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Linear solvation energy relationship to predict the adsorption of aromatic contaminants on graphene oxide
Chemosphere 185 (2017) 826e832
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Linear solvation energy relationship to predict the adsorption of aromatic contaminants on graphene oxide Sujie Shan a, b, Ying Zhao a, b, *, Huan Tang a, b, Fuyi Cui a, b, ** a b
State Key Laboratory of Urban Water Resource and Environment, Harbin, 150090, China School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, 150090, China
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
LSER model was employed to predict the adsorption capability of aromatic contaminants on graphene oxide for the first time. The model we established performed well with high fitness and predictability. The cavity formation and dispersion forces, and hydrogen-bond interactions were the predominant mechanisms controlling the adsorption of aromatic contaminants by graphene oxide.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 February 2017 Received in revised form 1 June 2017 Accepted 13 July 2017 Available online 14 July 2017
In this study, adsorption capability of aromatic contaminants on graphene oxide (GO) was predicted using linear solvation energy relationship (LSER) model for the first time. Adsorption data of 44 aromatic compounds collected from literature and our experimental results were used to establish LSER models with multiple linear regression. High value of R2 (0.919), strong robustness (Q2Loo ¼ 0.862), and desirable predictability (Q2ext ¼ 0.834) demonstrated the model worked well for predicting the adsorption of small aromatic contaminants (descriptor V＜3.099) on GO. The adsorption process was governed by the ability of cavity formation and dispersion forces captured by vV and hydrogen-bond interactions captured by bB. Effect of equilibrium concentrations and properties of GO on the model were explored; and the results indicated that upon an increase of equilibrium concentration, the values of regression coefficients (a, b, v, e, and s) changed at different levels. The oxygen content normalization of logK0.001 decreased the value of b dramatically; however, no obvious changes of the model deduced by the surface area normalization of logK0.001 were witnessed. Overall, our study showed that LSER model provided a potential approach for exploring the adsorption of organic compounds on GO. © 2017 Elsevier Ltd. All rights reserved.
Handling Editor: I. Cousins Keywords: LSER model Aromatic contaminants Adsorption Graphene oxide
1. Introduction
* Corresponding author. State Key Laboratory of Urban Water Resource and Environment, Harbin, 150090, China. ** Corresponding author. State Key Laboratory of Urban Water Resource and Environment, Harbin, 150090, China. E-mail addresses: [email protected] (Y. Zhao), [email protected] (F. Cui). http://dx.doi.org/10.1016/j.chemosphere.2017.07.062 0045-6535/© 2017 Elsevier Ltd. All rights reserved.
Graphene oxide (GO), which possesses large specific surface area and contains a variety of oxygen-containing functional groups, has attracted strong interests as a promising adsorbent in recent years. Numerous adsorption experiments have been conducted to explore the adsorption behaviors of organic compounds (OCs),
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including polycyclic aromatic hydrocarbons (Wang et al., 2014b), antibiotics (Gao et al., 2012), dyes (Ramesha et al., 2011), pesticides (Maliyekkal et al., 2013), and benzene derivatives (Pei et al., 2013; Wang et al., 2014a) on GO, and a fundamental understanding of the adsorption is required to promote the application and evaluate the environmental fate of GO and OCs. However, the currently available experimental adsorption data involved only a small fraction of the approximately 100,000 anthropogenic pollutants registered in European market over past 30 years (Whitaker et al., 2009), which is insufficient to predict the behavior of OCs. It is a time-consuming and laborious process to experimentally determine all the adsorption data for individual compounds. Furthermore, the toxicity of GO (Hu and Zhou, 2013) and organic compounds tends to bring poisonousness to the testers. Therefore, constructing models that can be utilized to predict the adsorption behaviors of OCs on GO is of great significance. Other information, such as adsorption mechanism, can also be obtained from these models. Quantitative structure-activity relationship (QSAR) model has been widely used to predict the adsorption capability of OCs on various adsorbents, and was suggested to enable the link between compounds activity and a range of property descriptors of OCs (de Ridder et al., 2010; Kennicutt et al., 2016; Wu et al., 2016). Among those QSAR models, LSER model based on solvation effects has been proposed as a relatively helpful and effective approach to develop the predictive correlations (Wu et al., 2016). Several LSER models have been used to establish the predictive relationships for adsorption of OCs on carbonaceous nanomaterials such as activated carbon (AC) (Shih and Gschwend, 2009), carbon nanotubes (CNTs), covering multi-walled carbon nanotubes (MWCNTs) and singlewalled carbon nanotubes (SWCNTs) (Apul et al., 2013a; Zhao et al., 2014; Yu et al., 2015; Ersan et al., 2016). Nevertheless, there was no research focus on the predictive correlations of OCs on GO which owns a definitely different structure from AC and CNTs. In order to fully understand the adsorption characteristics of OCs on GO, a comprehensive research concerning the adsorbate solvation descriptors and adsorbent properties is urgently needed. In this study, LSER model was utilized to develop predictive models for adsorption of OCs on GO synthesized by Hummers method. Adsorption data from literature and our experimental measurement including 44 OCs with diverse structures (aqueous solubility Cs ranging from 0.002 mg/L to 80,190 mg/L) were used to build and validate the models by means of multiple linear regression (MLR) analysis. Moreover, LSER models at different equilibrium concentrations (Ce) were generated to examine the concentration effects, other models built with oxygen content and surface area normalized adsorption descriptors were generated to explore the differences caused by GO properties. A term-by-term analysis that combined regression coefficients with adsorbate solvation descriptors offered new insights to interpret the relative contributions of various interaction mechanisms during sorption process. 2. Materials and methods 2.1. LESR model The approach of LSER model is a generalized treatment of solvation effects which assumes attractive solute-solvent interactions are frequently of two kinds: specific hydrogen-bond complex formation and non-specific dipolarity/polarizability (Taft et al., 1985). It has been widely used to quantify, correlate and rationalize complex intermolecular forces based on a range of solvation descriptors and partition coefficients (Kamlet et al., 1983; Goss, 2005), and to capture the cavity formation, dipolar interactions and
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hydrogen bonding interactions. The LSER model was calculated by the following equation: logK ¼ aA þ bB þ vV þ eE þ sS þ C where A, B, V, E, and S are the adsorbate solvation descriptors of the studied OCs. A and B are overall hydrogen-bond acidity and basicity, which represent the ability of donating and accepting protons, respectively. V (cm3$mol1)/100 is the McGowan characteristic volume, which can be employed as a “adsorbate size” parameter, to describe the cavity formation and dispersion interactions. E (cm3/ 10) is the excess molar refraction, which can capture the dispersion forces of n- and p-electrons. S is the dipolarity and polarizability, representing the interactions involve bond and induced dipoles. Since the adsorbate volume is always correlates with molar refraction and polarizability (Arey et al., 2005; Endo and Goss, 2014), it is impossible to distinguish exactly the diverse interactions relevant to dispersion forces (captured by V, E, S). Constant C is the logK-dependent offset obtained as a regression and served as complementary interactions that are unable to be interpreted by the above-mentioned effects. The regression coefficients (a, b, v, e, s) are utilized to characterize the “differences” between water and adsorbent to interact with adsorbate. All adsorbate solvation descriptors were collected from Absolv module of ACD/I-Lab (https://ilab.acdlabs.com/iLab2/). 2.2. Adsorption coefficients calculation The adsorption coefficients (K, L/g) of OCs were calculated by measuring the quantities of OCs absorbed on GO (Qe, mg/g) and the equilibrium concentrations in aqueous phase (Ce, mg/L) through the equation K ¼ Qe/Ce. The logarithmic values of K (logK) were scaled to describe the adsorption capability. Considering the nonlinear adsorption was frequently observed for OCs on GO surface due to the heterogeneous adsorption sites such as wrinkles(Wang et al., 2014b), K values would alter with the changes of concentration. In order to investigate the effect of concentration on K values and the corresponding LSER models, we calculated different logK values under three equilibrium concentrations (Ce ¼ 0.001, 0.01, and 0.1 of the adsorbate aqueous solubility at 25 C), and marked as logK0.001, logK0.01 and logK0.1, respectively. To account for the differences in the oxygen contents (from 15.80% to 50.90%) and surface areas (from 65 m2/g to 576 m2/g) of different GO, adsorption coefficients were also normalized with the oxygen contents and surface areas of various GO. 2.3. Dataset splitting Previous adsorption research mainly focused on the decontamination of aromatics, and the adsorption data for aliphatic OCs is insufficient and inaccessible, therefore, our modeling efforts were focused on the aromatics in this work. The adsorption data involved 44 aromatic contaminants, with 39 of them collected from 21 literature and 5 from our experimental measurements (Text S1, Figs. S1eS2, Table S1). To ensure the accuracy of the adsorption data, the 21 literature we chose were reviewed rigorously, and the selection criteria for the adsorption data were shown in Text S2. In order to achieve appropriate validation of the model, we split all aromatic compounds into the training (T) and the validation set (V). The splitting principle is summarized as follows: (1) the available data of 44 compounds were sorted based on the increasing logK values; (2) To extend the scope of adsorbate solvation descriptors, tetracycline with the largest descriptor V and benzene with the lowest V value were selected into T set; (3) to ensure that the compounds of V set are equally distributed within
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the range of logK values in T set, the rest 42 compounds were split into two sets followed the pattern: T-T-T-T-V-T-T-T-T-V- …. The detailed information of contaminants along with their adsorption coefficients were listed in Table S2. 2.4. Multiple linear regression € MLR analysis using SPSS22.0 software (Ozdemir et al., 2011) was employed to develop LSER models. F-value was used to evaluate the statistical significance of the model, and high values are of interest; t-test was performed to calculate the confidence intervals along with p value, which illustrated the significance of descriptors to the entire model; variance inflation factor (VIF) inspection was examined to detect the possible multicollinearity among independent variables, with higher VIF value indicated higher correlations and a VIF value above 10 can be considered as an indication of multicollinearity (Apul et al., 2013a). Determinations of coefficient R2 and the root mean square error of calibration RMSEC were employed to evaluate the goodness-offit of the model. Internal cross-validation (leave-one-out technique, LOO) and external validation were applied to assess the robustness and to predict the performance. Williams plots were used to describe the applicability domain (AD). All examination methods were described in Text S3. 3. Results and discussion 3.1. Development of the LSER models The range of adsorbate solvation descriptors (A from 0.38 to 1.73, B from 0.03 to 3.27, V from 0.72 to 3.099, E from 0.56 to 3.36 and S from 0.69 to 3.59) for the aromatic contaminants in the T set were listed in Table S2. Generally, highly polar chemicals (e.g., B > 1.5) are usually large in V, because they contain multiple polar functional groups that also increase the size. Meanwhile, small compounds rarely have very high S, A, and B values (Endo and Goss, 2014). In order to inspect the validity of the T set for LSER model construction, VIF values were calculated and were found to be less than 10.0, implying that there was no multicollinearity between adsorbate solvation descriptors, and the T set was appropriate to be employed to build LSER models on GO. Besides, it was suggested that there was a cutoff value for adsorbate molecular weight (220 g/mol) in the modelling of OCs adsorption by CNTs (Ersan et al., 2016) and molecular weight that was larger than 220 g/mol would exert a negative effect on the performance of the model (R2). However, this effect was not observed upon the introduction of the compounds whose Mw are larger than 220 g/mol when we established LSER models with GO (Table S3). Considering the existing LSER models for OCs adsorption on CNTs and AC were built under logK0.001 level (Xia et al., 2010; Apul et al., 2013a; Yu et al., 2015; Ding et al., 2016), and the available adsorption data was relatively sufficient than other concentrations, logK0.001 values were chose as an example to develop the LSER model in this section. The fitting equation of the T set was:
between the number of training compounds and adsorbate descriptors (7.2:1) was higher than the minimum criterion (5:1) (Topliss and Edwards, 1979), R2 and Q2 (including Q2Loo and Q2ext) were greater than 0.70, and the difference between R2 and Q2 did not exceed 0.3, and the value of RMSEC was small, demonstrating that the established model possessed good fitting capability, strong robustness, and desirable predictability (Eriksson et al., 2003; Tropsha, 2010). Besides, F value was greater than F0.01(5, 30) ¼ 3.699, indicating that independent variables were meaningful in predicting logK values at 99% level of significance. And pvalue (below 0.05 significance level) showed adsorbate solvation descriptors (A, B, V, E) were statistically significant to logK value, shown in Table S3 (statistical results). Descriptor S had a p-value of 0.744, well above 0.05 and thus should be excluded from the model as a result of insignificance. Although in statistical terms there is sometimes little benefit in using more than four of the descriptors, there is a big practical advantage because the equations for different properties can be directly compared (Zhao et al., 2001). Therefore, all five descriptors were retained to establish LSER model in our study. The plot of observed versus predicted logK0.001 values for the T and V set was shown in Fig. 1, which includes a perfect prediction line (y ¼ x) and two dotted lines representing 0.5 log unit deviation range. All adsorption data were equally distributed around the perfect prediction line, indicating a high quality of the LSER model to predict the adsorption of aromatics on GO. Deviations of the scatter, which also witnessed by other research (Apul et al., 2013a; Yu et al., 2015), could be attributed to the discrepancy existed in the experimental conditions and adsorbing materials in different articles. The applicability domain of the T and V sets was shown in Fig. 2. The standardized residuals of all contaminants were within three standard deviation units (±3s), neither outliers nor wrong predictions of logK0.001 values were observed. For h value, two contaminants (4-n-nonylphenol and tetracycline) in T set showed larger values than threshold value h* (0.50), but they displayed small residuals simultaneously, implying that tetracycline and 4-nnonylphenol could be termed “good high leverage points” as it has been formerly illustrated. These points can stabilize the established model and make it more accurate to predict the adsorption of aromatic compound with different structures on GO (Jaworska et al.,
logK0.001 ¼ (2.289 ± 0.230)V (1.927 ± 0.24532)B (0.380 ± 0.190) A þ (0.934 ± 0.198)E þ (0.060 ± 0.183)S (1.715 ± 0.227) (n ¼ 36, R2 ¼ 0.919, R2adj ¼ 906, RMSEC ¼ 0.300, F ¼ 68.21, Q2Loo ¼ 0.862, Q2ext ¼ 0.834) The residual analysis (Fig. S3) showed that there was no deviation for the adsorbate descriptors and the observed logK0.001. Ratio
Fig. 1. The observed versus predicted logK0.001 values of the training and validation set for aromatic contaminants adsorption by GO. The predicted logK0.001 values correlated well (R2 ¼ 0.919) with the observed values of the training compounds.
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Fig. 2. Williams plot for verifying the applicability domain of the LSER model. The horizontal dotted lines represent ±3 units standardized residuals (s) and the vertical dotted line represents warning Hat value (h*).
2005; Yu et al., 2015). And if the observed residues are large, the high leverage points will not fit the model. Overall, the dataset investigated in this model was still limited and diverse from different labs, more logK values of various compounds are necessary to develop the predictive model accurately. 3.2. Interpretation of the LSER models The corresponding coefficients (a, b, v, e, s) obtained through MLR analysis reflected the “differences” between adsorbent GO and water phase to interact with adsorbate (shown in Fig. 3). Generally, a coefficient of zero indicated that the respective interaction is equal in both phases but does not tell how strong the actual interaction may be (Endo and Goss, 2014). Descriptor V was found to be the most significant factor with a coefficient v of 2.289 and was positively correlated the logK0.001 value, according well with previous studies conducted on CNTs and
Fig. 3. The regression coefficients (a, b, v, e, and s) of the LSER models developed for logK0.001 and oxygen content normalized logK0.001,oc, and surface area normalized logK0.001,SA for GO.
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AC (Shih and Gschwend, 2009; Xia et al., 2010; Hüffer et al., 2014; Yu et al., 2015). The reason might be as follows: V term was referred to as cavitation and dispersion-repulsion energies, and the magnitude of v was a measure of the relative ease of separating solvent molecules and forming a cavity of suitable size to accommodate the adsorbate (Cossi et al., 2003). The aqueous solubility of adsorbate on a cavity term was proportional to its molecular volume (Kamlet et al., 1987), therefore, the increasing V of adsorbate would decrease the water solubility and thus enhance the dispersion interactions between adsorbate and adsorbent. Large and positive v value indicates that the adsorption capability onto GO enhanced with the increase of the adsorbate size. Descriptor B was the second most influential factor in the LSER model with a coefficient b of 1.927. B was negatively correlated with logK0.001 values, indicating that water had a much stronger ability to donate protons to aromatic compounds in a solutesolvent hydrogen bond than GO, thus expediting the dissolution of adsorbate and weakening the adsorption process (Yang et al., 2013). This was generally acceptable because functional groups on GO is far from adequate to offer as many protons as the abundant water does in aquatic environment. Besides, surface sites containing acidic groups of GO are more favorable to combine with water molecules than adsorbate by hydrogen-bond interactions and to form water molecule clusters. Thus, the formed water molecule clusters can suppress the direct interactions of GO with adsorbate and exclude the adsorption of adsorbate on these sites through steric hindrance (Wu et al., 2012; Apul et al., 2013b). The result was consistent with the existing LSER models concerning the adsorption of OCs on CNTs (Xia et al., 2010; Nam et al., 2015; Ersan et al., 2016), which also had negative coefficient for B. Descriptor E was the third strong factor in the model with a positive value of 0.934. The e coefficient referred to the difference in the ability of GO and water phase to interact with n- or p-electrons of the adsorbate. Positive e value indicated that the polarizable adsorbate favored stronger dispersive interactions with GO than with water molecules. The p electrons located at the surface and edges of large aromatic molecules and GO would be available for electron-donor-acceptor interactions. Descriptor A had a slightly negative value (a ¼ 0.380), implying that the hydrogen-bond accepting capability of adsorbent GO is weaker than water. Earlier research for CNTs also revealed the negative value (Xia et al., 2010). When water is abundant, adsorbatewater and GOwater H-bonds exist in strong competition with adsorbateGO H-bonds. Only adsorbateGO H-bonds that are much stronger than adsorbatewater and waterGO, H-bonds can contribute substantially to overall adsorption. Descriptor S was shown to be a statistically insignificant factor for the contaminants adsorption. The insignificant and minimal s value indicated that dipole-type interactions contribute little to the contaminants adsorption by GO. This could be ascribed to the following reasons: the s coefficient reflects the difference in the ability of GO and water phase to take part in dipoleedipole and dipole-induced-dipole interactions with adsorbate; since water is highly dipolar (Cheong and Carr, 1988), the strong solute-solvent dipole-type interactions would inhibit the adsorption of adsorbate; the interaction exhibited by S term cannot be separated entirely from that of V and E, thus it might have been included in other interactions, meanwhile some interactions between GO and adsorbate might be masked by their stronger interactions with water. From other perspective, both aqueous solubility and adsorption affinity to adsorbent may increase with polarizability, descriptor S does not significantly influence the adsorption (Shih and Gschwend, 2009; Apul et al., 2013a). Generally, examination of coefficients is not enough to assess the contributions of different interactions, only a term-by-term
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analysis that taken both the coefficients and adsorbate descriptors into consideration could allow the contribution of each term to be found. The interaction terms (aA, bB, vV, eE, sS) of the model were employed to describe the relative contributions (listed in Table S4). As is indicated by the coefficient, in the given adsorbate-adsorbentwater system, the cavity formation and dispersion interaction captured by vV term were the most significant factors that dominate the contaminants adsorption by GO (shown in Fig. 4). Two important factors that influence logK values are hydrogen-bond interactions captured by bB term, and p/n-electron dispersion forces captured by eE term, with adsorbate hydrogen-bond acidity term aA and dipole-type interactions term sS playing with almost no part. It should be noted that the minor contributions of aA term might be caused by the absence of hydrogen donating ability (i.e. A ¼ 0) of many compounds, whereas the inaction of dipole-type interactions (sS) were influenced by combined effects discussed as coefficient s. 3.3. Influence of equilibrium concentrations towards LSER models To explore the effects of concentration on aromatic contaminants adsorption modeling, same models were developed at higher equilibrium concentrations (Ce ¼ 0.01Cs and Ce ¼ 0.1Cs). The measured versus predicted logK values were plotted to evaluate the performance of LSER models (Fig. S4). As shown in Fig. 5, the regression coefficients varied at different levels with the increase of Ce. The main differences can be summed up as follows: (1) v increased from 2.289 to 2.631, b decreased slightly from 1.927 to 2.248, but this two coefficients were always predominant in the LSER models across an adsorption isotherm; (2) e decreased from 0.934 to 0.221, s increased from 0.060 to 0.689, with a changed a little from 0.380 to 0.147; (3) the value of constant C declined significantly from 1.715 to 2.597. From one perspective, the changes in coefficients with the increasing Ce might be partly due to the different number of contaminants isotherm data that is available for modeling (Ersan et al., 2016). From another angle, these changes could be explained by the complex adsorption process, and it was speculated that there are some evolutions of the intermolecular interactions among adsorbate, water molecules,
Fig. 4. Box and whisker plot for the relative contributions of various interaction terms to the overall adsorption (logK0.001). The empty circles(ο) and asterisks(*) represent the mild outliers and extreme values, respectively. The nonspecific interactions (i.e., cavity formation and dispersion interactions) captured by vV and eE terms played positive roles on the adsorption, whereas the specific hydrogen-bond interaction captured by bB played negative roles on the adsorption of aromatics by GO.
Fig. 5. The regression coefficients (a, b, v, e, and s) curve of the LSER models under different equilibrium concentrations.
and GO surface. Under low Ce system, adsorbate molecules would occupy the most energetic favorable sorption sites, with water molecules adsorbed on less favorable sites. Under high Ce system, the competition between adsorbate molecules on active sorption sites was great, thus rendering some adsorbate molecules to adsorbed on less energetic sites which was occupied by water molecules in low Ce system (Zhao et al., 2014; Yu et al., 2015). This would lead to an increase of v, s, and e coefficient and a decrease of a and b coefficient. Under higher Ce system, the sorption sites were insufficient, and the interactions between adsorbate molecules will be enhanced. The competitive adsorption process is more favorable for highly polarizable contaminants, thus resulting in a decrease of b coefficient. The increasing strengths of dipole-type interaction (s increased) was speculated to be attributed to the exposure of pelectron clouds and the corresponding increase in density of p electron onto adsorbate surface would favor the adsorption from water. For the decrease of e coefficient, it is hard to explain it clearly. One possible deduction is that there are some overlaps among the interactions captured by term eE, vV, and sS (Endo and Goss, 2014), and the dispersion interactions with n/p-election might be screened by other strong interactions. The significant decline of constant C indicated the increasing importance of other unknown adsorption interactions which cannot be interpreted by the above descriptors. The negative value revealed the inhibition effects which weaken the adsorption capability on GO. This would be an overall result of the complicated interactions among adsorbed molecules, water molecules, free adsorbate molecules, and GO surface. The interactions between free adsorbate and absorbed molecules might enlarge a lot with the increase of Ce and then pull the adsorbed molecules away from GO surface (Ding et al., 2016). The interaction terms of LSER models at Ce ¼ 0.01Cs and Ce ¼ 0.1Cs were listed in Table S5 and Fig. S5. At different saturation levels, the cavity formation and dispersion forces captured by vV and hydrogen-bond interactions captured by bB were always dominant in the adsorption process. The eE term always contributed positively to the adsorption.
3.4. Influence of GO properties on LSER models It was reported that the adsorption capability of GO towards OCs
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depends not only on the properties of adsorbate, but also on the nature of GO surface (Li et al., 2013; Sharma et al., 2013). The production of GO involves the introduction of oxygenated functional groups to the graphene basal plane and edges, which will break the p-conjugated network and result in a highly water-dispersible sheets, thus enlarging GO's surface area and providing more active adsorption sites (Kim et al., 2012; Yan et al., 2014). To account for the difference in the nature of GO, same models with oxygen content normalized logK0.001 and surface area normalized logK0.001 were developed (Tables S6eS7). The plots of observed normalized logK values versus predicted logK were shown in Figs. S6eS7. As shown in Fig. 3, certain changes were observed when we established the LSER model with oxygen content normalized logK0.001. A significant change is that the value of coefficient b reduced dramatically and s decreased a lot to a negative value. This can be interpreted as that the functional groups of GO surface contribute a lot to the adsorption of aromatic compound on GO. Oxygen content normalization of logK0.001 suppressed the hydrogen-bond interactions which was supposed to influence the aromatic contaminants adsorption. The decrease in s might also be a result of the decrease of hydrophilic sites deduced by functional groups, indicating that oxygen content was a limiting factor for the adsorption of aromatic contaminants on GO. On the other hand, surface area normalization of logK0.001 failed to improve the performance of LSER model and only minor changes on the regression coefficients were observed. Upon the introduction of normalized logK0.001, the value of a changed from negative to positive and the relative magnitude of each coefficient not varied, demonstrating the change was caused by the different number of available adsorption data for modeling. In dilute saturation (Ce ¼ 0.001 Cs), the surface of GO is larger than the size of adsorbate molecules and will provide sufficient adsorption sites for them. Therefore, surface area of GO was not a limiting factor for aromatic contaminants adsorption at low concentrations, which was also observed in previous research (Apul et al., 2013a). The interaction terms of LSER models were listed in Table S8 and Fig. S8, the change of various interactions followed the tendency indicated by the coefficient discussed above. 3.5. Comparison the finding with AC and CNTs Recent years, LSER models have attracted tremendous attention for the prediction of OCs adsorption by carbonaceous adsorbents such as CNTs and AC (Table S9), and this motivated our modeling efforts based on GO adsorption. Even though there existed large differences among the morphologies and properties of these carbon nanoadsorbents, the regression coefficients was found to follow the same tendency, illustrating that the aromatics adsorption by GO is influenced by similar interactions. Due to the similar structure of carbon planar, similar findings were also proposed in the existing LSER models (Hüffer et al., 2014; Yu et al., 2015). However, obvious differences on the magnitude of regression coefficients were observed for these models (Table S9); specifically, smaller v and larger b were observed for GO than that of CNTs and AC. Possible explanations could be summarized as: (1) The chemical composition of GO is different from that of CNT, which contains larger amount of oxygen-containing functional groups (such as eOH and eCOOH). The oxygen content of GO synthesized by Hummers method is 20 to 30% (Dave et al., 2016), while the oxygen content of CNT reported in literature varied from 1.6% to 11.3% (Khan et al., 2013). The abundant oxygenated functional groups make GO more hydrophilic, which suppressed the cavity formation and the dispersion interactions between GO and adsorbate molecules; in addition, compared to the less oxidized CNTs and AC, the hydrogen bond donating ability to adsorbate molecules were
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strengthened; (2) the difference between the number and type of probe compounds might lead to some deviations of modeling results; (3) the geometry of the adsorbate molecule was not taken into account in the LSER model, which might influence the way of adsorbate molecules interacting with GO (Endo and Goss, 2014; Hüffer et al., 2014); (4) the oxidation of GO might introduce many heterogeneous adsorption sites. It should be noted that it is just a speculation, and the clear explanation is still unavailable. A series of factors such as defect sites, interstitial and groove regions between CNT bundles, AC pore sizes, GO functional groups and morphology of these adsorbents could generate complicated effects on the adsorption capacity and affinity of GO. Besides, it is very hard to introduce an explicit standard to quantify the contribution of each factor. Therefore, more in-depth and comprehensive research considering the aforementioned factors is certainly required. 4. Conclusion Well-behaved LSER predictive models were developed for aromatic contaminants adsorption by GO under different concentrations (Ce ¼ 0.001, 0.01 and 0.1 of adsorbate aqueous solubility). LSER model was employed to predict the aromatic compounds adsorption by GO for the first time, and was proved to be an effective technique in assessing adsorption capability of aromatic contaminants with small adsorbate size (descriptor V < 3.099). The general interactions that influence the adsorption of aromatic compounds by GO from water can be identified as the ease of cavity formation and dispersion forces (vV term), hydrogen-bond interaction (bB term), and n/p-electron interaction (eE term). Certain changes of regression coefficients (a, b, v, e, s) were observed with the increase of Ce. Surface area normalization of logK0.001 did not have a significant influence on the model, and oxygen content normalization of logK0.001 dramatically reduced the hydrogen-bond interaction (bB term) of aromatic compounds adsorption. LSER models provide a novel insight into the simulation of the correlation between adsorption capability and adsorbate solvation descriptors, which contribute a lot to interpret the adsorption mechanisms and provide deeply understandings on the adsorption characteristics of organic compounds on GO that is important to improve the adsorption efficiency. Since the available data was limited and only several aromatic contaminants were included in this study, comprehensive research involving more compounds (such as aliphatic compounds) with a broader range of descriptors and structure differences are urgently needed. In addition, more detailed adsorbent characteristics (such as wrinkles and defects, types of functional groups) and experimental conditions (such as pH, temperatures and ionic strengths) should be taken into consideration, which could ensure the accuracy of the prediction. Furthermore, LSER models should also be extended to other GObased nanomaterials such as graphene nanosheets and GO composites to investigate the difference of adsorption characteristic between these adsorbents. Acknowledgement This paper is supported by National Natural Science Foundation of China (Grant No. 51278147, 71671050, 50808052 and 51408162), HIT Environment and Ecology Innovation Special Funds (No. HSCJ201606), and State Key Laboratory of Urban Water Resource and Environment (Grant No. 2016DX02). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.chemosphere.2017.07.062.
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Comparative study of methylene blue dye adsorption onto activated carbon, graphene oxide, and carbon nanotubes. Chem. Eng. Res. Des. 91, 361e368. Maliyekkal, S.M., Sreeprasad, T.S., Krishnan, D., Kouser, S., Mishra, A.K., Waghmare, U.V., Pradeep, T., 2013. Graphene: a reusable substrate for unprecedented adsorption of pesticides. Small 9, 273. Nam, S.W., Jung, C., Li, H., Yu, M., Flora, J.R.V., Boateng, L.K., Her, N., Zoh, K.D., Yoon, Y., 2015. Adsorption characteristics of diclofenac and sulfamethoxazole to graphene oxide in aqueous solution. Chemosphere 136, 20e26. € € Ozdemir, U., Ozbay, B., Veli, S., Zor, S., 2011. Modeling adsorption of sodium dodecyl benzene sulfonate (SDBS) onto polyaniline (PANI) by using multi linear regression and artificial neural networks. Chem. Eng. J. 178, 183e190. Pei, Z., Li, L., Sun, L., Zhang, S., Shan, X.Q., Yang, S., Wen, B., 2013. Adsorption characteristics of 1,2,4-trichlorobenzene, 2,4,6-trichlorophenol, 2-naphthol and naphthalene on graphene and graphene oxide. Carbon 51, 156e163. Ramesha, G.K., Kumara, A.V., Muralidhara, H.B., Sampath, S., 2011. Graphene and graphene oxide as effective adsorbents toward anionic and cationic dyes. J. Colloid Interface Sci. 361, 270e277. Sharma, P., Hussain, N., Borah, D.J., Das, M.R., 2013. Kinetics and adsorption behavior of the methyl blue at the graphene oxide/reduced graphene oxide nanosheetewater interface: a comparative study. J. Chem. Eng. Data 58, 3477e3488. Shih, Y.-h., Gschwend, P.M., 2009. Evaluating activated CarbonWater sorption coefficients of organic compounds using a linear solvation energy relationship approach and sorbate chemical activities. Environ. Sci. Technol. 43, 851e857. Taft, R.W., Abboud, J.-L.M., Kamlet, M.J., Abraham, M.H., 1985. Linear solvation energy relations. J. Solut. Chem. 14, 153e186. Topliss, J.G., Edwards, R.P., 1979. Chance factors in studies of quantitative structureactivity relationships. J. Med. Chem. 22, 1238e1244. Tropsha, A., 2010. Best practices for QSAR model development, validation, and exploitation. Mol. Inf. 29, 476e488. Wang, F., Haftka, J.J., Sinnige, T.L., Hermens, J.L., Chen, W., 2014a. Adsorption of polar, nonpolar, and substituted aromatics to colloidal graphene oxide nanoparticles. Environ. Pollut. 186, 226e233. Wang, J., Chen, Z., Chen, B., 2014b. Adsorption of polycyclic aromatic hydrocarbons by graphene and graphene oxide nanosheets. Environ. Sci. Technol. 48, 4817e4825. Whitaker, J., Chaplow, J.S., Potter, E., Scott, W.A., Hopkin, S., Harman, M., Sims, I., Sorokin, N., 2009. The comparative toxicity to soil invertebrates of natural chemicals and their synthetic analogues. Chemosphere 76, 345e352. Wu, W., Chen, W., Lin, D., Yang, K., 2012. Influence of surface oxidation of multiwalled carbon nanotubes on the adsorption affinity and capacity of polar and nonpolar organic compounds in aqueous phase. Environ. Sci. Technol. 46, 5446e5454. Wu, W., Yang, K., Chen, W., Wang, W., Zhang, J., Lin, D., Xing, B., 2016. Correlation and prediction of adsorption capacity and affinity of aromatic compounds on carbon nanotubes. Water Res. 88, 492e501. Xia, X.R., Monteiro-Riviere, N.A., Riviere, J.E., 2010. An index for characterization of nanomaterials in biological systems. Nat. Nanotechnol. 5, 671. Yan, H., Tao, X., Yang, Z., Li, K., Yang, H., Li, A., Cheng, R., 2014. Effects of the oxidation degree of graphene oxide on the adsorption of methylene blue. J. Hazard. Mater. 268, 191e198. Yang, X., Li, J., Wen, T., Ren, X., Huang, Y., Wang, X., 2013. Adsorption of naphthalene and its derivatives on magnetic graphene composites and the mechanism investigation. Colloids Surface. A Physicochem. Eng. Aspect. 422, 118e125. Yu, X., Sun, W., Ni, J., 2015. LSER model for organic compounds adsorption by singlewalled carbon nanotubes: comparison with multi-walled carbon nanotubes and activated carbon. Environ. Pollut. 206, 652. Zhao, Q., Yang, K., Li, W., Xing, B., 2014. Concentration-dependent polyparameter linear free energy relationships to predict organic compound sorption on carbon nanotubes. Sci. Rep. 4. Zhao, Y.H., Le, J., Abraham, M.H., Hersey, A., Eddershaw, P.J., Luscombe, C.N., Boutina, D., Beck, G., Sherborne, B., Cooper, I., 2001. Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structureeactivity relationship (QSAR) with the Abraham descriptors. J. Pharm. Sci. 90, 749e784.
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Chemosphere journal homepage: www.elsevier.com/locate/chemosphere
Linear solvation energy relationship to predict the adsorption of aromatic contaminants on graphene oxide Sujie Shan a, b, Ying Zhao a, b, *, Huan Tang a, b, Fuyi Cui a, b, ** a b
State Key Laboratory of Urban Water Resource and Environment, Harbin, 150090, China School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, 150090, China
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
LSER model was employed to predict the adsorption capability of aromatic contaminants on graphene oxide for the first time. The model we established performed well with high fitness and predictability. The cavity formation and dispersion forces, and hydrogen-bond interactions were the predominant mechanisms controlling the adsorption of aromatic contaminants by graphene oxide.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 February 2017 Received in revised form 1 June 2017 Accepted 13 July 2017 Available online 14 July 2017
In this study, adsorption capability of aromatic contaminants on graphene oxide (GO) was predicted using linear solvation energy relationship (LSER) model for the first time. Adsorption data of 44 aromatic compounds collected from literature and our experimental results were used to establish LSER models with multiple linear regression. High value of R2 (0.919), strong robustness (Q2Loo ¼ 0.862), and desirable predictability (Q2ext ¼ 0.834) demonstrated the model worked well for predicting the adsorption of small aromatic contaminants (descriptor V＜3.099) on GO. The adsorption process was governed by the ability of cavity formation and dispersion forces captured by vV and hydrogen-bond interactions captured by bB. Effect of equilibrium concentrations and properties of GO on the model were explored; and the results indicated that upon an increase of equilibrium concentration, the values of regression coefficients (a, b, v, e, and s) changed at different levels. The oxygen content normalization of logK0.001 decreased the value of b dramatically; however, no obvious changes of the model deduced by the surface area normalization of logK0.001 were witnessed. Overall, our study showed that LSER model provided a potential approach for exploring the adsorption of organic compounds on GO. © 2017 Elsevier Ltd. All rights reserved.
Handling Editor: I. Cousins Keywords: LSER model Aromatic contaminants Adsorption Graphene oxide
1. Introduction
* Corresponding author. State Key Laboratory of Urban Water Resource and Environment, Harbin, 150090, China. ** Corresponding author. State Key Laboratory of Urban Water Resource and Environment, Harbin, 150090, China. E-mail addresses: [email protected] (Y. Zhao), [email protected] (F. Cui). http://dx.doi.org/10.1016/j.chemosphere.2017.07.062 0045-6535/© 2017 Elsevier Ltd. All rights reserved.
Graphene oxide (GO), which possesses large specific surface area and contains a variety of oxygen-containing functional groups, has attracted strong interests as a promising adsorbent in recent years. Numerous adsorption experiments have been conducted to explore the adsorption behaviors of organic compounds (OCs),
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including polycyclic aromatic hydrocarbons (Wang et al., 2014b), antibiotics (Gao et al., 2012), dyes (Ramesha et al., 2011), pesticides (Maliyekkal et al., 2013), and benzene derivatives (Pei et al., 2013; Wang et al., 2014a) on GO, and a fundamental understanding of the adsorption is required to promote the application and evaluate the environmental fate of GO and OCs. However, the currently available experimental adsorption data involved only a small fraction of the approximately 100,000 anthropogenic pollutants registered in European market over past 30 years (Whitaker et al., 2009), which is insufficient to predict the behavior of OCs. It is a time-consuming and laborious process to experimentally determine all the adsorption data for individual compounds. Furthermore, the toxicity of GO (Hu and Zhou, 2013) and organic compounds tends to bring poisonousness to the testers. Therefore, constructing models that can be utilized to predict the adsorption behaviors of OCs on GO is of great significance. Other information, such as adsorption mechanism, can also be obtained from these models. Quantitative structure-activity relationship (QSAR) model has been widely used to predict the adsorption capability of OCs on various adsorbents, and was suggested to enable the link between compounds activity and a range of property descriptors of OCs (de Ridder et al., 2010; Kennicutt et al., 2016; Wu et al., 2016). Among those QSAR models, LSER model based on solvation effects has been proposed as a relatively helpful and effective approach to develop the predictive correlations (Wu et al., 2016). Several LSER models have been used to establish the predictive relationships for adsorption of OCs on carbonaceous nanomaterials such as activated carbon (AC) (Shih and Gschwend, 2009), carbon nanotubes (CNTs), covering multi-walled carbon nanotubes (MWCNTs) and singlewalled carbon nanotubes (SWCNTs) (Apul et al., 2013a; Zhao et al., 2014; Yu et al., 2015; Ersan et al., 2016). Nevertheless, there was no research focus on the predictive correlations of OCs on GO which owns a definitely different structure from AC and CNTs. In order to fully understand the adsorption characteristics of OCs on GO, a comprehensive research concerning the adsorbate solvation descriptors and adsorbent properties is urgently needed. In this study, LSER model was utilized to develop predictive models for adsorption of OCs on GO synthesized by Hummers method. Adsorption data from literature and our experimental measurement including 44 OCs with diverse structures (aqueous solubility Cs ranging from 0.002 mg/L to 80,190 mg/L) were used to build and validate the models by means of multiple linear regression (MLR) analysis. Moreover, LSER models at different equilibrium concentrations (Ce) were generated to examine the concentration effects, other models built with oxygen content and surface area normalized adsorption descriptors were generated to explore the differences caused by GO properties. A term-by-term analysis that combined regression coefficients with adsorbate solvation descriptors offered new insights to interpret the relative contributions of various interaction mechanisms during sorption process. 2. Materials and methods 2.1. LESR model The approach of LSER model is a generalized treatment of solvation effects which assumes attractive solute-solvent interactions are frequently of two kinds: specific hydrogen-bond complex formation and non-specific dipolarity/polarizability (Taft et al., 1985). It has been widely used to quantify, correlate and rationalize complex intermolecular forces based on a range of solvation descriptors and partition coefficients (Kamlet et al., 1983; Goss, 2005), and to capture the cavity formation, dipolar interactions and
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hydrogen bonding interactions. The LSER model was calculated by the following equation: logK ¼ aA þ bB þ vV þ eE þ sS þ C where A, B, V, E, and S are the adsorbate solvation descriptors of the studied OCs. A and B are overall hydrogen-bond acidity and basicity, which represent the ability of donating and accepting protons, respectively. V (cm3$mol1)/100 is the McGowan characteristic volume, which can be employed as a “adsorbate size” parameter, to describe the cavity formation and dispersion interactions. E (cm3/ 10) is the excess molar refraction, which can capture the dispersion forces of n- and p-electrons. S is the dipolarity and polarizability, representing the interactions involve bond and induced dipoles. Since the adsorbate volume is always correlates with molar refraction and polarizability (Arey et al., 2005; Endo and Goss, 2014), it is impossible to distinguish exactly the diverse interactions relevant to dispersion forces (captured by V, E, S). Constant C is the logK-dependent offset obtained as a regression and served as complementary interactions that are unable to be interpreted by the above-mentioned effects. The regression coefficients (a, b, v, e, s) are utilized to characterize the “differences” between water and adsorbent to interact with adsorbate. All adsorbate solvation descriptors were collected from Absolv module of ACD/I-Lab (https://ilab.acdlabs.com/iLab2/). 2.2. Adsorption coefficients calculation The adsorption coefficients (K, L/g) of OCs were calculated by measuring the quantities of OCs absorbed on GO (Qe, mg/g) and the equilibrium concentrations in aqueous phase (Ce, mg/L) through the equation K ¼ Qe/Ce. The logarithmic values of K (logK) were scaled to describe the adsorption capability. Considering the nonlinear adsorption was frequently observed for OCs on GO surface due to the heterogeneous adsorption sites such as wrinkles(Wang et al., 2014b), K values would alter with the changes of concentration. In order to investigate the effect of concentration on K values and the corresponding LSER models, we calculated different logK values under three equilibrium concentrations (Ce ¼ 0.001, 0.01, and 0.1 of the adsorbate aqueous solubility at 25 C), and marked as logK0.001, logK0.01 and logK0.1, respectively. To account for the differences in the oxygen contents (from 15.80% to 50.90%) and surface areas (from 65 m2/g to 576 m2/g) of different GO, adsorption coefficients were also normalized with the oxygen contents and surface areas of various GO. 2.3. Dataset splitting Previous adsorption research mainly focused on the decontamination of aromatics, and the adsorption data for aliphatic OCs is insufficient and inaccessible, therefore, our modeling efforts were focused on the aromatics in this work. The adsorption data involved 44 aromatic contaminants, with 39 of them collected from 21 literature and 5 from our experimental measurements (Text S1, Figs. S1eS2, Table S1). To ensure the accuracy of the adsorption data, the 21 literature we chose were reviewed rigorously, and the selection criteria for the adsorption data were shown in Text S2. In order to achieve appropriate validation of the model, we split all aromatic compounds into the training (T) and the validation set (V). The splitting principle is summarized as follows: (1) the available data of 44 compounds were sorted based on the increasing logK values; (2) To extend the scope of adsorbate solvation descriptors, tetracycline with the largest descriptor V and benzene with the lowest V value were selected into T set; (3) to ensure that the compounds of V set are equally distributed within
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the range of logK values in T set, the rest 42 compounds were split into two sets followed the pattern: T-T-T-T-V-T-T-T-T-V- …. The detailed information of contaminants along with their adsorption coefficients were listed in Table S2. 2.4. Multiple linear regression € MLR analysis using SPSS22.0 software (Ozdemir et al., 2011) was employed to develop LSER models. F-value was used to evaluate the statistical significance of the model, and high values are of interest; t-test was performed to calculate the confidence intervals along with p value, which illustrated the significance of descriptors to the entire model; variance inflation factor (VIF) inspection was examined to detect the possible multicollinearity among independent variables, with higher VIF value indicated higher correlations and a VIF value above 10 can be considered as an indication of multicollinearity (Apul et al., 2013a). Determinations of coefficient R2 and the root mean square error of calibration RMSEC were employed to evaluate the goodness-offit of the model. Internal cross-validation (leave-one-out technique, LOO) and external validation were applied to assess the robustness and to predict the performance. Williams plots were used to describe the applicability domain (AD). All examination methods were described in Text S3. 3. Results and discussion 3.1. Development of the LSER models The range of adsorbate solvation descriptors (A from 0.38 to 1.73, B from 0.03 to 3.27, V from 0.72 to 3.099, E from 0.56 to 3.36 and S from 0.69 to 3.59) for the aromatic contaminants in the T set were listed in Table S2. Generally, highly polar chemicals (e.g., B > 1.5) are usually large in V, because they contain multiple polar functional groups that also increase the size. Meanwhile, small compounds rarely have very high S, A, and B values (Endo and Goss, 2014). In order to inspect the validity of the T set for LSER model construction, VIF values were calculated and were found to be less than 10.0, implying that there was no multicollinearity between adsorbate solvation descriptors, and the T set was appropriate to be employed to build LSER models on GO. Besides, it was suggested that there was a cutoff value for adsorbate molecular weight (220 g/mol) in the modelling of OCs adsorption by CNTs (Ersan et al., 2016) and molecular weight that was larger than 220 g/mol would exert a negative effect on the performance of the model (R2). However, this effect was not observed upon the introduction of the compounds whose Mw are larger than 220 g/mol when we established LSER models with GO (Table S3). Considering the existing LSER models for OCs adsorption on CNTs and AC were built under logK0.001 level (Xia et al., 2010; Apul et al., 2013a; Yu et al., 2015; Ding et al., 2016), and the available adsorption data was relatively sufficient than other concentrations, logK0.001 values were chose as an example to develop the LSER model in this section. The fitting equation of the T set was:
between the number of training compounds and adsorbate descriptors (7.2:1) was higher than the minimum criterion (5:1) (Topliss and Edwards, 1979), R2 and Q2 (including Q2Loo and Q2ext) were greater than 0.70, and the difference between R2 and Q2 did not exceed 0.3, and the value of RMSEC was small, demonstrating that the established model possessed good fitting capability, strong robustness, and desirable predictability (Eriksson et al., 2003; Tropsha, 2010). Besides, F value was greater than F0.01(5, 30) ¼ 3.699, indicating that independent variables were meaningful in predicting logK values at 99% level of significance. And pvalue (below 0.05 significance level) showed adsorbate solvation descriptors (A, B, V, E) were statistically significant to logK value, shown in Table S3 (statistical results). Descriptor S had a p-value of 0.744, well above 0.05 and thus should be excluded from the model as a result of insignificance. Although in statistical terms there is sometimes little benefit in using more than four of the descriptors, there is a big practical advantage because the equations for different properties can be directly compared (Zhao et al., 2001). Therefore, all five descriptors were retained to establish LSER model in our study. The plot of observed versus predicted logK0.001 values for the T and V set was shown in Fig. 1, which includes a perfect prediction line (y ¼ x) and two dotted lines representing 0.5 log unit deviation range. All adsorption data were equally distributed around the perfect prediction line, indicating a high quality of the LSER model to predict the adsorption of aromatics on GO. Deviations of the scatter, which also witnessed by other research (Apul et al., 2013a; Yu et al., 2015), could be attributed to the discrepancy existed in the experimental conditions and adsorbing materials in different articles. The applicability domain of the T and V sets was shown in Fig. 2. The standardized residuals of all contaminants were within three standard deviation units (±3s), neither outliers nor wrong predictions of logK0.001 values were observed. For h value, two contaminants (4-n-nonylphenol and tetracycline) in T set showed larger values than threshold value h* (0.50), but they displayed small residuals simultaneously, implying that tetracycline and 4-nnonylphenol could be termed “good high leverage points” as it has been formerly illustrated. These points can stabilize the established model and make it more accurate to predict the adsorption of aromatic compound with different structures on GO (Jaworska et al.,
logK0.001 ¼ (2.289 ± 0.230)V (1.927 ± 0.24532)B (0.380 ± 0.190) A þ (0.934 ± 0.198)E þ (0.060 ± 0.183)S (1.715 ± 0.227) (n ¼ 36, R2 ¼ 0.919, R2adj ¼ 906, RMSEC ¼ 0.300, F ¼ 68.21, Q2Loo ¼ 0.862, Q2ext ¼ 0.834) The residual analysis (Fig. S3) showed that there was no deviation for the adsorbate descriptors and the observed logK0.001. Ratio
Fig. 1. The observed versus predicted logK0.001 values of the training and validation set for aromatic contaminants adsorption by GO. The predicted logK0.001 values correlated well (R2 ¼ 0.919) with the observed values of the training compounds.
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Fig. 2. Williams plot for verifying the applicability domain of the LSER model. The horizontal dotted lines represent ±3 units standardized residuals (s) and the vertical dotted line represents warning Hat value (h*).
2005; Yu et al., 2015). And if the observed residues are large, the high leverage points will not fit the model. Overall, the dataset investigated in this model was still limited and diverse from different labs, more logK values of various compounds are necessary to develop the predictive model accurately. 3.2. Interpretation of the LSER models The corresponding coefficients (a, b, v, e, s) obtained through MLR analysis reflected the “differences” between adsorbent GO and water phase to interact with adsorbate (shown in Fig. 3). Generally, a coefficient of zero indicated that the respective interaction is equal in both phases but does not tell how strong the actual interaction may be (Endo and Goss, 2014). Descriptor V was found to be the most significant factor with a coefficient v of 2.289 and was positively correlated the logK0.001 value, according well with previous studies conducted on CNTs and
Fig. 3. The regression coefficients (a, b, v, e, and s) of the LSER models developed for logK0.001 and oxygen content normalized logK0.001,oc, and surface area normalized logK0.001,SA for GO.
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AC (Shih and Gschwend, 2009; Xia et al., 2010; Hüffer et al., 2014; Yu et al., 2015). The reason might be as follows: V term was referred to as cavitation and dispersion-repulsion energies, and the magnitude of v was a measure of the relative ease of separating solvent molecules and forming a cavity of suitable size to accommodate the adsorbate (Cossi et al., 2003). The aqueous solubility of adsorbate on a cavity term was proportional to its molecular volume (Kamlet et al., 1987), therefore, the increasing V of adsorbate would decrease the water solubility and thus enhance the dispersion interactions between adsorbate and adsorbent. Large and positive v value indicates that the adsorption capability onto GO enhanced with the increase of the adsorbate size. Descriptor B was the second most influential factor in the LSER model with a coefficient b of 1.927. B was negatively correlated with logK0.001 values, indicating that water had a much stronger ability to donate protons to aromatic compounds in a solutesolvent hydrogen bond than GO, thus expediting the dissolution of adsorbate and weakening the adsorption process (Yang et al., 2013). This was generally acceptable because functional groups on GO is far from adequate to offer as many protons as the abundant water does in aquatic environment. Besides, surface sites containing acidic groups of GO are more favorable to combine with water molecules than adsorbate by hydrogen-bond interactions and to form water molecule clusters. Thus, the formed water molecule clusters can suppress the direct interactions of GO with adsorbate and exclude the adsorption of adsorbate on these sites through steric hindrance (Wu et al., 2012; Apul et al., 2013b). The result was consistent with the existing LSER models concerning the adsorption of OCs on CNTs (Xia et al., 2010; Nam et al., 2015; Ersan et al., 2016), which also had negative coefficient for B. Descriptor E was the third strong factor in the model with a positive value of 0.934. The e coefficient referred to the difference in the ability of GO and water phase to interact with n- or p-electrons of the adsorbate. Positive e value indicated that the polarizable adsorbate favored stronger dispersive interactions with GO than with water molecules. The p electrons located at the surface and edges of large aromatic molecules and GO would be available for electron-donor-acceptor interactions. Descriptor A had a slightly negative value (a ¼ 0.380), implying that the hydrogen-bond accepting capability of adsorbent GO is weaker than water. Earlier research for CNTs also revealed the negative value (Xia et al., 2010). When water is abundant, adsorbatewater and GOwater H-bonds exist in strong competition with adsorbateGO H-bonds. Only adsorbateGO H-bonds that are much stronger than adsorbatewater and waterGO, H-bonds can contribute substantially to overall adsorption. Descriptor S was shown to be a statistically insignificant factor for the contaminants adsorption. The insignificant and minimal s value indicated that dipole-type interactions contribute little to the contaminants adsorption by GO. This could be ascribed to the following reasons: the s coefficient reflects the difference in the ability of GO and water phase to take part in dipoleedipole and dipole-induced-dipole interactions with adsorbate; since water is highly dipolar (Cheong and Carr, 1988), the strong solute-solvent dipole-type interactions would inhibit the adsorption of adsorbate; the interaction exhibited by S term cannot be separated entirely from that of V and E, thus it might have been included in other interactions, meanwhile some interactions between GO and adsorbate might be masked by their stronger interactions with water. From other perspective, both aqueous solubility and adsorption affinity to adsorbent may increase with polarizability, descriptor S does not significantly influence the adsorption (Shih and Gschwend, 2009; Apul et al., 2013a). Generally, examination of coefficients is not enough to assess the contributions of different interactions, only a term-by-term
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analysis that taken both the coefficients and adsorbate descriptors into consideration could allow the contribution of each term to be found. The interaction terms (aA, bB, vV, eE, sS) of the model were employed to describe the relative contributions (listed in Table S4). As is indicated by the coefficient, in the given adsorbate-adsorbentwater system, the cavity formation and dispersion interaction captured by vV term were the most significant factors that dominate the contaminants adsorption by GO (shown in Fig. 4). Two important factors that influence logK values are hydrogen-bond interactions captured by bB term, and p/n-electron dispersion forces captured by eE term, with adsorbate hydrogen-bond acidity term aA and dipole-type interactions term sS playing with almost no part. It should be noted that the minor contributions of aA term might be caused by the absence of hydrogen donating ability (i.e. A ¼ 0) of many compounds, whereas the inaction of dipole-type interactions (sS) were influenced by combined effects discussed as coefficient s. 3.3. Influence of equilibrium concentrations towards LSER models To explore the effects of concentration on aromatic contaminants adsorption modeling, same models were developed at higher equilibrium concentrations (Ce ¼ 0.01Cs and Ce ¼ 0.1Cs). The measured versus predicted logK values were plotted to evaluate the performance of LSER models (Fig. S4). As shown in Fig. 5, the regression coefficients varied at different levels with the increase of Ce. The main differences can be summed up as follows: (1) v increased from 2.289 to 2.631, b decreased slightly from 1.927 to 2.248, but this two coefficients were always predominant in the LSER models across an adsorption isotherm; (2) e decreased from 0.934 to 0.221, s increased from 0.060 to 0.689, with a changed a little from 0.380 to 0.147; (3) the value of constant C declined significantly from 1.715 to 2.597. From one perspective, the changes in coefficients with the increasing Ce might be partly due to the different number of contaminants isotherm data that is available for modeling (Ersan et al., 2016). From another angle, these changes could be explained by the complex adsorption process, and it was speculated that there are some evolutions of the intermolecular interactions among adsorbate, water molecules,
Fig. 4. Box and whisker plot for the relative contributions of various interaction terms to the overall adsorption (logK0.001). The empty circles(ο) and asterisks(*) represent the mild outliers and extreme values, respectively. The nonspecific interactions (i.e., cavity formation and dispersion interactions) captured by vV and eE terms played positive roles on the adsorption, whereas the specific hydrogen-bond interaction captured by bB played negative roles on the adsorption of aromatics by GO.
Fig. 5. The regression coefficients (a, b, v, e, and s) curve of the LSER models under different equilibrium concentrations.
and GO surface. Under low Ce system, adsorbate molecules would occupy the most energetic favorable sorption sites, with water molecules adsorbed on less favorable sites. Under high Ce system, the competition between adsorbate molecules on active sorption sites was great, thus rendering some adsorbate molecules to adsorbed on less energetic sites which was occupied by water molecules in low Ce system (Zhao et al., 2014; Yu et al., 2015). This would lead to an increase of v, s, and e coefficient and a decrease of a and b coefficient. Under higher Ce system, the sorption sites were insufficient, and the interactions between adsorbate molecules will be enhanced. The competitive adsorption process is more favorable for highly polarizable contaminants, thus resulting in a decrease of b coefficient. The increasing strengths of dipole-type interaction (s increased) was speculated to be attributed to the exposure of pelectron clouds and the corresponding increase in density of p electron onto adsorbate surface would favor the adsorption from water. For the decrease of e coefficient, it is hard to explain it clearly. One possible deduction is that there are some overlaps among the interactions captured by term eE, vV, and sS (Endo and Goss, 2014), and the dispersion interactions with n/p-election might be screened by other strong interactions. The significant decline of constant C indicated the increasing importance of other unknown adsorption interactions which cannot be interpreted by the above descriptors. The negative value revealed the inhibition effects which weaken the adsorption capability on GO. This would be an overall result of the complicated interactions among adsorbed molecules, water molecules, free adsorbate molecules, and GO surface. The interactions between free adsorbate and absorbed molecules might enlarge a lot with the increase of Ce and then pull the adsorbed molecules away from GO surface (Ding et al., 2016). The interaction terms of LSER models at Ce ¼ 0.01Cs and Ce ¼ 0.1Cs were listed in Table S5 and Fig. S5. At different saturation levels, the cavity formation and dispersion forces captured by vV and hydrogen-bond interactions captured by bB were always dominant in the adsorption process. The eE term always contributed positively to the adsorption.
3.4. Influence of GO properties on LSER models It was reported that the adsorption capability of GO towards OCs
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depends not only on the properties of adsorbate, but also on the nature of GO surface (Li et al., 2013; Sharma et al., 2013). The production of GO involves the introduction of oxygenated functional groups to the graphene basal plane and edges, which will break the p-conjugated network and result in a highly water-dispersible sheets, thus enlarging GO's surface area and providing more active adsorption sites (Kim et al., 2012; Yan et al., 2014). To account for the difference in the nature of GO, same models with oxygen content normalized logK0.001 and surface area normalized logK0.001 were developed (Tables S6eS7). The plots of observed normalized logK values versus predicted logK were shown in Figs. S6eS7. As shown in Fig. 3, certain changes were observed when we established the LSER model with oxygen content normalized logK0.001. A significant change is that the value of coefficient b reduced dramatically and s decreased a lot to a negative value. This can be interpreted as that the functional groups of GO surface contribute a lot to the adsorption of aromatic compound on GO. Oxygen content normalization of logK0.001 suppressed the hydrogen-bond interactions which was supposed to influence the aromatic contaminants adsorption. The decrease in s might also be a result of the decrease of hydrophilic sites deduced by functional groups, indicating that oxygen content was a limiting factor for the adsorption of aromatic contaminants on GO. On the other hand, surface area normalization of logK0.001 failed to improve the performance of LSER model and only minor changes on the regression coefficients were observed. Upon the introduction of normalized logK0.001, the value of a changed from negative to positive and the relative magnitude of each coefficient not varied, demonstrating the change was caused by the different number of available adsorption data for modeling. In dilute saturation (Ce ¼ 0.001 Cs), the surface of GO is larger than the size of adsorbate molecules and will provide sufficient adsorption sites for them. Therefore, surface area of GO was not a limiting factor for aromatic contaminants adsorption at low concentrations, which was also observed in previous research (Apul et al., 2013a). The interaction terms of LSER models were listed in Table S8 and Fig. S8, the change of various interactions followed the tendency indicated by the coefficient discussed above. 3.5. Comparison the finding with AC and CNTs Recent years, LSER models have attracted tremendous attention for the prediction of OCs adsorption by carbonaceous adsorbents such as CNTs and AC (Table S9), and this motivated our modeling efforts based on GO adsorption. Even though there existed large differences among the morphologies and properties of these carbon nanoadsorbents, the regression coefficients was found to follow the same tendency, illustrating that the aromatics adsorption by GO is influenced by similar interactions. Due to the similar structure of carbon planar, similar findings were also proposed in the existing LSER models (Hüffer et al., 2014; Yu et al., 2015). However, obvious differences on the magnitude of regression coefficients were observed for these models (Table S9); specifically, smaller v and larger b were observed for GO than that of CNTs and AC. Possible explanations could be summarized as: (1) The chemical composition of GO is different from that of CNT, which contains larger amount of oxygen-containing functional groups (such as eOH and eCOOH). The oxygen content of GO synthesized by Hummers method is 20 to 30% (Dave et al., 2016), while the oxygen content of CNT reported in literature varied from 1.6% to 11.3% (Khan et al., 2013). The abundant oxygenated functional groups make GO more hydrophilic, which suppressed the cavity formation and the dispersion interactions between GO and adsorbate molecules; in addition, compared to the less oxidized CNTs and AC, the hydrogen bond donating ability to adsorbate molecules were
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strengthened; (2) the difference between the number and type of probe compounds might lead to some deviations of modeling results; (3) the geometry of the adsorbate molecule was not taken into account in the LSER model, which might influence the way of adsorbate molecules interacting with GO (Endo and Goss, 2014; Hüffer et al., 2014); (4) the oxidation of GO might introduce many heterogeneous adsorption sites. It should be noted that it is just a speculation, and the clear explanation is still unavailable. A series of factors such as defect sites, interstitial and groove regions between CNT bundles, AC pore sizes, GO functional groups and morphology of these adsorbents could generate complicated effects on the adsorption capacity and affinity of GO. Besides, it is very hard to introduce an explicit standard to quantify the contribution of each factor. Therefore, more in-depth and comprehensive research considering the aforementioned factors is certainly required. 4. Conclusion Well-behaved LSER predictive models were developed for aromatic contaminants adsorption by GO under different concentrations (Ce ¼ 0.001, 0.01 and 0.1 of adsorbate aqueous solubility). LSER model was employed to predict the aromatic compounds adsorption by GO for the first time, and was proved to be an effective technique in assessing adsorption capability of aromatic contaminants with small adsorbate size (descriptor V < 3.099). The general interactions that influence the adsorption of aromatic compounds by GO from water can be identified as the ease of cavity formation and dispersion forces (vV term), hydrogen-bond interaction (bB term), and n/p-electron interaction (eE term). Certain changes of regression coefficients (a, b, v, e, s) were observed with the increase of Ce. Surface area normalization of logK0.001 did not have a significant influence on the model, and oxygen content normalization of logK0.001 dramatically reduced the hydrogen-bond interaction (bB term) of aromatic compounds adsorption. LSER models provide a novel insight into the simulation of the correlation between adsorption capability and adsorbate solvation descriptors, which contribute a lot to interpret the adsorption mechanisms and provide deeply understandings on the adsorption characteristics of organic compounds on GO that is important to improve the adsorption efficiency. Since the available data was limited and only several aromatic contaminants were included in this study, comprehensive research involving more compounds (such as aliphatic compounds) with a broader range of descriptors and structure differences are urgently needed. In addition, more detailed adsorbent characteristics (such as wrinkles and defects, types of functional groups) and experimental conditions (such as pH, temperatures and ionic strengths) should be taken into consideration, which could ensure the accuracy of the prediction. Furthermore, LSER models should also be extended to other GObased nanomaterials such as graphene nanosheets and GO composites to investigate the difference of adsorption characteristic between these adsorbents. Acknowledgement This paper is supported by National Natural Science Foundation of China (Grant No. 51278147, 71671050, 50808052 and 51408162), HIT Environment and Ecology Innovation Special Funds (No. HSCJ201606), and State Key Laboratory of Urban Water Resource and Environment (Grant No. 2016DX02). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.chemosphere.2017.07.062.
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