to predict organic compound sorption on carbon nanotubes. Sci. Rep. 2014, 4, 3888. (46) Hüffer, T.; Endo, S.; Metzelder, F.; Schroth, S.; Schmidt, T. C. Prediction of sorption of aromatic and aliphatic organic compounds by carbon nanotubes using poly-parameter linear free-energy relationships. Water Res. 2014, 59, 295−303. (47) Chen, R.; Zhang, Y. T.; Sahneh, F. D.; Scoglio, C. M.; Wohlleben, W.; Haase, A.; Monteiro-Riviere, N. A.; Riviere, J. E. Nanoparticle surface characterization and clustering through concentration-dependent surface adsorption modeling. ACS Nano 2014, 8 (9), 9446−9456. (48) Apul, O. G.; Wang, Q. L.; Shao, T.; Rieck, J. R.; Karanfil, T. Predictive model development for adsorption of aromatic contaminants by multi-walled carbon nanotubes. Environ. Sci. Technol. 2013, 47 (5), 2295−2303.

28


Page 28 of 34

Page 29 of 34


(49) Xia, X. R.; Monteiro-Riviere, N. A.; Mathur, S.; Song, X. F.; Xiao, L. S.; Oldenberg, S. J.; Fadee, B.; Riviere, J. E. Mapping the surface adsorption forces of nanomaterials in biological systems. ACS Nano 2011, 5 (11), 9074−9081. (50) Xia, X. R.; Monteiro-Riviere, N. A.; Riviere, J. E. An index for characterization of nanomaterials in biological systems. Nat. Nanotech. 2010, 5 (9), 671−675. (51) Delley, B. An all-electron numerical method for solving the local density functional for polyatomic molecules. J. Chem. Phys. 1990, 92 (1), 508−517. (52) Delley, B. From molecules to solids with the DMol3 approach. J. Chem. Phys. 2000, 113 (18), 7756−7764. (53) Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 1996, 77 (18), 3865−3868.

(54) Benedek, N. A.; Snook, I. K.; Latham, K.; Yarovsky, I. Application of numerical basis sets to hydrogen bonded systems: A density functional theory study. J. Chem. Phys. 2005, 122 (14), 144102−144108.

(55) Inada, Y. and Orita, H. Efficiency of numerical basis sets for predicting the binding energies of hydrogen bonded complexes: Evidence of small basis set superposition error compared to Gaussian basis sets. J. Comput. Chem. 2008, 29 (2), 225−232. (56) Liu, P. and Rodriguez, J. A. Catalysts for hydrogen evolution from the [NiFe] hydrogenase to the Ni2P (001) surface: The importance of ensemble effect. J. Am. Chem. Soc. 2005, 127 (42), 14871−14878. (57) Grimme, S. Semiempirical GGA-type density functional constructed with a long-range

29



dispersion correction. J. Comput. Chem. 2006, 27 (15), 1787−1799. (58) Wang, H. M.; Wang, H. X.; Chen, Y.; Liu, Y. J.; Zhao, J. X.; Cai, Q. H.; Wang, X. Z. Phosphorus-doped graphene and (8, 0) carbon nanotube: Structural, electronic, magnetic properties, and chemical reactivity. Appl. Surf. Sci. 2013, 273, 302−309. (59) Scott, A. M.; Gorb, L.; Mobley, E. A.; Hill, F. C.; Leszczynski, J. Predictions of gibbs free energies for the adsorption of polyaromatic and nitroaromatic environmental contaminants on carbonaceous materials: Efficient computational approach. Langmuir 2012, 28 (37), 13307−13317.

(60) Zou, M. Y.; Zhang, J. D.; Chen, J. W.; Li, X. H. Simulating adsorption of organic pollutants on finite (8,0) single-walled carbon nanotubes in water. Environ. Sci. Technol. 2012, 46 (16), 8887−8894. (61) Andzelm, J.; Kölmel, C.; Klamt, A. Incorporation of solvent effects into the density functional calculations of molecular energies and geometries. J. Chem. Phys. 1995, 103 (21), 9312−9320. (62) Barone, V. and Cossi, M. Quantum calculation of molecular energies and energy gradients in solution by a conductor solvent model. J. Chem. Phys. A 1998, 102 (11), 1995−2001. (63) Hohenberg, P. and Kohn, W. Inhomogeneous electron gas. Phys. Rev. B 1964, 136 (3B), 864−871. (64) Kohn, W. and Sham, L. J. Self-consistent equations including exchange and correlation effects. Phys. Rev. A 1965,140 (4A), 1133−1138.

30


Page 30 of 34

Page 31 of 34


(65) Abraham, M. H.; Grellier, P. L.; McGill, R. A. Determination of olive oil-gas and hexadecane-gas partition coefficients, and calculation of the corresponding olive oil-water and hexadecane-water partition coefficients. J. Chem. Soc., Perkin Trans. 2 1987, No. 6, 797−803. (66) Abraham, M. H. and McGowan, J. C. The use of characteristic volumes to measure cavity terms in reversed phase liquid chromatography. Chromatographia 1987, 23 (4), 243−246. (67) Abraham, M. H.; Whiting, G. S.; Doherty, R. M.; Shuely, W. J. Hydrogen bonding. Part 13. A new method for the characterization of GLC stationary phases-The Laffort data set. J. Chem. Soc. Perkin Trans. 2 1990, No. 8, 1451−1460.

(68) Abraham, M. H.; Whiting, G. S.; Doherty, R. M.; Shuely, W. J. Hydrogen bonding. XVI. A new solute solvation parameter, π2H, from gas chromatographic data. J. Chromatogr. 1991, 587 (2), 213−228.

(69) Abraham, M. H. Hydrogen-bonding. 31. Construction of a scale of solute effective or summation hydrogen-bond basicity. J. Phys. Org. Chem. 1993, 6 (12), 660−684. (70) Abraham, M. H. Scales of solute hydrogen-bonding: Their construction and application to physicochemical and biochemical processes. Chem. Soc. Rev. 1993, 22 (2), 73−83. (71)

Goss, K., et al., The Partition behavior of fluorotelomer alcohols and olefins. Environ.

Sci. Technol. 2006, 40 (11), 3572−3577.

(72) Hunter, C. A. and Sanders, J. K. M. The Nature of π-π Interactions. J. Am. Chem. Soc. 1990, 112 (14), 5525−5534.

31



(73) Alapati, S. V.; Johnson, J. K.; Sholl, D. S. Empirical prediction of heats of vaporization and heats of adsorption of organic compounds. J. Phys. Chem. B 2006, 110 (17), 8769−8776.

(74) Goss, K. U. and Schwarzenbach, R. P. Empirical prediction of heats of vaporization and heats of adsorption of organic compounds. Environ. Sci. Technol. 1999, 33 (19), 3390−3393. (75) Goss, K. U. and Schwarzenbach, R. P. Linear free energy relationships used to evaluate equilibrium partitioning of organic compounds. Environ. Sci. Technol. 2001, 35(1), 1−9. (76) Endo, S.; Goss, K. U. Applications of polyparameter linear free energy relationships in environmental chemistry. Environ. Sci. Technol. 2014, 48 (21), 12477−12491. (77) Geisler, A.; Endo, S.; Goss, K. U. Partitioning of organic chemicals to storage lipids: Elucidating the dependence on fatty acid composition and temperature. Environ. Sci. Technol. 2012, 46 (17), 9519−9524. (78) Stephens, T. W.; De La Rosa, N. E.; Saifullah, M.; Ye, S.; Chou, V.; Quay, A. N.; Acree, W. E., Jr.; Abraham, M. H. Enthalpy of solvation correlations for organic solutes and gases dissolved in 2-propanol, 2-butanol, 2-methyl-1-propanol and ethanol. Thermochim. Acta 2011, 523 (1−2), 214−220.

(79) Mintz, C.; Clark, M.; Acree, W. E., Jr.; Abraham, M. H. Enthalpy of solvation correlations for gaseous solutes dissolved in water and in 1-octanol based on the Abraham model. J. Chem. Inf. Model. 2007, 47 (1), 115−121. (80) Gramatica, P. Principles of QSAR models validation: Internal and external. QSAR Comb. Sci. 2007, 26 (5), 694−701.

(81) Golbraikh, A.; Shen, M.; Xiao, Z. Y.; Xiao, Y. D.; Lee, K. H.; Tropsha, A. Rational

32


Page 32 of 34

Page 33 of 34


selection of training and test sets for the development of validated QSAR models. J. Comput. Aided. Mol. Des. 2003, 17 (2), 241−253.

(82) Nguyen, T. H.; Goss, K. U.; Ball, W. P. Polyparameter linear free energy relationships for estimating the equilibrium partition of organic compounds between water and the natural organic matter in soils and sediments. Environ. Sci. Technol. 2005, 39 (4), 913−924. (83) Arey, J. S. Environmental screening of future gasoline additives: Computational tools to estimate chemical partitioning and forecast widespread groundwater contamination. Ph.D. Dissertation, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 2004, 200 pp. (84) Egli, M. and Sarkhel, S. Lone pair-aromatic interactions: To stabilize or not to stabilize. Acc. Chem. Res. 2007, 40 (3), 197−205. (85) Jain, A; Purohit, C. S.; Verma, S.; Sankararamakrishnan, R. Close contacts between carbonyl oxygen atoms and aromatic centers in protein structures: π···π or Lone-Pair···π Interactions? J. Phys. Chem. B 2007, 111 (30), 8680–8683. (86) Askew, B.; Ballester, P.; Buhr, C.; Jeong, K. S.; Jones, S.; Parris, K.; Williams, K.; Rebek, J., Jr. Molecular recognition with convergent functional groups. VI. Synthetic and structural studies with a model receptor for nucleic acid components. J. Am. Chem. Soc. 1989, 111 (3), 1082−1090. (87)

Tsuzuki, S.; Honda, K.; Uchimaru, T.; Mikami, M.; Tanabe, K. Origin of the attraction

and directionality of the NH/π interaction: Comparison with OH/π and CH/π interactions. J. Am. Chem. Soc. 2000, 122 (46), 11450–11458

33



(88)

Grimme, S.; Huenerbein, R.; Ehrlich, S. On the importance of the dispersion energy for

the thermodynamic stability of molecules. ChemPhysChem 2011, 12(7), 1258−1261. (89) Jiang, L. H.; Liu, Y. G.; Liu, S. B.; Zeng, G. M.; Hu, X. J.; Hu, X.; Guo, Z.; Tan, X. F.; Wang, L. L.; Wu, Z. B. Adsorption of estrogen contaminants by graphene nanomaterials under natural organic matter preloading: Comparison to carbon nanotube, biochar, and activated carbon. Environ. Sci. Technol. 2017, 51 (11), 6352–6359. (90) Sedlak, D. L. Professor Einstein and the quantum mechanics. Environ. Sci. Technol. 2015, 49 (5), 2585–2585.

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