Removing Cadmium Ions from Water via Nanoparticle-Enhanced

Mar 15, 2010 - Breck , D. W. Zeolite Molecular Sieves: Structure, Chemistry, and Use; John Wiley and Sons, Inc.: New York, 1974. There is no correspon...
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Environ. Sci. Technol. 2010, 44, 2570–2576

Removing Cadmium Ions from Water via Nanoparticle-Enhanced Ultrafiltration ANNA JAWOR AND ERIC M.V. HOEK* Department of Civil & Environmental Engineering and California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, California, 90095

Received July 30, 2009. Revised manuscript received January 5, 2010. Accepted January 7, 2010.

Here we evaluate removal of cadmium ions from water by nanoparticle-enhanced ultrafiltration using polymer and zeolite nanoparticles. This evaluation considered nanoparticle physical-chemical properties, metal-binding kinetics, capacity and reversibility, and ultrafiltration separation for a Linde type A zeolite nanocrystals, poly(acrylic acid), alginic acid, and carboxyl-functionalized PAMAM dendrimers in simple, laboratory prepared ionic solutions. The three synthetic materials exhibited fast binding kinetics and strong affinity for cadmium, with good regeneration capabilities. Only the zeolite nanoparticleswerecompletelyrejectedbytheultrafiltrationmembranes tested. Overall, colloidal zeolites performed similar to conventional metal binding polymers, but were more easily recovered using relatively loose filtration membranes (i.e., lower energy consumption). Further, the superhydrophilic colloidal zeolites caused relatively little flux decline even in the presence of divalent cations which caused dense, highly impermeable polymer gels to form over the membranes. These results suggest zeolite nanoparticles may compete with polymeric materials in low-pressure hybrid filtration processes designed to remove toxic metals from water.

Introduction The rising global demand for clean water is challenged by various factors like ever increasing freshwater pollution, more stringent health-based drinking water regulations, and competing demands for clean water from a variety of users (1, 2). At the same time, the increasing levels of heavy metals in the environment represent a serious threat to human health and ecological systems. In nature, metal ions complex with proteins, humic substances, and biopolymers as well as inorganic colloids such as clay particles (3). Mobile and soluble toxic metal species are not biodegradable, and thus, tend to accumulate in living organisms, causing various diseases and disorders. Among others, cadmium is one of the most toxic nonessential heavy metals present in the environment, even at low concentrations. Cadmium readily bioaccumulates giving rise to adverse health effects in humans such as renal disturbances, decreased lung capacity, bone lesions, cancer, and hypertension (4, 5). Toxic metal contaminants, like cadmium, exist naturally in many ground waters, aqueous industrial waste streams (electroplating, smelting, alloy manufacturing, pigments, plastic, battery, * Corresponding author phone: (310) 206-3735; fax (310) 206-2222; e-mail: [email protected]. 2570

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etc.), as well as surface waters impaired by resource extraction activities (mining, refining) (6, 7). Numerous processes exist for removing dissolved heavy metals, including ion exchange, precipitation, adsorption, filtration, and electrodialysis (7). Among various treatment methods ion exchange and adsorption look like the most promising ones when effective, reusable, and relatively lowcost ion exchangers and adsorbents are available. In the past, organic and inorganic particles have been investigated to assess their toxic metal binding abilities (8-10). Standard polymeric complexing agents like low molecular weight polyethylenimine, poly(acrylic acid), and alginic acid are known for their significant binding capacity, while relatively new dendritic polymers provide new opportunities to develop high-capacity nanoscale chelating agents for environmental applications (11). Many researchers also have found that living biomaterials, such as algae, fungus, and bacteria, have the ability to adsorb heavy metal ions from environment (12). Among inorganic materials, zeolites are known for their ion exchange properties (10), but recently zeolite synthesis has advanced to include formation of nanoscale crystals with precisely tailored physical-chemical properties; thus, significantly increasing their potential effectiveness in an array of chemical, biomedical, and environmental applications (13). To make the toxic metal removal process most efficient and effective, nanoparticles for complexation with metal ions have to possess a few specific characteristics: (1) high affinity toward the target metal, (2) low affinity toward nontarget metal ions, (3) possibility of regeneration, (4) chemical and mechanical stability, (5) low toxicity, and (6) low cost. A critical step is the nanoparticle separation, which could be achieved by low pressure membrane processes in a manner resembling polymer enhanced ultrafiltration (PEUF) (14, 15). For nanoparticle-enhanced ultrafiltration (NEUF), membrane characteristics like MWCO, physical-chemical properties (pore size, charge, material type, hydrophobicity, etc.), and fouling resistance, as well as energy consumption play an important role. The current study focused on elucidating the relative abilities of four nanoscale materials to bind cadmium ions, and subsequently, to be removed from water by ultrafiltration. Kinetics, binding capacity, and regeneration of synthesized Linde type A (LTA) zeolite, poly(acrylic acid), alginic acid, and PAMAM dendrimers nanoparticles were tested at various experimental conditions. Separation of nanoparticle metal complexes was investigated using ultrafiltration membranes with molecular weight cutoff ranging from 5 to 100 kDa. The discussion sheds some light on the fundamental physicalchemical mechanisms governing nanoparticle-enhanced filtration and its potential for practical application.

Materials and Methods Nanoparticle Characterization. Linde type A (LTA) zeolite nanoparticles were synthesized using a microwave heated hydrothermal process as previously described (16). Poly(acrylic acid) (PAA), alginic acid (AA), and carboxylic acid functionalized dendrimers (PAMAM) were purchased from Sigma Aldrich (St. Louis, MO). These materials had molecular weights of 70,000, 46,000, and 41,000 Da, respectively. Hereafter, all four materials will be referred to as “nanoparticles” due to their characteristic nano-size when dispersed in water. Suspension of each nanoparticle was analyzed by dynamic light scattering (DLS, Brookhaven Instruments Corporation, USA) to confirm the size and dispersibility of the particles. Zeta potentials of the nanoparticles were determined from measured electrophoretic 10.1021/es902310e

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mobility (ZetaPALS, Brookhaven Instruments Corp. Holtesville NY) via Smoluchowski equation (17). All nanoparticles were dispersed in 10 mM NaCl solution and the pH was adjusted to 7.0 for the zeta potential and dynamic light scattering analyses. Nanoparticle hydrophilicity was examined using a contact angle goniometer (DSA 100, Kru ¨ ss Instruments, Nazareth, PA). To perform this analysis, the LTA zeolite nanoparticles were grown on a metal plate to produce a continuous zeolite film, as described previously (18). Polymeric materials where deposited on a nanofiltration membrane and analyzed after drying for 24 h at room temperature in a laboratory desiccator (19). At least ten equilibrium contact angles were measured for each material and average values were calculated after discarding the lowest and highest values. Using the multiple probe liquids, surface tensions and interfacial free energies were calculated as described previously (20, 21). The specific surface area of the zeolite nanoparticles was measured using nitrogen adsorption and BET analysis (22). Binding Kinetics, Capacity, and Regeneration Experiments. Binding kinetics and binding capacity curves were generated by titrating nanoparticle suspensions with concentrated solution of cadmium chloride as described previously (23). Kinetic studies were used to elucidate rate constants for the various nanoparticles and cadmium ions. For kinetic analysis, a mass of cadmium was added to the aqueous nanoparticle dispersion. The change in cadmium concentration with time was measured by ICP. Binding capacity was obtained from titration of nanoparticles with fixed amount of cadmium chloride salt. Titration curves show the concentration of free cadmium ions versus the total amount of cadmium added. As the slope of the plot approaches unity, it is inferred that the total binding capacity was exceeded. Tracing the line of unity slope back to the X-intercept gives the apparent maximum binding capacity of given nanoparticle for cadmium cations. All experiments were performed at pH 7.0 by addition of NaOH or HCl and with total ionic strength of 10 mM obtained by addition of NaCl. All chemicals and salts used were reagent grade. Binding kinetic and capacity experiments were performed in a stirred membrane reactor (Millipore, Billerica, MA) with 5 kD polyethersulfone (PES) membrane at the bottom of the reactor. Pressure was provided by compressed N2 tank, through a stainless steel vessel equipped with a valve controlling pressure flow to the reactor. Three hours before each experiment, 0.05 g of nanoparticles was mixed with 240 mL of 10 mM NaCl solution. In the case of zeolites, an additional 1 h of ultrasonication was applied right before each experiment was performed. Before moving the solution to the experimental cell, pH was adjusted using NaOH or HCl. The volume in the reactor was then raised to 250 mL by addition of 10 mL of cadmium with fixed concentration, to obtain 5 mg/L total metal concentration, and kinetic studies were performed. Every 2 min, 1 mL of permeate was collected for ICP (TJA Radial IRIS 1000 ICP-OES, Thermo Scientific, Waltham, MA) analysis to monitor cadmium concentration versus time. For every 1 mL of permeate withdrawn for ICP analysis, 1 mL of fresh solution was added to the reactor to maintain constant volume. Also, for each binding kinetic experiment, a blank experiment was performed with cadmium concentration of 5 mg/L with no nanoparticles to evaluate potential sorption of cadmium to the membrane or other components of the experimental system. After the last sample for kinetic experiments was taken, reactor volume was replenished by addition of 10 mL of solution with fixed cadmium concentration of 5 mg/L through the inlet port. The binding capacity titration was continued by injection of 1 mL of solution, providing net cadmium concentration increase of 5 mg/L with each injection, and

1 mL samples were collected for ICP at 20 min time intervals to allow equilibrium to be achieved. This routine maintained a constant volume of solution in the reactor and nanoparticles concentration through the entire experiment. Before ICP analysis, each sample collected was acidified using 10% HNO3, and GFS Chemicals standards (Powell, OH) were used during elemental analysis. All experiments were conducted at room temperature (22 °C), and constant mixing of 300 rpm was provided. Regeneration of nanoparticles was attempted immediately after binding experiments. Regeneration of polymeric materials was accomplished by HCl acid titration. Samples were collected at pH 7, 5, 3, and 1 for ICP analyses to measure the free cadmium concentration (24, 25). After the binding studies, zeolite nanoparticles were separated from the solution by centrifugation at 7000g. The nanoparticles were then immersed in 1 M NaCl, and samples for ICP analysis were collected after 1, 4, 8, and 24 h to measure the concentration of free cadmium released from zeolite nanoparticles (26, 27). Membrane Characterization. Commercially available polyethersulfone (PES) membranes (Millipore, Billerica, MA) with MWCO of 5, 30, and 100 kDa were used for nanoparticle filtration experiments. Membranes surface tensions and interfacial free energies were determined from measured contact angles using an automated contact angle goniometer ¨ SS GmbH, Hamburg, Germany), using the (DSA10; KRU approach described previously (19, 27). Morphological characterization of the membranes was carried out using scanning electron microscopy (SEM, Philips XL30-FEG). Before the analysis, dry samples were sputter coated for 20 s with a mixture of gold and palladium. Membrane surface roughness was determined for air-dried samples by tapping mode AFM (Digital Instruments, Santa Barbara, CA) in air using a silicon nitride probe as described previously (28). Filtration Experiments. Filtration experiments were performed in a dead-end, stirred ultrafiltration system. The filtration unit comprised a 300 mL glass stirred cell (Millipore, Billerica, MA) with membrane area of 40 cm2. Pressure was provided by a compressed N2 tank. The permeate samples were collected every 5 to 10 min and their mass was monitored by analytical balance. Permeate flux was calculated from the change in the mass of permeate water (with the assumption that the experimental solution has a density of 1000 kg/m3) as a function of time. To investigate nanoparticle rejection and flux decline, the experiments were performed with 0.2 g/L of nanoparticles, at pH 7, and ionic strength of 10 mM NaCl. One hour before each experiment, 50 mg of cadmium was added to evaluate the influence of cadmium ions on filtration parameters. All experiments were performed with constant initial permeate flux of 135 L/m2 · h. Before each experiment, membranes were compacted with 18 MΩ · cm deionized water and pure water flux was measured at various pressures. After each experiment fouled membranes were carefully removed from the experimental cell and placed in a desiccator for drying prior to SEM analysis. The poly(acrylic acid), alginic acid, and dendrimer concentrations in the feed and permeate were determined by total organic carbon (TOC) analysis (TOC Combustion Analyzer, Apollo 900, Tekmar-Dohrmann, Mason, OH). In the case of zeolite nanoparticles IPC (TJA Radial IRIS 1000 ICP-OES, Thermo Scientific, Waltham, MA) analysis was employed. Continuous mixing at 600 rpm and temperature of 22 °C were maintained in all experiments. According to Baker (29), porous membranes can foul through various mechanisms that include (a) internal pore constriction from adsorption of small solutes onto pore walls, (b) external (complete or partial) blocking of pores by initial particle deposition, and (c) cake (or gel) layer formation over VOL. 44, NO. 7, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Physical-Chemical, Binding, and Energetic Properties of Nanoparticles nanoparticle (-)

MW (g/mol)

dp (nm)

ζp (mV)

zeolite polyacrylic acid dendrimer alginic acid

n/a 70,000 41,000 46,000

180 ( 6 14 ( 2 6(4 7(2

-64 ( 2 -14 ( 3 -16 ( 4 -29 ( 2

θw (deg) kcd2+ (a) qcd2+ (mgcd2+/gNP) YCd2+/qCd2+ (%) 2(1 39 ( 6 12 ( 2 53 ( 4

2.92 2.13 1.53 0.46

147 180 169 87

92 97 93 78

-∆G1wTOT ∆G1w1TOT ∆G1w2TOT (mJ/m2) (mJ/m2) (mJ/m2) 145.5 129.4 143.9 116.4

104.7 28.7 109.8 -29.4

30.1 -17.8 19.0 -41.3

a Units for rate constants: second order, mg · L-1 min-1 (zeolite, poly(acrylic acid), dendrimer); first order, min-1 (alginic acid).

top of the membrane. In all cases, flux declines according to J ) J0(1 + kt)-n

(1)

where J0 is the initial flux, J is the flux at a given time, t is the time, k is an empirical rate constant, and n is a theoretical power corresponding to the fouling mechanism. According to theory, the value of n is 0.5, 1.0, 1.5, or 2.0 when the fouling mechanism is cake formation, internal pore constriction, partial pore blocking, or complete pore blocking, respectively. In this study, we used the k-value as a fitting parameter to fit each form of eq 1 to the experimental flux decline data. In some cases, flux decline data were fit over different timesegments with different models (n-values). For example, initial rapid flux decline was fit with the complete and/or intermediate pore blocking forms while the relatively steady, shallow flux decline over longer times was fit with the cake filtration form.

Results Physical-Chemical Properties of Nanoparticles and Membranes. The zeolite nanoparticles examined in this paper are submicrometer sized alumino-silicate crystals containing pores and cavities of molecular dimensions able to exchange metal ions (27). Poly(acrylic acid), alginic acid, and carboxylic acid functionalized dendrimers had molecular weights of 70, 46, and 41 kDa, respectively. Poly(acrylic acid) is a chemical compound comprising an unsaturated carboxylic acid with a vinyl group. Alginic acid has high carboxylate content from mannuronic and guluronic acid, which comprise its structure (30). The PAMAM dendrimers selected for this study are hierarchically structured polymers with a low polydispersity and a high density of carboxylic acid groups on their surface. As presented in Table 1, dynamic light scattering analysis estimated the size of LTA zeolites to be around 180 nm. Among the polymeric nanoparticles, PAA was the largest (∼14 nm) while dendrimers and AA were around 6 and 7 nm, respectively. At 10 mM NaCl and pH 7, the zeolites were most negatively charged (zeta potential was -64 mV). The polymers were negatively charged with zeta potentials of -14, -16, and -29 mV for PAA, PAMAM, and AA respectively. The UF membranes used for separation of nanoparticlemetal complexes from solution were characterized in terms of their surface morphology, charge, and energy. According to pore size estimates from MWCO values reported in the literature (7, 33), the 5, 30, and 100 kDa MWCO membranes had characteristic pore diameters of around 2.3, 5.2, and 9.1 nm, respectively. It is, however, important to note that these are not “absolute” because PES membranes can have a wide distribution of pore sizes; hence, more quantitative analysis of particle rejection from pore size is difficult (32). The AFMderived rms roughness values were 22, 8, and 2 nm for the 100, 30, and 5 kDa membranes, respectively. Metal Binding Kinetics, Capacity, and Regeneration. To elucidate the kinetics of cadmium sorption first and second 2572

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order kinetic models were fitted to the experimental data (7). Alginic acid was best fit with the first order model, whereas poly(acrylic acid), dendrimers, and zeolites were best fit with the second order model. Rate constants, kCd2+, from kinetic analyses are presented in Table 1. The fast binding kinetics of poly(acrylic acid) and dendrimers are attributed to their solubility and high content of easily accessible carboxylic functionality, which complexes with divalent cations (9, 15, 31). However, the LTA zeolites had even higher binding kinetics because of their hydrophilicity and submicrometer size, which makes them stable in water (like the polymers) and offers fairly short diffusion path lengths to access internal binding sites (32). Additionally, the high specific surface area, 616 m2/g, creates a large density of exchange sites per unit mass of zeolite. Binding capacity curves, which relate free cadmium concentration to the total cadmium concentration present in the experimental system, indicated that at pH 7 poly(acrylic acid) had the highest binding capacity, qCd2+, of almost 180 mg/g, followed by 169 and 146 mg/g for PAMAM dendrimers and zeolite LTA nanoparticles, respectively. Significantly lower binding capacity, 87 mg/g, was observed for alginic acid. As presented in Table 1, the full binding capacity was not completely recovered for any of the materials. Polymeric nanoparticles were regenerated by changing the pH of the solution to cleave the polymer-metal bond (14). The recovered binding capacity, YCd2+/qCd2+, for PAA required pH of 2, at which virtually all acidic sites were protonated, so metal-polymer complexes were not strongly formed (25). A similar situation was observed for the dendrimers, while alginic acid exhibited the lowest recovery of binding capacity. More than 90% of zeolite binding capacity was recovered by immersing the zeolites in a 1 M NaCl solution. The high concentration of sodium drove the re-exchange of sodium into the crystals with release of cadmium (26). Interfacial Properties of Nanoparticles and Membranes. Using contact angles from multiple probe liquids, one can calculate solid surface tension parameters for membranes and foulants (20). Solid and liquid surface tensions can then be combined to produce interfacial free energy parameters that quantitatively describe wettability, hydrophilicity, and fouling propensity (19). The solid-liquid interfacial free TOT energy (-∆G1W ) represents the energy of wetting for water TOT on a solid substrate. A solid material is wetting if -∆G1W is greater than the total surface tension of the liquid and nonwetting if the energy is less than the surface tension. The TOT interfacial free energy of cohesion (∆G1W1 ) represents the nanoparticle-nanoparticle or membrane-membrane interaction energy (at contact) when these materials are immersed in water. A negative value indicates self-attraction (i.e., “hydrophobic”) and a positive value indicates selfrepulsion (i.e., “hydrophilic”). The interfacial free energy of TOT adhesion (∆G1W2 ) represents the interaction energy (at contact) between the nanoparticles and PES membranes immersed in water, i.e., the fouling propensity.

FIGURE 1. Observed rejections and applied pressures for separation of nanoparticles at a constant initial flux of 135 L · m-2 · h. Interfacial free energies for nanoparticles and membranes are provided in Table 1. In summary, PES membranes were negatively charged and wetting, but moderately hydrophobic; PAA, PAMAM, and zeolites were negatively charged, wetting, and hydrophilic; while alginic acid was negatively charged, wetting, and moderately hydrophobic. From the adhesive free energies, alginic acid and poly(acrylic acid) were expected to exhibit high fouling propensity for the PES membranes. In contrast, the dendrimers and zeolites should have low fouling propensity due to their strongly hydrophilic character. Of course, electrostatic double layer interactions and divalent cation bridging can complicate these relatively simple classifications of fouling propensity (21, 34). Ultrafiltration of Metal Binding Nanoparticles. Filtration experiments were performed for every combination of the four nanoparticles and three PES ultrafiltration membranes with different MWCOs. Figure 1 presents observed nanoparticle rejection and applied pressure required to generate the same initial flux for each membrane. The 5, 30, and 100 kDa membranes required operating pressures of about 550, 100, and 10 kPa (∼80, ∼14, and ∼2 psi). Colloidal zeolite crystals were completely rejected by all three ultrafiltration membranes. The 5, 30, and 100 kDa membranes rejected (a) dendrimers about 90, 70, and 30%, (b) poly(acrylic acid) about 99, 95, and 60%, and (c) alginic acid about 99, 95, and 70%, respectively. Partial rejection of organic macromolecules can lead to internal pore constriction and external pore blockage, which effectively reduced the pore size and porosity of the membrane, respectively (33). Figure 2 compares the experimental permeate flux decline with time (symbols) for every combination of nanoparticle and membrane, in addition to model fits (lines) using the fouling models described in eq 1. All experiments were performed with identical initial flux and stirring rate to ensure stable mass transfer conditions. Irrespective of the nanoparticle tested, the 5 kDa membranes suffered the lowest flux decline, while the 100 kDa membranes suffered the greatest flux decline. Mechanistically, membranes with larger pores should suffer more flux decline due to internal pore constriction and external pore blocking than membranes with smaller pores. The zeolites produced the least flux declinesonly about 10 to 20%sbecause of their large size and complete rejection, which minimized pore constriction and blocking. The polymeric materials produced much more flux decline, approaching 50% or more over the same filtration time most likely due to their partial rejection, which led to more internal fouling. The open square symbols in Figure 2 indicate that cadmium exacerbated flux decline during filtration of polymeric nanoparticles by the 30 kDa membranes. This negative influence was most visible for alginic acid, less so for poly(acrylic acid) and dendrimers, and not at all for

zeolites. Flux decline by poly(acrylic acid) and dendrimers increased by 10-20% when cadmium was present, while alginic acid flux decline was almost 90% greater in the presence of cadmium. Recent studies have demonstrated that divalent ions play a very important role in natural organic matter (NOM) fouling of UF and NF membranes (35). The fouling behavior is attributed to the complexation of divalent ions with carboxylic acid functional groups of NOM, which leads to a more compact deposit layer offering larger hydraulic resistance to permeation. None of the filtration models described in eq 1 reasonably fit the flux decline data over the entire time of filtration. Visually, experimental data suggest a rapid initial loss of flux followed by a relatively slow continuous loss of flux over longer time. The initial rapid loss of flux could be from concentration polarization (i.e., osmotic pressure losses) or some type of pore fouling (i.e., pore constriction or pore blocking). At longer times, although the magnitude of water flux was different, the slope of flux decline was virtually identical for all three membranes. This suggests a common fouling mechanismscake formation for zeolites and gel formation for the polymers. The zeolites and polymers were sufficiently large that (on average) they did not produce a significant trans-membrane osmotic pressure drop; assuming a membrane surface concentration 10× the feed concentration, the osmotic pressure of the polymers was less than 0.7 kPa (0.1 psi) in all cases. The first few flux data points from each fouling experiment were equally well-fit using all of the pore blocking models, eq 1 with n ) 0.5, 1.0, or 1.5 as well as an arbitrary exponential decay model, while the latter fluxes were wellfit with the cake filtration model. Therefore, we concluded some type of pore constriction or pore blocking acted over the first few minutes of filtration, but little insight beyond this could be derived about the relative impacts of internal pore constriction versus external pore blockingsor the type of pore blocking (i.e., complete versus partial). Figure 3 presents images of clean and fouled 100-kDa membranes. In all cases, a thick deposit layer formed over the membranes. The zeolite nanoparticle fouling layer looked like a colloidal deposit and was relatively porous, while polymeric deposit layers had a relatively dense “gel like” appearance. Some cracks formed in the polymeric gel layers during the drying step of SEM sample preparation. For example, the upper right corner of the dendrimer gel layer (Figure 3c) cracked when it dried out and the gel layer substructure was revealed. In the case of zeolites, the porous nature of the deposit layer largely explains lower observed flux decline; however, the complete rejection of zeolites also minimized pore blocking and pore constriction. In addition, the dense structure of polymer gels that formed on the membrane produced much higher hydraulic resistance than the zeolites.

Discussion Performance of Zeolitic and Polymeric Nanoparticles. Nanoparticles to be used in nanoparticle-enhanced ultrafiltration processes must possess a few specific properties such as rapid binding kinetics, high binding capacity and strong selectivity for the target contaminant, as well as high rejection by low pressure membranes, easy (and cheap) regeneration, and low fouling propensity. The results presented here suggest zeolites had slightly lower initial binding capacity, but faster binding kinetics than the synthetic polymers. Zeolites suffered greater loss of initial binding capacity by regeneration than the polymers. Here, we conducted only a single regeneration for comparative purposes; additional study should consider loss of binding capacity of zeolites versus polymers over multiple regeneration cycles. One potential benefit of VOL. 44, NO. 7, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Flux data and fouling model fits for filtration experiments using PES membranes with molecular weight cut-offs of 5 (blue 2), 30 (dark green 0), and 100 (red b) kDa. Nanoparticle filtration experiments with 30 kDa membranes also considered (light green 0) cadmium ions presented in feed solution. zeolite nanoparticles over polymers is the use of salt for regeneration rather than acid. It is obviously safer and easier to handle salts than acids. This may offset the drawback of a slightly lower binding capacity. Based on the cost of the materials used in this study, we conclude that both dendrimers and zeolites are relatively expensive compared to poly(acrylic acid) and alginate. Rejection data strongly correlated with particle size, but the “flexiblility” of polymers may allow transport through relatively small membrane pores by deforming their shape, which is not reflected by their molecular weight or hydrodynamic diameter. Clearly the economics of a membrane process are related to the operating pressure, which makes looser UF or MF membranes more attractive when complete rejection is possible. Only the zeolites were completely rejected by the 100 kDa membranes. Looking ahead toward more practical application of nanoparticle-enhanced ultrafiltration, zeolites potentially offer another advantage because they were completely rejected by more energy efficient UF membranes, whereas all three polymeric materials would require nanofiltration for complete recovery. Fouling by Zeolitic and Polymeric Nanoparticles. Generally, membranes with high intrinsic hydraulic resistance experience less flux decline from colloidal fouling than membranes with low intrinsic hydraulic resistance because the relative loss of flux is proportional to the ratio of fouling layer resistance to membrane resistance (36). Membranes with lower MWCO have higher membrane resistance and will experience less flux decline than a higher MWCO membrane when covered by the same fouling layer. However, partial rejection of the polymeric nanoparticles can also cause internal pore constriction and external pore blockage, which effectively reduce the pore size and porosity of a membrane (33). 2574

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The PES membrane used here was moderately wetting TOT TOT TOT > γw ), hydrophobic (∆G1w1 < 0), and fouling prone (-∆G1w TOT (∆G1w2 < 0). The surface charge of polymeric membranes, described here by the zeta potential, can also impact UF membrane fouling (37, 38). In general, membranes with a more negative zeta potential are more resistant to fouling by negatively charged particles; however, charge functionality can also lead to calcium bridging if carboxylic acid functionality is present in both the membrane and foulant, i.e., polyamides and NOM (34). Polyethersulfone (PES) membranes comprise sulfonyl groups, ether groups, and benzene rings, which offer both hydrophilic and hydrophobic segments, but no carboxylic acid functionality. Therefore, any effects of divalent cation bridging can only be attributed to denser fouling layer structure through nanoparticlenanoparticle interactions. Among the nanoparticles characterized, the LTA zeolite and dendrimers were the most hydrophilic, while alginic acid was the least hydrophilic material. All of the polymeric nanoparticles contained significant carboxylic acid functionality, and hence, fouling was exacerbated by the addition of cadmium. However, the zeolite nanoparticles do not have carboxylic acid functionality on their surfaces. Fouling by zeolite nanoparticles was not impacted by the presence of divalent cations. The highest flux decline was observed for alginic acid, which was expected from the interfacial free energy parameters described above. Practical Water Treatment Implications. While these early results are promising, the data derived from these simple, well-controlled laboratory experiments need to be expanded greatly to understand the potential for nanoparticle-enhanced ultrafiltration in practical water treatment scenarios. Herein, colloidal zeolite crystals exhibited competitive binding kinetics, capacity, and regenerationsrelative to natural, conventional,

FIGURE 3. Surface SEM images of 100 kDa ultrafiltration membranes before fouling (a) and after fouling by (b) alginic acid, (c) dendrimers, (d) poly(acrylic acid), and (e) LTA zeolites. and dendritic polymerssevaluated here for removal of cadmium ions. Before more practical application can be considered, additional laboratory experimentation is needed to elucidate the effects of competing divalent ions like calcium and magnesium. Also, additional experiments should compare the operational advantages of cross-flow versus dead-end filtration, as well as hydraulic backflushing and chemical cleaning for fouling control. Finally, filtration studies must consider real waters where dissolved organic matter and hardness ions will exacerbate membrane fouling and complicate nanoparticle recovery and regeneration.

Acknowledgments This research was supported in part by the University of California Toxic Substances Research and Training Program: Lead Campus on Nanotoxicology and the United States Environmental Protection Agency through the Desalination

Research Innovation Partnership, which is managed by the Metropolitan Water District of Southern California.

Supporting Information Available Information on the contact angle measurement, calculations of the solid-liquid interfacial free energy, polar and apolar components of the solid interfacial tension, and interfacial free energy of adhesion and cohesion. Also, membrane and nanoparticle contact angles, surface tensions, and surface energies (Table S1), schematic of stirred dead-end filtration system (Figure S1), cadmium ion exchange kinetics for various nanoparticles (Figure S2), cadmium binding capacities of nanoparticles (Figure S3), regeneration of nanoparticles (Figure S4), exemplary blocking and cake filtration model fitted to flux decline data for PAA and the 5 kDa UF membrane (Figure S5), and exemplary blocking filtration model fitted to initial flux data with cake filtration model fit to long-term VOL. 44, NO. 7, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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flux data for PAA and the 5 kDa UF membrane (Figure S6). This material is available free of charge via the Internet at http://pubs.acs.org.

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