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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
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Unveiling Adsorption Mechanisms of Organic Pollutants onto Carbon
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Nanomaterials by DFT Computations and pp-LFER Modeling
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Ya Wang,†,‡ Jingwen Chen,†, * Xiaoxuan Wei,‡
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Arturo J Hernandez Maldonado,§ Zhongfang Chen ‡,*
6 7
†
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Environmental Science and Technology, Dalian University of Technology, Linggong Road 2,
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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
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§
Department of Chemical Engineering, University of Puerto Rico, Mayagüez, PR 00681,
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USA
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Table of Contents Graphic
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ABSTRACT
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Predicting adsorption of organic pollutants onto carbon nanomaterials (CNMs) and
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understanding the adsorption mechanisms are of great importance to assess the environmental
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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.
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INTRODUCTION
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Carbon nanomaterials (CNMs), including carbon nanotubes (CNTs)1 and graphene2, have
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shown promising applications in many fields, such as sensors, electronic devices, catalysts,
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composite materials, photochemotherapy, energy storage, and environmental remediation3-10
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due to their remarkable physical and chemical properties. The production of CNMs has been
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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
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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,
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the adsorption mechanisms have been examined by different simulation methods, such as
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quantum mechanics, molecular mechanics, molecular dynamics and Monte Carlo simulation,
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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
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ambiguous. Moreover, the differences in adsorption mechanisms between gaseous and
59
aqueous phases are not well understood.
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Regarded as carbon nanotubes with an infinite diameter (D), graphene has different
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adsorption ability when compared with CNTs. Though the impact of diameter and chirality
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on the adsorption energy of some small molecules toward CNTs has been discussed,39-41 no
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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.
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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
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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.
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Herein we employed the density functional theory (DFT) computation to predict the
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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
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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
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most representative molecules onto SWNTs with different chirality and curvatures, and
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extrapolated the adsorption on graphene from the fitting equations. These developed models
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may pave a promising way for predicting the adsorption on CNMs with in silico techniques.
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COMPUTATIONAL SECTION
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Adsorbates and Adsorbent Models
89
In this study, 38 different aliphatic and aromatic compounds (Table 1) were chosen as the
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adsorbate models. Their large variety of the functional groups allow us to investigate
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substituent effects on the adsorption towards pristine graphene in gaseous and aqueous phases.
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Besides pristine graphene, we also examined different nanotube adsorbent models, including
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seven armchair SWNTs, namely, (4, 4), (5, 5), (6, 6), (7, 7), (8, 8), (9, 9) and (10, 10) SWNTs,
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and seven zigzag SWNTs, namely, (6, 0), (7, 0), (8, 0), (9, 0), (10, 0), (11, 0) and (12, 0)
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SWNTs, for investigating the impact of curvature and chirality of SWNTs on the gaseous
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adsorption energies. In the computation, the graphene (8 × 8 × 1) supercell includes 128
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carbon atoms; while the supercells of armchair and zigzag SWNTs contain seven and four
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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
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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
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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
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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
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(GGA-PBE)53 was used to describe the exchange-correlation term. The double-numerical
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basis with polarization functions (DNP),54,55 which is comparable to Pople’s 6-31G** basis
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set, 56 was adopted. To accurately describe the long-range electrostatic interactions, the
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PBE+D2 method with the Grimme van der Waals (vdW) correction was utilized.57 A 4×4×1
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and a 1×1×6 k-point sampling were used for graphene and SWNTs, respectively, and a
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Methfessel-Paxton smearing of 0.005 Ha58 was applied for the Brillouin-zone integration.
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The phenyl (or cyclohexane) planes of adsorbates were initially placed parallel to the surfaces
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of carbon nanostructures for geometry optimizations.59,60
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Due to the complexity of the realistic aqueous environment, it is impractical to include
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thousands of solvent models explicitly in our DFT computations. Instead, we chose the
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popular conductor-like screening model (COSMO) 61 to implicitly simulate the aqueous
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environment (with the dielectric constant of water, 78.54) and the hexadecane environment
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(with the dielectric constant of n-hexadecane, 2.06). In COSMO, the solvent is represented by 7
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a dielectric continuum, in which there is a cavity with the shape of solute molecule, and the
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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
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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,
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Coulomb energy, exchange and correlation energies.63,64 The subscript G-X stands for the
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graphene-compound complex system, while G represents graphene and X stands for the
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adsorbates. The adsorption energies on SWNTs were defined similarly. For clarity, we used
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the absolute values of the adsorption energy (|Ead|) for discussions. According to our
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definition, the larger the |Ead| value, the stronger the interactions between the adsorbates and
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graphene or SWNTs are.
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Abraham Descriptors for pp-LFERs
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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
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logK = eE + sS + aA + bB + vV + c
(2)
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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
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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
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units of (cm3 mol-1)/100; and L, the logarithmic hexadecane-air partition coefficient. e, s, a, b,
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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
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UFZ-LSER database (https://www.ufz.de/index.php?en=31698).
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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
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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.
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Hence, the nonspecific interactions cannot be fully separated by pp-LFERs. Note that the
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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.
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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
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pp-LFERs Development and Characterization
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|Ead| values for the 38 compounds were used for developing pp-LFER models. The
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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
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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
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n = 38, R2adj = 0.917, RMSE = 1.341, Q2LOO = 0.858, Q2BOOT = 0.762 10
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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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models cover the aliphatic and aromatic compounds with the substituents -NH2, -CH3, -NO2,
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-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)
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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]
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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%
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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
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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
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With more functional groups, these compounds have stronger interaction with graphene than
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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
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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
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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
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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
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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
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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
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