Interaction Mechanisms and Predictive Model for the Sorption of

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Interaction Mechanisms and Predictive Model for the Sorption of Aromatic Compounds onto Nonionic Resins Bingjun Pan and Huichun Zhang* Department of Civil and Environmental Engineering, Temple University, 1947 North 12th Street, Philadelphia 19122, United States S Supporting Information *

ABSTRACT: Understanding interaction mechanisms between porous sorbents and organic compounds is important in selecting or custom-synthesizing an appropriate sorbent. In this study, sorption isotherms of a set of 14 (XAD-4&7) or 11 (MN200) aromatic compounds were measured for three nonionic resins, and a phase conversion approach (from aqueous phase to n-hexadecane or gas phase) was applied to separate sorbate-sorbent interactions from the overall involved interactions. Subsequently, contributions of individual interactions to the overall ΔG were quantified by poly parameter linear free energy relationships (pp-LFERs). Cavity energy (V), energy costs for creating cavities in bulk water, is the dominant driving force for the sorption from aqueous phase. Meanwhile, sorption was substantially abated by H-bonding accepting capacities of the solutes (B) due to the high electron accepting capacity of water molecules. Solute’s H-bonding donating capacity (A) and polarity/polarizability (S) are predominantly responsible for the n-hexadecane or gas-phase converted sorptions; V is also important in the gas-phase converted sorption. XAD-7 has larger A and S coefficients than XAD-4 and MN200 for both the original and converted analyses, while the opposite is true for V coefficients. More promisingly, a predictive model, developed based on the sorption of 7 simple aromatic compounds by the resins, can accurately estimate the sorption behaviors of 7 other relatively complex aromatic compounds within a wide range of concentrations.

1. INTRODUCTION Since first developed in the 1960s,1 macroreticular polymeric sorbents (resins) have been attracting increasing attention as one of the most promising alternatives to activated carbon due to their versatility in removal or recovery of organic compounds from water, air, and other media,2−4 solid-phase extraction,5,6 and bioseparation.7,8 Upon appropriate polymerization and functionalization, polymeric sorbents can be custom-synthesized for specific applications.9,10 The interactions taking place in the sorption of an organic solute from aqueous phase to resins include sorbate-sorbent and solvent-associated interactions (i.e., water-solute, water-sorbent, and water−water interactions). Knowledge about interactions between resins and target compounds is crucial for choosing or customizing an appropriate resin for a specific separation problem. To date, although many studies have reported the application of resins in industrial wastewater treatment and in recovery of useful organic chemicals from waste streams for reuse, most studies have been confined to rather phenomenological descriptions of the sorption process. This is partly due to the complexity of separating solvent-associated interactions from sorbate-sorbent interactions. In addition, no quantitative data are available to predict the sorption capacity of a given contaminant by a resin. Given the fact that (1) there are a large variety of polymeric sorbents being offered by a variety of chemical companies to © 2013 American Chemical Society

treat an equally bewildering array of contaminants, (2) these resins function mechanistically differently for sorbing the contaminants; and (3) there is no unifying principle to base on regarding the selection of a given sorbent for a given contaminant, there is a great need for both a fundamental understanding of the sorption mechanisms of organic contaminants by resins and predictive models that can be used to guide resin selection processes for a given contaminant. The phase-conversion approach is an attractive method to separately account for complex solvent-associated and sorbatesorbent interactions. This approach is to choose the same simple reference state for all compounds. By doing so, contributions of sorbate-sorbent interactions can be comparatively explored. Borisover and Graber converted aqueous-phase sorption to the corresponding partitioning from n-hexadecane (HD) to a water-wet sorbent.11 As a result, the difference in the contributions of solvent-associated interactions for different solutes is minimized or even eliminated. Zhu and Pignatello also adopted the same conversion to develop a hexadecanewater partitioning coefficient (KHW)-based multiparameter model.12 Another more attractive option would be to use the ideal gas phase as the reference state where there are no Received: June 18, 2013 Published: July 23, 2013 17707

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intermolecular interactions in the reference state,13−15 that is, only sorbate-sorbent interactions are responsible for the converted sorption. Note that the sorbate-sorbent interactions here mean the interactions between sorbate and water-wet sorbent. Comparison of the original and converted sorption can provide insights into the nature of both specific (i.e., electron donor−acceptor) and nonspecific (i.e., van der Waals) interactions between sorbates and sorbents. To relate sorption to solute structures or properties, linear correlation of distribution coefficients with n-octanol−water partition constants (Kow) or aqueous solubility has been historically adopted.16−18 The major problem for such an approach is the overemphasis on solvent-associated interactions, with the result being an inadequate accounting of contribution of sorbate-sorbent interactions. This is particularly the case for polar compounds exhibiting specific interactions with the sorbent. Furthermore, the method of correlation with aqueous solubility chooses the pure or subcooled liquid state of each solute as the reference state.18 Such a reference state is not a judicious choice when comparing aqueous sorption among various compounds because it is compound-specific, that is, different compounds have different solute−solute and solute− solvent interactions. For example, only nonspecific interactions exist among apolar or monopolar compounds, while there are additional specific interactions among bipolar compounds. Moreover, apolar compounds undergo only nonspecific interactions with water molecules, while monopolar and bipolar compounds possess functional groups that can participate in specific interactions with water molecules. In short, performing these correlations would eventually introduce different baselines when comparing interactions among different solutes. Polyparameter linear free energy relationships (pp-LFERs) have been widely used to estimate either the partition of organic compounds between two bulk phases19 or the sorption of organic compounds by NOM,20,21 activated carbon,22,23 and carbon nanomaterials.24,25 pp-LFERs are typically developed by specifically considering energy contributions of multiple soluteassociated interactions27,28 SP = eE + sS + aA + bB + vV + c

serve to characterize differences between the two phases in terms of their respective interactions with the solutes. The coefficient c carries factors independent of solutes13 and may contain an entropy term that represents solvent-specific or sorbent-specific free energy contribution.40 In the present study, three widely used resins (XAD-4, XAD7, and MN200) were examined as representative nonionic resins; sorption isotherms of a set of 14 (XAD-4&7) or 11 (MN200) aromatic compounds were measured over a wide range of aqueous equilibrium concentrations, and a phaseconversion method was applied (to the HD phase or the ideal gas phase). Multiple linear regression analysis (eq 1) was then employed to fit the original and converted results to quantitatively describe the contribution of each interaction to the overall sorption, based on the same sorbed concentration (qe) on resins for different solutes rather than equilibrium aqueous solute concentration (Cw). Endo et al. believed that using qe for normalization is necessary for implementation of LFERs into nonlinear sorption,13 because molecular interactions between different compounds and the sorbent are compared at the same loading. In the case of HD-based conversion, the nonspecific interactions for sorption are expected to be approximately eliminated (reflected in the vV and eE terms). 12,41 As for the gas phase conversion, contributions from complex solvent-associated interactions (water-solute and water−water interactions) have been removed, and the converted sorption coefficients would reflect the contributions of sorbate-sorbent interactions. By comparing the results among the original sorption, and the HD-converted and gas phase-converted ones, we can gain insights into the effects of chemical structures or properties of both solutes and sorbents on the involved interactions. Contributions of nonspecific and specific interactions can also be quantified. Subsequently, based on the dependency of the regression coefficients on qe, a predictive model was developed to accurately estimate the sorption behavior of organic compounds from the aqueous phase by the three nonionic resins within a wide range of concentrations. 1.1. Phase Conversion Analysis and pp-LFERs Fits. Upon the thermodynamic cycle (Scheme 1), the net free

(1)

where SP is a free energy related property of the system arising from distribution of solute between phases of interest (e.g., distribution coefficients, logK); the terms E, S, A, B, and V represent solute descriptors introduced by Abraham’s group.29 The excess molar refraction (E) is intended to capture nonspecific interactions arising from induced dipoles that involve London dispersive forces (induced dipole−induced dipole, ID-ID) and Debye forces (dipole−induced dipole, DID);29 the McGowan’s characteristic molecular volume (V) accounts for cavitation energy and part of London dispersive forces beyond what is captured by the E term;30 the polarity/ polarizability parameter, S, was originally defined by Abraham31 and is believed to reflect predominantly the effects of stable polarity (i.e., dipole−dipole interactions) and some effects of induced dipole (polarizability), and thus has cross effects with E;32 A and B are the descriptors for the overall H-bonding acidity and basicity (electron accepting and donating capacities).27 Note that, although these descriptors were introduced to represent various interactions, it is not possible to separate out exactly the various interactions. These solute descriptors are either widely available33−36 or can be estimated based on existing methods.29,30,37−39 The coefficients e, s, a, b, and v are determined by multiple linear regression analysis and

Scheme 1. Relationship of Gibbs Free Energy Change Arising from Distribution between Different Phases

energy change, ΔGS−W,i, resulting from sorption of solute i from the aqueous phase to a solid sorbent is equal to the sum of ΔGH−W,i and ΔGS−H,i ΔGS−W, i = ΔG H−W, i + ΔGS−H, i

(2)

where ΔGH−W,i is the corresponding net free energy change for the partitioning of solute i from aqueous phase to a hypothetical bulk phase, and ΔGS−H,i is that for the sorption of solute i from the hypothetical bulk phase to the water-wet sorbent. In the context of this paper, the hypothetical bulk 17708

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phase is either the HD phase (ΔGS−W,i = ΔGHD−W,i + ΔGS−HD,i) or the ideal gas phase (ΔGS−W,i = ΔGg‑W,i + ΔGS‑g,i). ΔGH−W,i is related to the equilibrium partition coefficients, KH−W,i (mol/L: mol/L), between the hypothetical bulk phase and the aqueous phase by18 ΔG H−W, i = −RT ln KH−W, i + RT ln

VW VH

(3)

here R [8.314 × 10−3 KJ/(mol·K)] and T (K) are the universal gas constant and the absolute temperature, respectively; and VW and VH are the molar volumes of water and the hypothetical bulk phase. Generally, the contribution of solute i to the molar volume of the mixture can be neglected for moderately or sparingly soluble compounds, so we use water molar volume (0.018 L/mol) as VW , and HD molar volume (0.293 L/mol) or ideal gas molar volume (24.73 L/mol) as VH . ΔGS−W,i is related to the aqueous sorption process and can be approximately calculated from KS−W,i by12 ΔGS−W, i = −RT ln KS−W, i + Cads

(4)

where K S−W,i (L/kg) is the equilibrium sorbent-water distribution coefficient (=qe/Cw). Cads is a correction term for the net free energy of transfer of solute i from the bulk aqueous phase to the sorbent and can be approximately calculated by Cads/RT = ln[ VW /(Vi ρads)]−1, where Vi is the molar volume of solute i which can be estimated based on atomic volumes and the number of bonds;39 and ρads (g/mL) is the density of the sorbent. Substitution of eq 3 and 4 into eq 2 yields

Figure 1. Original aqueous sorption isotherms of 14 or 11 aromatic compounds on XAD-4, XAD-7, or MN200. The isotherms for NB, caffeine, and phenols on XAD-4 and XAD-7 have been adapted from Pan and Zhang 2012.41

⎛ ⎛ q ⎞ V ⎞ −RT ⎜ln e − ln K g−W, i⎟ + RT ⎜⎜Cads /RT − ln W ⎟⎟ Vg ⎠ ⎝ Cw ⎠ ⎝

ΔGS−H, i = −RT (ln KS−W, i − ln KH−W, i) ⎛ V ⎞ + RT ⎜Cads/RT − ln W ⎟ VH ⎠ ⎝

= f e (qe) ·E + f s (qe) ·S + f a (qe) ·A

(5)

+ f b (qe) ·B + f v (qe) ·V + f c (qe)

Then the values of ΔGS−W,i and ΔGS−H,i (i.e., ΔGS−HD,i or ΔGS‑g,i) can be examined for their dependencies on solute descriptors by replacing SP in eq 1

(7)

After arrangement, we obtain ln Cw = ln qe +

ΔGS−H, i(or ΔGS−W, i) = eE + sS + aA + bB + vV + c (6)

f TOT (qe) RT

−

Cads V + ln W − ln K g−W, i RT Vg (8)

1.2. Development of Predictive Model. A predictive model was developed on the basis of the ideal gas phase conversion, because the contributions from solvent-associated interactions (ΔG g‑W,i ) and sorbate-sorbent interactions (ΔGS‑g,i) can be separately accounted for. Sorption of organic solutes on polymeric sorbents is typically nonlinear (Figure 1), so the coefficients e, s, a, b, v, and c are concentrationdependent. The following steps were followed to develop the model. 1. For any arbitrary value of qe, we can calculate the corresponding value of Cw for each solute according to the isotherm fits by the D−A model (see below) and then the value of ΔGS‑g,i using eq 5. 2. At each qe, multiple linear regressions between ΔGS‑g,i and the descriptors for all solutes can be conducted based on eq 6, and a set of e, s, a, b, v, and c values can be obtained. 3. For multiple hypothetical values of qe, the relationships between the coefficients e, s, a, b, v, c, and qe [denoted as f sd(qe), sd means e, s, a, b, v, or c] can be obtained by correlating the sets of e, s, a, b, v, and c values with the corresponding qe values. 4. Substituting f sd(qe) and eq 5 into eq 6, we get

TOT

where f (qe) means the right side of eq 7, and Kg‑W,i and Vg are the gas−water equilibrium partition coefficient (i.e., Henry’s constant) and the molar volume of ideal gas phase, respectively. If all interactions responsible for sorption are captured, the estimated Cw at any qe should be equal to the experimentally measured values for various solutes. Consequently, eq 8 can be employed to accurately predict the nonlinear sorption behavior of any solute, if armed with the values of solute descriptions and Henry’s constants.

2. EXPERIMENTAL DETAILS Characteristics of Amberlite XAD-7 (polymethacrylate), XAD-4 (polystyrene), and MN200 (hyper-cross-linked polystyrene) are listed in Table S1. MN200 is similar to XAD-4 but with a higher degree of cross-linkage, which is expected to yield more micropores and a higher surface area. The measured BET surface areas for XAD-7, XAD-4, and MN200 are 495, 829, and 1021 m2/g, respectively; and the proportion of micropores ( XAD-4 > XAD7 for the test compounds, agreeing with the abundance of micropores in the sorbents (Table S1). Using eq 6, multiple linear regressions were conducted between ΔGS−W,i and Abraham’s solute descriptors. The best regression model was determined by the lowest Mallow’s Cp value while keeping the p-value of each parameter less than 0.1 to ensure each involved term statistically significant. The regression models were identified for sorbents at different sorbed concentrations (10−3000 μmol/g for XAD-4, 10−2000 μmol/g for XAD7, 10−3800 μmol/g for MN200). As shown in Table 2 (only results at 100 and 1000 μmol/g were listed as an example), sorption from aqueous phase was only promoted by cavity formation (positive values of the V coefficient) and primarily inhibited by the electron donating capacity of the solutes (large negative values of B coefficients). The S and A terms have some negative contributions to varying degrees. The solutes that disperse in water have a tendency to be “expelled” from the bulk water phase due to the large free energy cost for cavity formation. Such a tendency is always one of the primary driving forces for organic compound sorption from aqueous solution. Thus, term V is responsible for the sorption. On the other hand, though the polymeric sorbents can provide electrons to form electron donor−acceptor (EDA) interactions with polar compounds, the solvent (i.e., water) has greater electron accepting and donating capacities (A = 0.82, B = 0.35).33 Consequently, compounds with larger A or B values instead have less tendency to be sorbed. 3.2. HD-Based Conversion Analysis. The dependency of ΔGS−HD,i, the free energy change of solute sorption from the HD phase to water-wet polymeric sorbents, on the solute descriptors were examined and also listed in Table 2. After conceptually converting aqueous sorption to the corresponding sorption from HD at the same activity, the contributions of V and B terms, which predominate the sorption from the aqueous phase, vanished; and the terms A and S stand out to be

exclusively responsible for the sorption. HD is an inert saturated hydrocarbon which can undergo only induced dipole-related interactions with all compounds, irrespective of their polarity.18 Borisover and Graber14 observed that Gas-HD distribution coefficients of both apolar and polar compounds have practically the same dependency on molar refraction as that of Gas-NOM distribution coefficients of the apolar compounds, indicating that HD undergoes only polarizabilityrelated interactions. Zhu and Pignatello12 also concluded that, based on a theoretically similar HD conversion analysis, the contributions of terms V and E were actually included in the hydrophobic component which is quantified by KHW. All three polymeric sorbents have electron donating capacities,41 while HD has no potential sites to form H-bonding with solutes. It follows that, as shown in Table 2, term A is the predominant factor responsible for sorption of the solutes. Additionally, because both the sorbent phases and the HD phase lack Hbonding donating capacities, term B did not significant contribute to the converted sorption. 3.3. Ideal Gas Phase-Based Conversion Analysis. After conversion of the original aqueous sorption to the ideal gas phase sorption, the reconstructed sorption isotherms for the test compounds are shown in Figure 2. The converted concentrations in the gas phase (Cgas) correspond to the aqueous concentrations (Cw) when excluding the effect of solvent-associated interactions and were calculated by (see derivation in the SI) ⎛ −ΔGg‐W, i C ⎞ + ads ⎟ Cgas = Cw exp⎜ RT ⎠ ⎝ RT

(10)

where ΔGg‑W,i is the corresponding net free energy change for the partition from the aqueous phase to the gas phase. For the original sorption isotherms (Figure 1), differences in sorption behaviors among various compounds are somewhat obscured, because differences in sorbate-sorbent interactions are offset by solvent-associated interactions. This is especially the case for 17711

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London dispersive forces are captured by term V. Therefore, this part of term V may represent the contribution of London dispersive forces. Additionally, all regression models for three resins have a negative “c” value. Constant c may contain the entropy contributions that represent differential “freedom” of molecules between the two phases and is independent of sorbates.40 As can be expected, the “molecular motion” of chemicals is maximal in the gas phase, so significant entropy cost occurs when sorbed from it. Whether in the aqueous phase or after HD- and gas phaseconversion, term E is always insignificant, which means that the tested solutes performed almost the same “E term-captured” interactions with the resin phases as with the bulk phases. Actually, the E values of water, hexadecane and gas phase are all zero, indicating they are incapable of forming “E termcaptured” interactions. Thus, the selected three resins do not have observed “E term-captured” interactions with the tested compounds. By comparison, the S and A coefficients for XAD-7 are usually larger than those of XAD-4 and MN200. XAD-7 has a polymethacrylate structure while XAD-4 and MN200 are polystyrene. Generally, polymethacrylates have higher polarity and stronger electron donating capacities. This can be inferred by comparing methylbenzene (S = 0.52, B = 0.14) and methylacetate (S = 0.64, B = 0.45).33 Consequently, XAD-7 can form stronger S and A interactions with the tested compounds. On the other hand, MN200 and XAD-4 have larger V coefficients than XAD-7, which correlates with the abundance of micropores in resin phases. Additionally, the A coefficients of the gas-phase conversion are comparable to those of the HDphase conversion, while the S coefficients are much larger. Although the HD phase lacks stable polarity, it can still undergo some S term-captured interactions with solute molecules through its polarizability and thus partly cancel out contributions of such interactions between sorbates and sorbents. The observed Gibbs free energy changes upon distribution between difference phases can be compared. Generally, the −ΔG values are greater at lower qw for all three resins (data not shown), which corresponds to the fact that sorption spaces with higher sorption potential are available at lower qw. Taking the sorption of 14 solutes by XAD-4 at qe = 100 μmol/g as an example, the values of −ΔGS−W,i (23.09−38.37 KJ/mol) are relatively comparable among all the tested solutes mainly because of interference of solvent-associated interactions. However, distinct difference exists in the values of −ΔGS‑g,i (29.50−71.93 KJ/mol). Comparatively, sorbate-sorbent interactions (ΔGS‑g,i) are the most important driving force in the overall sorption; while solute-water interactions (−ΔGg‑W,i: 0.10 ∼ −33.56 KJ/mol) contribute negatively mainly due to the strong H-bonding interactions (cavity formation in water contributes positively to the sorption process). Within sorbatesorbent interactions, contributions from specific interactions (−ΔGS‑HD,i: 14.29−31.51 KJ/mol) are more important than those of nonspecific interactions (−ΔGHD−W,i: −4.10−15.70 KJ/mol). To the best of our knowledge, this is the first time the relative contributions of solvent-associated and specific and nonspecific sorbate-sorbent interactions to the overall Gibbs free energy of sorption have been quantified. 3.4. Validation and Application of the Predictive Model. To correlate the regression coefficients e, s, a, b, v, and c with qe, multiple linear regressions between ΔGS‑g,i and solute descriptors were conducted at various sorbed concentrations based on the “training set” which included seven compounds

Figure 2. The reconstructed sorption isotherms based on eq 10 by converting the original aqueous sorption to sorption from the ideal gas phase.

substituted phenols and anilines. After converting to gas-phase sorption (Figure 2), significant differences in the sorption behaviors can be observed. In general, chemicals with tendencies toward strong specific interactions with the sorbent phase demonstrated much greater sorption capacities. Specifically, the sorption on all three resins have general trends as follow: (i) nitro-substitution > chloro-substitution > methylsubstitution (e.g., 4-NP > 4-CP > 4-MP) and (ii) p-NO2 > mNO2 > o-NO2. Nitro groups are strongly electron-withdrawing, chloro groups are weakly electron-withdrawing, and methyl groups are slightly electron-donating. Functional groups with electron-withdrawing abilities can increase the polarizability and H-bonding donating capacity of a compound and, thus, increase the ability to form strong specific interactions. As for the position of nitro substituents, p-NO2 groups have additional resonance effects that further increase their electron-withdrawing ability; there are only inductive effects in m-NO2 groups; and intramolecular H-bonding within o-NO2 groups may exist to decrease their electron-withdrawing ability. Additionally, the sorption of BPA, an emerging contaminant of concern, is highest among the tested compounds, because of its high S, A, and V values. The dependency of ΔGS‑g,i on the solute descriptors is presented in Table 2. After eliminating the contributions from complex solvent-associated interactions, contributions of multiple sorbate-sorbent interactions to the Gibbs free energy change can be assessed. Similar to the results of the HD phase conversion, terms A and S are also the dominant contributors while B and E are insignificant. However, additional contributions from V emerged. Intuitively, we did not expect that V contributes to ΔGS‑g,i, because no cavity effect exits in the gas phase. As aforementioned, it is not possible to accurately separate out various interactions and a part of 17712

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(NB, phenol, 4-MP, 4-CP, 4-NP, aniline, 4-CA). The other seven compounds were treated as the “testing set”. Terms B and E are not significant to the regressions for all three resins, so they were excluded. Values of all the obtained coefficients as a function of qe are plotted in Figure S1. Based on the obtained functions [f sd(qe)], eq 8 was used as the predictive model to estimate the corresponding equilibrium aqueous concentration at any given qe. Comparison of the estimated values versus the experimental values is illustrated in Figure 3, which indicates

Figure 4. Correlation of the experimental equilibrium concentrations with the estimated values based on the regressions without term A.

certain sorbed capacity. The reverse is true for the chemicals with low A values. In addition, after carefully inspecting the results of phenols and anilines as two groups, such a trend is not strictly followed. For example, the estimated Cw of 4-NP or 4-NA, with the highest A values in their own group, were instead lower than others. Arey et al. believed that term S may reflect some mixing or interference with the hydrogen-bonding terms.44 Thus, the effect of term S may more or less be involved in the A-excluding normalization. As seen in Table 1, 4-NP and 4-NA have the largest S, which may promote the underestimation. Similar results were also found for the regressions without term S (Figure S2), i.e., the equilibrium concentrations were generally overestimated for the chemicals with larger S values and were underestimated for the chemicals with smaller S values.

Figure 3. Correlation of the estimated equilibrium concentrations using eq 8 with the experimental values for seven training-set compounds with 139, 139, and 130 data points for XAD-4, XAD-7, and MN200 and seven or four test-set compounds with 115, 117, or 73 data points for XAD-4, XAD-7, or MN200, respectively.

that eq 8 can estimate the sorption behavior with high accuracy for all the three resins. Although 2-NAPH, BPA, and caffeine have more complex structures than the compounds in the “training set”, the model can also predict their sorption behavior accurately. Results did not practically change when one compound (4-CP) was moved to the “test set”, or another compound (4-NA) was added to the “training set”. Moreover, the precision was not improved when term B or E was involved in the regressions. As discussed, terms A and S are the dominant factors in the converted sorption. Intentionally, term A or S was withheld from the regression to test the dependence of sorption on them. The estimated values based on the regressions without term A are illustrated in Figure 4. Generally, the estimated values deviated from the measured ones to varying degrees, with the calculated Cw of phenols overestimated, and those of NB, caffeine, and anilines underestimated. The A values of the overestimated chemicals (0.57−0.99) are larger than those of the underestimated chemicals (0−0.42). By withholding term A, all the studied chemicals were supposed to have the same Hdonating capacity which is an average of the included chemicals. Hence, the H-donating capacities of the chemicals with higher A values will be underestimated and so will the sorption capacities. Consequently, larger Cw are needed to achieve a

4. CONCLUSIONS To use nonionic resins to remove organic contaminants during water treatment, a fundamental understanding of the sorption mechanisms and development of predictive models that can be used to guide resin selection processes for a given contaminant are necessary. Combined with the phase-conversion approach, pp-LFERs yield insights into the interactions between solute molecules and resins, which have been obscured by the complexity of solute-water interactions in aqueous sorption processes. Together with information derived from original aqueous sorption, this information enables estimation of the effectiveness of a given resin for compounds of interest and may be employed as a guide for custom synthesis of appropriate resins for a specific separation problem. For example, electronrich functional groups can be introduced to the matrix of polymeric sorbents to enhance their sorption capacities for polar compounds with high H-bonding acidities. The ppLFERs-based predictive model represented by eq 8 allows highly accurate predictions of sorption behavior of many 17713

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aromatic contaminants on a given sorbent which is essential for any separation process. Note that the LFER parameters for the new compounds of interest should be within the range of the tested compounds.

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ASSOCIATED CONTENT

S Supporting Information *

Additional figures and tables, as well as detailed information of sorption experiments and derivation of eq 10. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 215-204-4807. Fax: 215-204-4696. E-mail: hjzhang@ temple.edu. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was partly supported by Grant/Cooperative Agreement Number G11AP20102 from the US Geological Survey via a subaward from the Pennsylvania Water Resource Research Center. The authors would like to acknowledge Dr. Eric J. Weber from the US EPA National Exposure Research Laboratory in Athens, GA for reviewing the manuscript.

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REFERENCES

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