Understanding and Modeling Removal of Anionic Organic

May 30, 2014 - Despite the importance of the NPM of the anions, neutral solutes were sorbed much less, indicating synergistic combinations of electros...
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Understanding and Modeling Removal of Anionic Organic Contaminants (AOCs) by Anion Exchange Resins Huichun Zhang,* Anthony J. Shields, Nastaran Jadbabaei, Maurice Nelson, Bingjun Pan, and Rominder P.S. Suri Department of Civil and Environmental Engineering, Temple University, 1947 North 12th Street, Philadelphia, Pennsylvania 19122, United States S Supporting Information *

ABSTRACT: Ionic organic contaminants (OCs) are a growing concern for water treatment and the environment and are removed inefficiently by many existing technologies. This study examined removal of anionic OCs by anion exchange resins (AXRs) as a promising alternative. Results indicate that two polystyrene AXRs (IRA910 and IRA96) have higher sorption capacities and selectivity than a polyacrylate resin (A860). For the polystyrene resins, selectivity follows: phenolates ≥ aromatic dicarboxylates > aromatic monocarboxylates > benzenesulfonate > aliphatic carboxylates. This trend can be explained based on hydration energy, the number of exchange groups, and aromaticity and hydrophobicity of the nonpolar moiety (NPM) of the anions. For A860, selectivity only varies within a narrow range (0.13−1.64). Despite the importance of the NPM of the anions, neutral solutes were sorbed much less, indicating synergistic combinations of electrostatic and nonelectrostatic interactions in the overall sorption. By conducting multiple linear regression between Abraham’s descriptors and nature log of selectivity, induced dipole-related interactions and electrostatic interactions were found to be the most important interaction forces for sorption of the anions, while solute H-bond basicity has a negative effect. A predictive model was then developed for carboxylates and phenolates based on the poly parameter linear free energy relationships established for a diverse range of 16 anions and 5 neutral solutes, and was validated by accurate prediction of sorption of five test solutes within a wide range of equilibrium concentrations and that of benzoate at different pH.

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INTRODUCTION Many organic contaminants (OCs) contain acidic functional groups such as carboxylates, phenolates, and sulfonates and exist predominantly in the anionic form under solution pH greater than their pKa values. Examples include priority contaminants such as pesticides, dyestuffs, petrochemicals, chemical intermediates, and emerging contaminants such as pharmaceutical and personal care products. The anionic OCs (AOCs) have become a great concern to the environment as many studies have demonstrated their widespread prevalence and known or potentially harmful effects to human health and the environment.1−4 Existing water and wastewater treatment technologies were not specifically designed to remove AOCs and the elimination rate can vary from negligible to over 90%.4−9 For example, adsorption of 6-aminopenicillanic acid (the core of penicillins, pKa = 2.3) on activated carbon or 4nitrophenol (pKa = 7.16) on a neutral resin was found to decrease dramatically with increasing pH.10,11 Therefore, new technologies specifically targeting AOCs are desired. A promising alternative approach can be through the use of anion exchange resins (AXRs), although continuous efforts are needed to develop more cost-effective resin regeneration technologies, including those that can effectively treat regenerant brines. AXRs can selectively target certain anions © 2014 American Chemical Society

as they can be custom-synthesized during polymerization by including desired functional groups: usually quaternary ammonium (strong AXRs) or amine groups (weak AXRs).11,12 Furthermore, they can be efficiently regenerated by base elution.13 These resins have already been successfully used to treat industrial wastewater containing aromatic sulfonates.12−14 A limited number of studies have also been carried out to examine the mechanisms and efficiencies of using these resins to remove carboxylates15−18 and phenolates.19 Two typical reactions occur during anion exchange processes:13 R 4N+Cl− + A− ⇌ R 4N+A− + Cl−

(1)

R 2NH + A− + H 2O ⇌ R 2NH+2 A− + OH−

(2)

where R can be a substituent or −H. Sorption capacity by AXRs when protonated amines are the functional group (eq 1) is much higher than when amines are in the free base form (eq 2).12,20 Polystyrene and polyacrylate resins are two widely used resin matrices, with polyacrylates more polar and hydrophilic Received: Revised: Accepted: Published: 7494

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with open structures.21 By examining sorption of pentachlorophenolate (PCP) by two strong AXRs, Li and Sengupta19 believed that the nonpolar moiety (NPM) of the aromatic anion, polarity or moisture content of the resin matrix, and solvent dielectric constant are the three most important parameters affecting the sorption equilibrium. Specifically, although the sorption capacity of the aromatic anion (and 10 dissolved organic matter isolates22,23) is entirely based on the ion exchange mechanism with the release of an equivalent amount of Cl− into the solution, selectivity of the anions depends heavily on the interactions between the NPM of the anion and the resin matrix. Therefore, removal of aromatic anions is much higher by polystyrene-based than by polyacrylate-based AXRs.18 Compared with the resin matrix effect on sorption capacity, the effects of resin ion exchange capacity or the form of polymer (gellular vs macroporous) are much smaller.18,19 Additionally, the hydrophobic resin matrix itself is only able to remove a small portion of the anions.12,19 An important parameter for ion exchange (e.g., anion A− against Cl−) is selectivity (αA/Cl, or separation factor): y xCl αA/Cl = A yCl xA (3)

ence of each interaction between the solution phase and the sorbent phase. A positive coefficient means the sorbent phase will have a stronger interaction with a solute for that particular interaction force than the solution phase. In order to capture the additional interactions in partition of ionic species between two solvents, Abraham’s group proposed to include two additional descriptors: J− for anions and J+ for cations.35−37 Thus, for anionic species the new equation becomes SP = eE + sS + aA + bB + vV + j− J− + c

Relevant descriptors have been available for carboxylates,35 phenolates,36 and a few other anionic compounds.38 Although the new equation has successfully described partition of both neutral and ionic species between two solvent phases, to the best of our knowledge, there is no information on whether and how pp-LFERs can be applied to ion exchange processes. Overall, there are complex electrostatic and nonelectrostatic interactions in sorption of organic anions by AXRs, all of which can be greatly affected by both resin and solute structure and functionalization. Despite the available information, most studies have been confined to rather phenomenological descriptions of the anion exchange process for a limited number of compounds, and no predictive tools are available to estimate either selectivity or sorption capacity of a diverse range of anions by AXRs within a wide range of solution concentrations. Not to mention the lack of knowledge on the relative contribution of either electrostatic or nonelectrostatic interactions to the overall sorption. Toward this end, we examined sorption isotherms of 17 anions and 5 neutral solutes by three different types of AXRs within a wide range of concentrations. The impact of solute and resin structures and solution pH on the sorption behavior was evaluated. Interaction forces involved in the sorption processes were then interpreted after applying the new pp-LFER (eq 5). The obtained ppLFERs also helped develop a predictive model to estimate the sorption capacity and selectivity of a given solute by one of the resins (i.e., IRA910) within a wide range of concentrations. Since many AOCs have pKa values within the environmental pH range, they can exist as either neutral or anionic species depending on the solution pH. The model was then expanded to predict the sorption capacity of a given AOC by an AXR at any pH.

where yi and xi represent the mole fraction of counterion i in the exchanger phase and in the aqueous phase, respectively. Selectivity of inorganic anions is mainly determined by solvation energy of the ion.24,25 Selectivity of organic anions increased with an increase in the number of aromatic rings in the anion and in the number of carbon atoms around the exchange site.12,26 For example, selectivity against Cl− follows benzoate (1.0−2.1) > bicarbonate (0.28−0.33) > propionate (0.08−0.23) for polystyrene resins and benzoate (0.04−0.38) > propionate ion (0.03−0.12) for polyacrylate resins.16,17 The low selectivity for short-chain carboxylates is because they are more strongly hydrated than Cl−.15 A combination of electrostatic and hydrophobic interactions has been used to explain the higher selectivity of polystyrene than that of polyacrylate resins.26 Polyparameter linear free energy relationships (pp-LFERs) have been successfully applied to elucidating sorption mechanisms of a diverse range of neutral organic solutes by activated carbon,27 multiwalled carbon nanotubes,28 soil organic carbon,29 and nonionic resins.30,31 A general relationship was developed by Abraham’s group that relates any specific partition (SP) to individual interaction forces:32,33 SP = eE + sS + aA + bB + vV + c

(5)

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MODEL DEVELOPMENT The Gibbs free energy of transfer of solute, i, from the aqueous phase to the sorbent phase, ΔGS−W,i, can be related to the equilibrium sorbent-water distribution coefficient KS−W,i:

(4)

The terms E, S, A, B, V are descriptors individual to each solute that are designated to represent the energy contribution of separate interactions: E represents the contribution from nonspecific induced-dipole related interactions such as London dispersive forces and Debye forces; V accounts for the solvent cavity formation forces and some additional nonspecific interactions; S primarily represents forces from permanent dipole interactions although it does include some induceddipole interactions as well; A and B represent the hydrogen bond acidity and basicity respectively or the electron accepting and donating capacities; and c is a constant used to capture all other additional forces and might be entropy related.28 It is important to note that some of these descriptors have overlaps between them in accounting for the various interactions.31,34 The terms e, s, a, b, v, c are regression coefficients determined through multiple linear regression to demonstrate the differ-

ΔGS − W, i = −RT ln KS − W, i

(6)

where R is the universal gas constant [0.008314 KJ/(mol·K)] and T (K) is the absolute temperature of the system. KS−W,i can be determined based on the mole fraction of counterion i in the exchanger phase (yi) and in the aqueous phase (xi), and can be calculated as19 KS − W, i =

yx i Cl yCl xi

=

Qe Qc − Qe

m

.

[Cl−]0 + Q e V Ce

(7)

where Qe (μmol/g) is the equilibrium sorption capacity, Qc is the resin ion exchange capacity (meq/g), [Cl−]0 is the initial Cl− concentration (μM), Ce (μM) is the solute equilibrium concentration, m is the mass of the resin (g), and V is the 7495

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Table 1. Physical−Chemical Properties and Solute Descriptors of the Selected Organic Compounds solute descriptorsa compounds (abbr.)

pKaa

hydration energy

benzoate (BA) 4-chlorobenzoate(CBA) 3-methyl-2-nitrobenzoate (2-NBA) 3-methyl-4-nitrobenzoate (4-NBA) 4-methoxybenzoate (MBA) 1-naphthoate (Naph) ibuprofen (IBU) acetate isobutyrate (IB) dichloroacetate (DCA) 4-nitrophenolate (NPN) 2,6-dichlorophenolate (2,6-DCP) phthalate dianion (PHTH) tere-phthalate dianion (TERE) flumequine anion (FLU) ciprofloxacin anion (CIP) benzenesulfonate (BS)

anionic solutes

4.21 4.00 2.17 3.40 4.49 3.61 4.85 4.75 4.88 1.34 7.16c 7.02c 2.89, 8.40 3.51, 8.33 6.5 6.2, 8.8 −2.8

−286 −282 −326 −291 −284 −293 −326 −349 −334 −320 −276 −245 −637 −568 −309 −310 −345

neutral solutes

3.9 0.6 10.0 10.3 NA

4-chloroaniline (4-CA) 4-nitroaniline (4-NA) phenol 4-methylphenol (4-MP) nitrobenzene (NB)

b

E

S

A

B

V

J−

0.88 0.99 1.14 1.14 1.05 1.61 0.88 0.415 0.35 0.632 1.22c 1.05c 1.4 1.4 0.8d 1.0d

3.64 3.37 3.7 3.35 4.0 4.13 3.5 2.19 1.61 2.53 4.048c 4.12c 3.88 3.55 3.50d 4.10d

0 0 0 0 0 0.05 0 0 0 0 0.045c 0c 0 0 0.02d 0.01d

2.88 2.6 2.85 2.77 3.05 2.87 3.31 2.93 2.97 2.18 2.414c 2.38c 5.25 5.08 3.87d 4.86d

0.910 1.033 1.225 1.225 1.110 1.442 1.756 0.443 0.725 0.688 0.928c 0.998c 1.104 1.104 1.77d 2.41d

2.395 2.179 2.255 2.185 2.4 1.61 2.404 2.075 1.992 1.426 2.075c 2.350c 3.124 3.356 2.65d 3.02d

1.06 1.22 0.81 0.82 0.871

1.13 1.91 0.89 0.87 1.11

0.30 0.42 0.60 0.57 0

0.31 0.38 0.30 0.31 0.28

0.939 0.991 0.775 0.916 0.891

0 0 0 0 0

a Ref 36 unless otherwise noted. bCalculated using SPARC (http://www.archemcalc.com/sparc.html) as ion transfer energy from the gas phase to the aqueous phase: kJ/mol. cRef 35. dThis work.

solution volume (L). As reported,19 selectivity for all anions against Cl− (αA/Cl) can also be calculated using eq 7. Equation 5 can then be applied to ΔGS−W,i and selectivity to examine the importance of the solute descriptors:

m ⎛ Q [Cl −]0 + Q e V e ⎜ −RT ln⎜ · Ce ⎝Q c − Q e

⎞ ⎟⎟ ⎠

= f e (Q e)E + f s (Q e)S + f a (Q e)A + f b (Q e)B −

+ f v (Q e)V + f j (Q e)J− + f c (Q e)

ΔGS − W, i(or αA/Cl) = eE + sS + aA + bB + vV + j− J− + c

(9)

After rearranging to solve for Ce the model becomes

(8)

After determining ΔGS−W,i for the sorption of a set of training compounds, a predictive model can be developed based on the following steps: 1 For any arbitrary value of Qe, the corresponding value for Ce can be calculated based on the Polanyi DubininAstakhov (D-A) model for fitting isotherms (details in Text S1 and Table S1 in the Supporting Information (SI)).31 Then the corresponding ΔGS−W,i can be calculated based on eqs 6 and 7 using the observed linear relationships between m/V and Qe (SI Table S2). 2 For each Qe multiple linear regression can be performed between ΔGS−W,i and the solute descriptors based on eq 8 to determine the regression coefficients e, s, a, b, v, j−, and c. 3 A relationship can then be developed between each regression coefficient and Qe based on the regressions performed at various arbitrary values of Qe. These relationships will be denoted as fe(Qe), fs(Qe), fa(Qe), etc. for each coefficient. 4 The relationships from step 3 can be combined with eqs 6−8 to get

Ce =

⎛ m ⎞ TOT ·⎜[Cl−]0 + Q e ⎟ ·e f (Q e)/ RT Qc − Qe ⎝ V⎠ Qe

(10)

TOT

where f (Qe) is equal to the right-hand side of eq 9. Thus, given a solute’s Abraham descriptors and a value for Qe, the corresponding Ce value can be obtained for a given resin (known Qc) at any m/V value; selectivity can then be estimated based on eq 7. If the model successfully represents all sorption interactions, the predicted Ce values will be equal to the experimental Ce values obtained from isotherm experiments. Equation 10 can be used to estimate sorption of any similar compound that has descriptor values within the ranges of the descriptors for the training set of compounds used to develop the model. Increasing the diversity of the training set will increase the applicability of the developed model. For neutral compounds, calculation of ΔGS−W,i is similar to our recent work31 with some minor modifications (details in SI Text S2).

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EXPERIMENTAL DETAILS Sorbents and Solutes. Amberlite IRA910 and IRA96 were obtained from Dow (USA). IRA910 is a strongly basic polystyrene AXR with dimethylethanolammonium as the functional group. IRA96 is a weakly basic polystyrene AXR with tertiary amines as the functional group (SI Table S3).

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Figure 1. Effect of pH on the sorption of (a) NB, (b) BS, and (c) BA on neutral resins, MN100 and MN200, and AXRs, IRA96, and IRA910. The solid line in (c) is the model prediction based on eq 11 (see the last section for details) and the dashed lines in (c) are for the mole fractions of BA0 = benzoic acid and BA− = benzoate. Experimental conditions: 1000 μM NB, BS or BA, 20 mL, 40 mg of MN100/MN200 or 65 mg IRA96/IRA910 for NB, 120 mg of MN100/MN200 or 30 mg IRA96/IRA910 for BS, and 65 mg of MN100/MN200 or 50 mg IRA96/IRA910 for BA.

Purolite A860 is also a strongly basic AXR but with polyacrylate matrix and quaternary ammonium functional groups. Purolite MN200 and MN100 are both neutral hyper-cross-linked polystyrene resins with MN200 having no functional groups and MN100 having a small fraction of tertiary amine functional groups (exchange capacity 0.1−0.3 eq/g as reported by the manufacture). Before employed, these resins were subjected to intensive wash by NaOH, HCl solution, and then rinsed with NaCl solution to ensure the AXRs in the chloride form. At last, they were treated with ethanol in a Soxhlet extractor to remove residual impurities. Twenty two organic compounds were obtained from Fisher Scientific and used without further purification. Listed in Table 1 are the solutes, their abbreviations and relevant properties. Sorption Experiments. All sorption isotherms were performed inside amber glass bottles using Teflon-sealed lids at 23 ± 1 °C. Generally, more than 12 experimental data points for each test compound were employed with equilibrium concentration ranging from 0.1 μM to 0.5−15 mM. The ratios of aqueous solution to solids were adjusted to achieve 20−80% sorption of the target compounds. All samples were dosed with 20 mM of NaCl to keep comparable ionic strength. Samples were then periodically adjusted with either NaOH or HCl to achieve the desired pH: pH > pKa+2 for anions and pH < pKa-2 for neutral species. The reactors were then placed on a shaker at 175 rpm for 48 h. After reaching apparent equilibrium, the experimental supernatants were withdrawn for analysis. Concentrations for all solutes were analyzed using highperformance liquid chromatography, ultraviolet/visible spectroscopy, or a total organic carbon analyzer (details in SI Text S3). Nitrobenzene (NB) was selected as a model compound to investigate the pH effect on nonelectrostatic interactions between resins and solutes, and benzenesulfonate (BS) was selected for detecting the pH effect on electrostatic interactions (details in SI Text S4). Additionally, titration experiments were

performed on crushed resin samples to determine pKa values of the ion exchange functional groups (details in SI Text S5). Estimating Abraham Descriptors. Because there are no reported descriptors for flumequine (FLU) and ciprofloxacin (CIP) anions, we started by estimating the descriptors for their neutral species. V was estimated based on the volume contribution of all atoms and the number of bonds in the molecular structure.39 E was estimated from refractive index.37 All other descriptors were estimated based on the group contribution method, where a descriptor can be estimated from the sum of descriptor values of all functional groups in the solute structure.39 The descriptors for FLU and CIP anions were then estimated based on a set of equations developed to predict descriptors for carboxylate anions based on the descriptors for neutral carboxylic acids.36 Two of the obtained descriptors (i.e., E and S) were slightly adjusted by adding FLU or CIP one at a time to the predictive model (eq 10) so that the model precision as reflected by root-mean-square errors (RMSE) between the predicted and the experimental Ce values did not significantly change (data now shown). Estimating descriptors for BS is not possible using this approach because there are no reported descriptors for any sulfonates.

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RESULTS AND DISCUSSION

pH Effect on Buffering and Sorption Capacities of Resins. IRA910 is a strong base AXR and IRA96 is a weak base AXR. Generally, weak AXRs have higher buffering capacities than strong AXRs. SI Figure S1 shows the comparison of buffering capacities of IRA910, IRA96, and MN100−a neutral resin with a small fraction of tertiary amine functional groups. As expected, IRA910 and MN100 demonstrated no significant buffering capacity beyond the background capacity of water, while IRA96, which contains tertiary amine functional groups, exhibits a broad buffering capacity between pH 5 and 9.8. A similar dissociation of amine functional groups over a wide range of pH has been reported for an aminated hyper-cross7497

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linked resin.12 The pKa value for IRA96 was determined to be approximately 6.0 (SI Figure S1). By comparing sorption of a neutral compound with that of an ionic compound onto both neutral and exchange resins, a qualitative understanding of the relative contribution of nonelectrostatic versus electrostatic interactions can be determined. Figure 1 demonstrates the sorption of NB (neutral) and BS (anion) on MN100 and MN200 (neutral resins), IRA96 and IRA910 (AXRs) between pH 2 and 12. The neutral resins exhibited much higher sorption capacities toward the neutral compound (NB) than the AXRs, while the AXRs had higher sorption capacities of the ionic compound (BS). Similar observations have also been reported.18,40 This is due to (1) a greater affinity of the ionic molecules to bulk water molecules than to the surface of the neutral resins, and (2) a synergistic combination of electrostatic and nonelectrostatic interactions in sorption by AXRs (see more discussion in the next section). Additionally, sorption of the neutral compound by all the resins is almost pH-independent between pH 2 and 12. However, this is not the case for the sorption of the ionic compound: sorption of BS by IRA910 is pH independent between pH 2 and 10 but slightly decreased at pH > 10 which is mainly due to the competition of a large amount of OH− for the exchange sites. Sorption of BS on IRA96 is relatively pH independent between pH 2 and 9 and decreased drastically at pH > 9. This agrees with a previous finding that when the amine functional groups are in the free base form, the resin loses its ion exchange capacity and can only sorb a much smaller amount of anions according to eq 2.12 The minor effect of pH on the removal of BS by MN-100 (i.e., higher sorption capacities of BS by MN100 than by MN200 at acidic pH) is because of the small number of amine functional groups present in MN100, which are protonated at acidic pH to enable MN100 to function somewhat as an exchange resin rather than a truly nonionic resin as MN200. Further results show that solute speciation can greatly affect the overall sorption. Figure 1c demonstrates the effect of pH on the sorption of benzoate (BA) by two neutral resins (MN100 and MN200) and two AXRs (IRA910 and IRA96). As shown, a significant drop in sorption capacity with increasing pH occurs for neutral resins which correlates well with the abundance of the neutral species, benzoic acid; while the opposite is observed for the AXRs, that is, sorption capacity correlates well with the abundance of the ionic species, benzoate. This demonstrates the usefulness of AXRs for the removal of AOCs under typical water treatment pH values. Aqueous Sorption Isotherms of Anionic and Neutral OCs on AXRs. The experimental data for the aqueous sorption isotherms of various compounds on IRA910, IRA96, and A860 are illustrated in Figure 2. Based on these figures, a qualitative examination of the selectivities for all anions can be made to understand how resin and solute structure and properties affect sorption. Results in Figure 3 show that the two polystyrene resins (IRA910 and IRA96) have nearly the same order of compound selectivity while the polyacrylate resin behaves quite differently. As compared to IRA910, the weak base exchanger IRA96 has slightly higher sorption capacities. One explanation may be that IRA96 has an ion exchange capacity approximately 12% higher than that of IRA910 (SI Table S3). For the polystyrene resins, the order of the removal of various compound classes under most equilibrium concentrations is as follows: phenolates ≥ aromatic dicarboxylates > aromatic monocarboxylates > BS > aliphatic carboxylates. The

Figure 2. Aqueous sorption isotherms of 22 compounds on IRA910, 15 compounds on IRA96, and 10 compounds on A860. Lines are examples of the D−A model fitting.

selectivity order of phenolates > carboxylates > sulfonate can be well explained by their hydration energies which follow the order of sulfonate > carboxylates > phenolates (Table 1). This finding suggests that, similar to inorganic ions, hydration energy is the dominant factor affecting selectivity of organic anions with different charged groups. Within carboxylates, dianions (structures in SI Figure S2) showed much higher selectivity than monoanions. This can be explained based on the presence of two exchange functional groups in the dianions which significantly increases their binding efficiencies through ion exchange mechanisms. Within monocarboxylates, a poor correlation between hydration energy and selectivity was observed (R2 = 0.45, SI Figure S3), indicating hydration energy alone cannot determine selectivity for organic anions. Much higher selectivities were observed for the aromatic carboxylates than the aliphatic ones, likely due to two primary factors: first, for the aromatic carboxylates, the negative charge on the carboxylates can delocalize to the neighboring aromatic ring resulting in smaller charge densities on the carboxylates which lead to their lower hydration energies and probably weaker electrostatic interactions with the resin exchange groups; second, nonelectrostatic interactions between the NPM of the ions and the resin matrix should also play an important role, that is, the aromatic anions are more hydrophobic and also have specific π−π interactions with the polystyrene resin matrix. The stronger interactions between the NPM of the aromatic anions and the resin matrix thus contribute at least partly to the observed higher selectivity for the aromatic carboxylates. 7498

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primarily located in the macroporous region of the pores,12 a large portion of the sites were believed to be still inaccessible for large ions such as perchlorate.20 The size effect can also explain the sharp decrease in CIP selectivity with increasing sorption capacity. Once CIP anions have bonded with easily accessible exchange groups, further access to exchange groups in smaller pores becomes harder. Although the calculated molecular volumes of IBU and CIP are comparable to or larger than that of FLU (Table 1), their structures are more flexible and likely have easier access to some of the exchange sites. Selectivity is concentration dependent and decreases with increasing sorption capacity for almost all ions (Figure 3). Early work examined sorption of tetraalkylammonium cations by cation exchange resins and observed a similar decrease in selectivity with increasing sorption capacity.41 The authors explained this based on two mechanisms. First, the sorbed ions increasingly interact with themselves with increasing sorption; second, the large sized ions cannot screen the charges on the resin very efficiently so that at higher sorption capacity, there is an increased binding of the smaller counterion (i.e., Na+) and the selectivity for the organic ions becomes smaller. Both mechanisms are applicable to our observed decreasing selectivity with increasing sorption capacity. Sorption capacities of the neutral solutes are much lower than those of the aromatic anions but are higher than those of the two nonchlorinated acetates (Figure 2). This observation agrees with previous findings that there is a strong synergistic combination of electrostatic and nonelectrostatic interactions in the overall interaction force between the counterions and the resin exchange functional groups.19 An example of this is the sorption of 2-NBA by IRA910, where the Qe is 21.3 μmol/g at Ce of 10 μmol/L. If we treat its structure as a sum of NB and acetate and assume there is no additional interaction force beyond what has been involved in the sorption of NB and acetate, we would expect the sorption capacity of 2-NBA to be comparable to the sum of those of NB and acetate. However, at Ce of 10 μmol/L, Qe of NB and acetate are 0.33 and 0.11 μmol/g, respectively, with the sum 2 orders of magnitude lower than that of 2-NBA. Examining sorption capacity of the neutral solutes by the polystyrene resins shows an order of 4-nitroaniline > p-cresol ≈ 4-chloroaniline > phenol > NB. There are three likely reasons: first is the same π−bonding ability of the solutes as discussed above for aromatic carboxylates; second is H-bonding. Because NB is a poor H-donor, it has the lowest sorption capacity; finally, solute−solvent interactions also contribute significantly as sorption reflects differences in interaction forces between solute−sorbent and solute−-water.31 Compared with the polystyrene resins, the polyacrylate resin (A860) showed much lower sorption capacities and its selectivity trends also behaved rather differently. Although selectivity also follows aromatic carboxylates ≈ BS > aliphatic carboxylates, selectivity for all 10 anions is much smaller and within a narrow range (between 0.13 and 1.64, Figure 3c). Removal of neutral solutes by A860 was negligible (data not shown). This difference from polystyrene resins is due to the more hydrophilic nature of the resin matrix. Water contents of the three resins were measured to be 53.4%, 58.8%, and 70.1% for IRA910, IRA96, and A860, respectively (SI Table S3). Water content has been used as a good indicator for hydrophilicity of polymer matrices.20,42 As already reported,19 the polyacrylate matrix of A860 is more polar and hydrophilic and thus does not have as strong of hydrophobic and π − π

Figure 3. Selectivity at different Qe for sorption of the ions onto the three anion exchange resins.

Further trends are observed on the effects of nonionic functional groups on the selectivity of anions. Within the aromatic monocarboxylates, the general preference was nitro- > chloro- > methoxy- groups. This preference in functional groups can be explained by the relative electron withdrawing or donating capability of the group. A stronger electron-withdrawing group will help the compound interact with the resin matrix by increasing its ability to undergo π−π and H-bonding interactions. Therefore, the compounds with nitro-groups have the highest removals because nitro-groups are the strongest electron-withdrawing group. Comparatively, chloro-groups have a slight electron-withdrawing capacity and methoxygroups are slightly electron donating. It was also observed that nitro-groups in the para-position were preferable to the orthoposition. This is because para-nitro- will have additional resonance effects that increase its electron withdrawing capability while ortho-nitro- has intramolecular hydrogenbonding that decreases it. Within the aliphatic carboxylates, DCA has a much higher selectivity than the two nonchlorinated analogues likely due to its stronger dipole−dipole interactions with the resin matrices. Other than simple anionic solutes, sorption of more complex anionic contaminants including ibuprofen (IBU), FLU, and CIP was also examined (structures in SI Figure S2). As shown in Figure 3a, selectivity of IBU and CIP is within the range for other aromatic carboxylates, but the selectivity of FLU is much lower. The low selectivity of FLU is likely due to its bulky size that prevents it from accessing ion-exchange functional groups on the resin matrix. Although amine functional groups are 7499

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Table 2. Regression Coefficients for Multiple Linear Regressions Between lnαA/Cl (All Anions Included, 16 Points for Each Qe) or ΔGS−W,i (All Solutes Included, 21 points for each Qe) and Solute Descriptors for IRA-910 at Qe = 2, 20, 200 μmol/g Qe

c

e

s

lnαA/Cl

2 20 200

−6.83 ± 1.67 −6.23 ± 1.67 −4.28 ± 1.47

4.74 ± 1.78 4.95 ± 1.78 5.01 ± 1.58

0.57 ± 1.02 0.26 ± 1.02 −0.32 ± 0.90

ΔGS−W,i

2 20 200

−18.07 ± 2.15 −17.59 ± 2.15 −13.88 ± 2.78

10.12 ± 2.78 9.97 ± 2.79 8.93 ± 2.94

1.85 ± 1.47 1.51 ± 1.48 0.82 ± 1.61

a

17.93 ± 4.23 17.80 ± 4.23 13.42 ± 6.43

b

v

j−

R2

−1.73 ± 0.84 −1.34 ± 0.84 −1.16 ± 0.78

0.32 ± 0.89 0.15 ± 0.89 0.02 ± 0.78

3.37 ± 1.46 2.98 ± 1.46 2.69 ± 1.29

0.886 0.872 0.854

−4.26 ± 1.72 −3.21 ± 1.72 −2.50 ± 1.69

0.30 ± 1.55 −0.45 ± 1.55 −1.25 ± 1.62

9.06 ± 3.06 8.18 ± 3.06 7.22 ± 3.01

0.916 0.911 0.862

the regression coefficients for the model. The pp-LFER regression coefficients were determined as functions of Qe to allow for a meaningful comparison of solute-sorbent interactions by providing an equal coverage of the sorption sites. After obtaining a set of e, s, a, b, v, j−, c values for each Qe (Table 2 and SI Tables S6 and S7), correlations were performed between each coefficient and Qe to generate equations for the dependence of the coefficients on Qe (SI Figure S4). A quantitative analysis of the relative importance of each coefficient can then be performed. For IRA910, in addition to E, B, and J− being the major contributors to the sorption as shown above, a positive contribution of the A term emerges but applies to the neutral solutes only because all anions have negligible H-bond acidity (A ≈ 0). In addition, with increasing sorption capacity, the absolute values of e, a, b, and j− all decreased. This suggests that at higher sorption capacities, there are smaller contributions from induced dipole (E), dipole (S), and electrostatic (J−) related interactions due to the smaller selectivities. With eq 10, Ce values can now be calculated for a range of compounds at a given Qe and m/V value armed only with the compound Abraham descriptors. Five compounds (CIP, 4-MP, 2,6-DCP, IB, and 4-NBA) were randomly selected from the five groups of the 21 solutes (three complex anions including CIP, FLU, and IBU, 5 neutral solutes, two phenolates, three aliphatic carboxylates, and eight aromatic carboxylates), excluded from the development of the model, and set aside as a “test set”. Figure 4b shows that the developed model for IRA910 (eq 10 and SI Figure S4) based on the training set of 16 solutes can predict the sorption of the five test compounds well and thus validates the model. Additional cross-validations were conducted by including all 21 compounds in the training set (Figure 4a) or choosing different combinations of the solutes as the test-set compounds (examples in SI Figure S5), the obtained similar precisions (i.e., RMSE values) indicate the robustness of the developed model. Note that excluding the descriptors whose regression coefficients are not statistically significant from the model development did not have any significant impact on the model precision (data not shown), but to keep consistent with the literature,27,29,32,33 we chose to include all descriptors in the model. A further increase in the number and types of solutes in the training set will also make the model more robust in predicting sorption capacity for a diverse range of solutes. With the above model for both neutral and anionic solutes, we can estimate the sorption capacity of weak acids that can undergo proton transfer reaction under changes of pH. As ionic and nonionic form of a solute have different sorption behavior, it was assumed that the total sorption capacity at any pH (qT) is equal to the sum of the sorption capacities of all species:

interactions with the organic solutes as the two polystyrene resins do. Another result of this is the much weaker synergistic combination of electrostatic and nonelectrostatic interactions for the polyacrylate resin matrices. Take sorption of 2-NBA by A860 as an example, at Ce of 10 μmol/L, Qe = 1.2 μmol/g, whereas for NB and acetate Qe = 0 and 0.40 μmol/g by the same resin. For the purpose of selective removal of AOCs with large differences in selectivity, polystyrene AXRs are clearly preferred. pp-LFERs. To quantify the relative contribution of various interaction forces to the overall selectivity of a given anion, we turned to pp-LFERs by conducting multiple linear correlations between natural log of selectivity at different sorption capacities and Abraham descriptors. Due to the lack of the descriptors, BS was not included in the correlation. Descriptor A was not included since all anions have negligible H-donating ability. As the results show in Table 2 and SI Tables S4 and S5 for IRA910, sorption from the aqueous phase was promoted mostly by induced dipole-related interactions (large positive e values) and second by electrostatic interactions (positive j− values) and inhibited somewhat by H-accepting capacity of the solutes (small negative b values, marginally significant). The S and V terms have statistically negligible contributions based on the high p-values (SI Table S5). This means anions with large E and J− values will be strongly sorbed by IRA910 while anions with large B values will tend to interact more favorably with water molecules through H-bonding. Note that our own previous work and others have all reported that it is not possible to separate π−π interactions from H-bonding interactions and there is intensive overlap between S and A/ B.31,34 Indeed for all the anions in Table 1, there is a strong correlation between B and J− (r = 0.97), and multicollinearity between S and B, S and J− (correlation > 0.79). Thus, the relative insignificance of B and the insignificance of S may be due to the collinearity. Additionally, we cannot attribute J− alone to electrostatic interactions. It is likely that S, B, and J− all reflect part of the electrostatic interactions. A860 in general is not as selective in the removal of the target compounds, so we did not examine it further. For IRA96, it is more difficult than IRA910 to work with because it has a large amount of tertiary amines as the exchange functional group and thus has a high buffering capacity. As a result, it has become difficult to change its solution pH to outside the 6−7 range which is necessary for a number of solutes. So we only examined a few anions for IRA96. Due to the limited number of solutes examined for sorption by IRA96 and A860, no attempt was made to obtain pp-LFERs for their selectivities. Modeling Sorption Capacity by IRA910. To develop a robust model for prediction purposes, we included five neutral solutes in the correlations between ΔGS−W,i and the descriptors. Table 2 shows an example of the steps involved to determine 7500

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Article

ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in the text. 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: [email protected]. 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 U.S. Geological Survey via a subaward from the Pennsylvania Water Resource Research Center and the Pennsylvania Sea Grant.

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Figure 4. Model development with (a) 21 solutes except BS (303 data points) and (b) 16 solutes in the training set (217 data points), the test set: CIP, 2,6-DCP, IB, 4-MP, and 4-NBA (86 data points). The test compounds in red circles were accurately predicted within the range of the training set of compounds. The solid lines show a perfect prediction, where the predicted values for Ce exactly equal the experimental values; the dashed lines are 0.5 log units above or below the solid lines. N

qT =

∑ αiqi i=1

REFERENCES

(11)

where αi and qi are the sorption capacity and mole fraction for species i. All qi are constant and can be estimated based on the constructed predictive model (eq 10). For example, BA exists as either benzoic acid (neutral) or benzoate (anion); qi for both species can be estimated based on eq 10 and the respective Abraham descriptors (E, S, A, B, and V of 0.73, 0.90, 0.59, 0.40, and 0.932 for benzoic acid43 and see Table 1 for benzoate). After calculating αi for both species at different pH’s, eq 11 was then applied to calculating qT for BA at various pH values. As shown in Figure 1c, the estimated sorption capacities of BA over the tested pH range compare well with the experimental data. This simple eq thus greatly expands the applicability of our model toward estimating the sorption capacity of a given anionic solute at any pH. With the developed models (eqs 10 and 11) applying to different resins in the future, it will become feasible to estimate the sorption capacity and selectivity of a given anionic organic contaminant by an AXR. This information will guide the resin selection process in a given treatment scenario as the current process still involves trial experiments with multiple candidate resin samples. Results from this work will also facilitate the application of resins to other processes where separation and purification of materials are needed. 7501

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