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Adsorption of Contaminants of Emerging Concern from Aqueous Solutions using Cu Amino Grafted SBA-15 Mesoporous Silica: Multi-Component and Metabolites Adsorption 2+

Krisiam Ortíz-Martínez, Doris Vargas Valentin, and Arturo J. Hernandez-Maldonado Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b05168 • Publication Date (Web): 13 Apr 2018 Downloaded from http://pubs.acs.org on April 16, 2018

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Industrial & Engineering Chemistry Research

Adsorption of Contaminants of Emerging Concern from Aqueous Solutions using Cu2+ Amino Grafted SBA-15 Mesoporous Silica: Multi-Component and Metabolites Adsorption

Krisiam Ortiz-Martínez, Doris A. Vargas-Valentín and Arturo J. Hernández-Maldonado*

Department of Chemical Engineering, University of Puerto Rico-Mayagüez Campus, Mayagüez, Puerto Rico 00681-9000

* To whom correspondence should be addressed: Phone: 787-832-4040 x3748; Fax: 787-834-3655; E-mail: [email protected]

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Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Abstract SBA-15 and Cu2+ amino grafted SBA-15 adsorbents (CuNH2_g_SBA-15) were prepared to investigate the equilibrium and dynamic adsorptive removal of a set of contaminants of emerging concern (CECs; carbamazepine, caffeine, clofibric acid, salicylic acid, and naproxen) from water. Multi-component and CEC metabolites adsorption was also evaluated to elucidate effects on adsorption capacity and selectivity. Equilibrium adsorption studies showed that CuNH2_g_SBA-15 is capable of removing anionic and acidic CECs even at trace level concentrations. However, multi-component tests revealed a decrease of 30% in adsorption capacity in the case of the acids. The CuNH2_g_SBA-15 variant was the less affected by multicomponent competitive adsorption, probably due to the ability of this adsorbent to interact with CECs through several, simultaneous adsorption mechanisms (i.e., electrostatic interaction, hydrogen bonds, and metal coordination complexes). These were confirmed via X-ray photoelectron spectroscopy (XPS) analyses. Preliminary fixed-bed adsorption tests showed that beds made CuNH2_g_SBA-15 are prone to mass-transfer resistance in comparison to those made of unmodified SBA-15, causing a decrease in both adsorption capacity and degree of bed utilization. Adsorption of parent/metabolites CEC binary mixtures resulted in a reduction in adsorption capacity of the parent CECs, but the adsorbents showed a remarkable selectivity toward the metabolites, particularly CuNH2_g_SBA-15. In general, this study provides substantial evidence that the anchoring of amine/copper species onto the surface of mesoporous silica may provide a platform for the development of CEC selective adsorbents for water treatment.

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1. Introduction Several efforts have been dedicated to the development of advanced technologies for water remediation, particularly for the removal of contaminants of emerging concern (CECs).1-7 Adsorption processes have been one of the most extensively studied,6, 8-12 mainly because of its simplicity of design and the possible absence of toxic by-products. Adsorbents can efficiently handle trace levels of CECs with selectivity upon proper functionalization and fixed beds made from these adsorbents can be used sequentially coupled to other advanced treatment processes.11, 13-15

Despite these advantages and the progress reported in the literature for the development of

new adsorbents, there is a tremendous need for more studies based of multi-component CEC adsorption and selectivity to help identify variables or information that could bridge the gap between lab scale testing and large-scale implementation. CEC matrices, for instance, can be complex in nature and this has to be considered when designing or testing adsorbent candidates. Analytical techniques have paved the way for proper identification and measurement of actual concentration levels in the environment,16-19 and have revealed significant concentrations of CEC metabolites.13,

20-23

In recent studies, the presence of metabolic byproducts in water has been

detected in concentrations larger than their parent compounds24-26 and most of these metabolites are biologically active (i.e., more toxic than the parent) with the potential to produce further toxic compounds when conjugated with dissolved species in water. The toxic compounds include other metabolic byproducts, organic/inorganic materials, transformation products from other conventional water treatments, and even chemicals currently used for water disinfection 27-29. Previous efforts for the development of CEC adsorbents have focused predominantly on the testing porous adsorbent materials such as zeolites, activated carbon, clays, metal-organic frameworks (MOFs), mesoporous silica materials, and porous composites.4, 6, 8-12, 14, 15, 30-43 For

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instance, recent studies conducted by Bhadra et al. employed carbons derived from a ZIF-8 (MOF) to adsorb binary mixtures of ibuprofen and diclofenac, and found that the adsorption capacity can be tailored based on the carbonization temperature.11 This enhances surface area and forms surface acidic and basic functional groups that impart selectivity. In another recent report, Arya et al. demonstrated that clay-composite adsorbents made of activated carbon, chitosan and iron nanoparticles (Fe3O4) can be used for the uptake of several pharmaceuticals CECs, both in single- and multi-component fashion.8 The combination brought greater hydrophobicity to the clay surface, more effective adsorption sites, and the possibility of recovery of the spent adsorbent using an external magnetic field. Hernandez-Maldonado and coworkers have developed and studied transition metal containing porous composite adsorbents for the uptake of CECs, with an adsorption interaction mechanism governed by the choice of transition metal.30, 31, 34, 35, 40, 44, 45 Among the composites studied, it was demonstrated that the incorporation of both the amino-organic groups and the copper cations onto mesoporous SBA-15 silica added the ability to form unique interactions with specific CECs, ranging from specific electrostatic and complexation interactions, and significantly improving single component adsorption capacity. In addition to these enhancements, it was demonstrated that the presence of the amino-organic groups produced a significantly hydro-stable surface due to the effective blockage of water from entering micropore cavities present on the mesopore walls. However, the efficacy of these transition metal-based SBA-15 adsorbents for the handling of multi-component CEC mixtures has not been addressed. The main objective of the present work is to assess the equilibrium and dynamic adsorptive removal of a set of CECs from water using copper amino grafted SBA-15 adsorbents (i.e., CuNH2_g_SBA-15), with emphasis on the separation of multi-component CEC feeds. The

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CECs evaluated were carbamazepine (CBZ), caffeine (CFN) and naproxen (NPX) as parent compounds, and salicylic acid (SA) and clofibric acid (ClofA) as metabolites of aspirin and clofibrate compounds, respectively. These were chosen based on their physicochemical properties, water occurrence and persistence in aquatic environments. Since very little is known about the influence of CEC metabolites on capacities and selectivity in adsorption-based treatment, the results also include binary equilibrium adsorption tests for metabolites (Odesmethylnaproxen (O-DNPX), paraxanthine (PX), and carbamazepine-10,11-epoxide (EPCBZ)) and parent CECs. Finally, an attempt was also made to elucidate adsorption mechanisms based on X-ray photoelectron spectroscopy (XPS) analyses.

2. Experimental Section 2-1 Regents and Material Preparations SBA-15 and Cu2+ amino grafted SBA-15 (CuNH2_g_SBA-15) were prepared according to previous reported methods.35, 46 Reagents used for the synthesis and surface modification of the mesoporous silica adsorbents were the following: distilled/deionized water, Pluronic P123 Surfactant, hydrochloric acid (HCl, 37wt%, ACS Reagent), tetraethyl orthosilicate (TEOS 98 wt%), toluene (anhydrous, 99.8%), (3-amino-propyl) triethoxysilane (3-APTS, 98%), dichloromethane (99.5%, ACS Reagent), diethyl ether (98%, ACS Reagent), ethanol (denaturized), and copper(II) sulfate (anhydrous, 99.99%). Reagents used for the determination of CEC concentrations using the HPLC-MS/MS system were: water (LC-MS Grade), acetonitrile (LC-MS Grade), methanol (LC-MS Grade), formic acid (LC-MS Grade), and ammonium acetate (LC-MS Grade). All reagents were obtained from Sigma-Aldrich and were used without further purification. Selected physicochemical properties

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of the adsorbates are listed in Table 1. The water employed during the adsorption tests was both distilled and deionized. SBA-15 and CuNH2_g_SBA_15 adsorbents were characterized according to the procedures described in the Supplementary Material. 2-2 Equilibrium Adsorption Tests Adsorption equilibrium experiments for each CEC were conducted in batch mode. For each experiment, 0.015 g of the adsorbent were mixed with 15 mL of the aqueous solution with initial concentrations of the corresponding CECs (1, 5, 10, 15, 20, 50 and 100 µg L-1) inside a 50 mL borosilicate centrifuge tubes. The initial solution pH was adjusted with 0.1 M NaOH or HCl solutions in order to reach a final nearly neutral equilibrium pH (ca. 6 – 7 range). The tubes were shaken at room temperature for 48 hours, which was adequate to reach equilibrium, as determined in uptake tests as a function of time (Supplementary Material, Figure S1). The solids and aqueous phases were separated via centrifugation at 8,500 rpm for 10 min. Tests were performed in triplicates. Samples were taken from the supernatant phases and the final equilibrium concentration of each CECs were estimated via using an Agilent 1290 high performance liquid chromatography system coupled to 6460 Triple Quadrupole mass spectrometer (HPLC-MS/MS). Separation of the adsorbates from the aqueous solutions was performed with an Agilent ZORBAX Eclipse Plus C18 (2.1 x 50 mm; 1.8 µm particle size) at 40 °C equipped with a guard column and in-line filter. Sample injection volume was 15 µL using gradient elution at a flow rate of 0.4 mL min-1. For the analysis under positive ionization mode, the mobile phase was composed of a mixture of water + 0.1% (v/v) formic acid (solvent A) and acetonitrile + 0.1% (v/v) formic acid (solvent B).

For the analysis under negative ionization

mode, the mobile phase was composed of water + 5 mM ammonium acetate (solvent A) and (90% acetonitrile + 10% water) + 5 mM ammonium acetate (solvent B). The MS/MS system was

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operated in electrospray ionization (ESI)-Jet Stream Technology using both positive and negative ion mode with multiple reaction monitoring (MRM) approach. Two precursor ion/product ion transitions (i.e. a quantifier ion and a qualifier ion) were monitored for each compound. The equilibrium-adsorbed amount of the CECs was calculated based on concentrations differences using the following equation:

q=

( C0 − C e ) V m

(1)

where q is the adsorbed amount (µg g-1), C0 the initial CEC concentration (µg L-1), Ce the concentration at equilibrium (µg L-1), V the liquid volume of the batch (L), and m the mass of the adsorbent (g). Finally, equilibrium adsorption isotherm data for each CEC were fitted with Freundlich isotherm model: 1

q = K F Ce

nF

(2)

where q is the adsorbed amount, KF is the interaction parameter, and nF is a qualitative constant related to the adsorbent material heterogeneity.

2-3 Fixed-Bed Adsorption Tests Fixed-beds were employed to perform dynamic adsorption experiments. Each experiment was performed via a vertical setup where the flow driving force was limited to gravity. This setup was accomplished by using a vertical glass column with an inner diameter of 0.8 cm and total depth of 30 cm. The experimental conditions were based on previous study performed by Hernandez-Maldonado and co-workers.34,

40

In general, the experiments were performed at

ambient conditions with an inlet concentration of 10 µg L-1 for each CEC. The average flow rate (Q) at the bed outlet was 1.5 mL min-1. The particle size of the SBA-15 adsorbent beds was fixed

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at 150 - 420 µm and a total mass of 2 g account for an effective bed depth (H) of 10.0 cm. Aqueous samples at the outlet of the column were gathered at regular time intervals and the concentration determined using the HPLC-MS/MS system described above. Adsorption loading amounts were estimated using an expression that resulted from a steady-state adsorbate mass balance as follows:

CQ q(t ) = i m

 C (t )   1−  Ci   0

∫

t

(3)

where q is the adsorbed amount (µg g-1), at any given time t (min), Ci is the bed inlet concentration (µg L-1), C(t) is the bed outlet concentration (µg L-1), at any time t, Q is the total volumetric flow rate (mL min-1), and m is the adsorbent bed weight (g). Loading amounts occurring at the bed breakthrough point (qb) were estimated using Eq. 3 and assuming that t = tb occurs when C(t)/Ci is ca. 0.05. Similarly, whenever possible, loading amounts occurring at the bed saturation point (qs) were estimated assuming that t = ts occurs when C(t)/Ci is ca. 0.95.

3. Results and Discussion 3-1 Equilibrium Adsorption Figure 1 shows the equilibrium adsorption data for each adsorbent-adsorbate combination in both single and multi-component fashion. It is evident that the adsorption capacities were significantly influenced by the modifications made to the adsorbents (i.e., amino-organic groups and copper cations). In general, it can be observed that low concentration (i.e., ppb), the adsorbents have the capability of interacting with the CECs and even maintaining adsorption trends similar to those observed at high concentration levels.35 Visibly, a better adsorption capacity and affinity toward the neutral and basic CECs was obtained for the unmodified

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adsorbent, while for the Cu-modified adsorbent a better affinity toward the anionic and acidic CECs was achieved. Although each material displays affinity toward specific families of CECs, it should be noted that CuNH2_g_SBA-15 exhibited the best overall adsorption working capacities. This suggests that the main adsorption mechanism present in these silica-based materials must be influenced by electrostatic interactions and hydrogen bonds. For instance, at the working pH, the surface of SBA-15 is anionic and acidic which favors the adsorption of CBZ and CFN molecules (see Figure S2 and Table 1). In contrast, CuNH2_g_SBA-15 contains aminoorganic groups and metal cations resulting in a cationic and basic surface which favors the adsorption of SA, ClofA and NPX molecules. For the case of NPX, the adsorption capacity in CuNH2_g_SBA-15 exceeds those observed in unmodified SBA-15 by almost 7-fold, suggesting that interactions of metal coordination complexes are also playing an important role in the process. For the multi-component adsorbate tests (Figures 1B and 1D), it was observed that the presence of multiple CECs in the water affected the individual interactions of each CECs with the adsorbent, thus revealing CECs competition for the adsorption sites. It should be noted that, although competition between adsorbates was observed, the selectivity toward specific families of contaminants was maintained. Moreover, these tests revealed that the adsorption process in unmodified SBA-15 was more competitive among the CECs. A decrease in overall adsorption capacity was observed as compared against the results obtained for single-component adsorption; approximately 60 and 30% for unmodified and modified SBA-15, respectively. One possible explanation for this behavior is that the modified material can still undergo significant interactions with the CECs through a combination of adsorption mechanisms in contrast with the material without modification. This increases the availability of adsorption sites, so the presence

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of several CECs in the mixture does not significantly compromise the adsorption capacity. This is further supported by the XPS studies, which will be discussed below. The data shown in Figure 1 were fitted to a Freundlich isotherm model (Eq. 2). The isotherm parameters as well as the corresponding fit Residual Root Mean Square Errors (RRMSE) (Eq. S4) are shown in Table 2. The Freundlich model was chosen based on previous studies since it most accurately describes the adsorption experimental data for these adsorbate/adsorbent systems.35 Additionally, it is important to note that the adsorption experimental profiles do not show saturation plateaus and the surface of these adsorbents are heterogeneous in nature. This is corroborated with the values obtained for both the isotherm interaction parameter and heterogeneity factors (see Table 2). Table 3 shows CECs equilibrium adsorption capacities for different adsorbent materials, including those studied here. The results contain data for both single and multi-component CEC adsorption. In general, the adsorption capacities obtained with the SBA-15 based adsorbents are comparable to or greater than those reported for other adsorbents at similar operating conditions. CuNH2_g_SBA-15, for instance, displayed adsorption capacities for ClofA and NPX that exceed those seen in the other materials, including activated carbon, suggesting that the Cu-based adsorbents could be an appropriate platform to develop strategies for the removal of acidic and anionic CECs at low concentration levels.

3-2 Single Component CEC Fixed-bed Adsorption Figures 2A and 2C show single component CEC concentration profiles as measured at the exit of both unmodified and Cu-modified SBA-15 fixed-beds. It can be seen that both anionic and acidic CECs break through the beds of unmodified SBA-15 beds faster than the neutral and

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basic ones. The opposite applies to the CuNH2_g_SBA-15 beds. These trends compare well with the reported based equilibrium adsorption tests (i.e., Figure 1). For the unmodified SBA-15 beds, a significant affinity towards CBZ is observed, whereas for the Cu-modified bed an affinity toward NPX is observed. However, affinity toward CECs that fall within a family of organic molecules can be better highlighted through dynamic tests. For example, an inspection of the adsorption equilibrium results gathered for CuNH2_g_SBA-15 shows that the NPX adsorption was not the most favored among the anionic and acidic contaminants (Figure 1C). However, at non-equilibrium conditions, the adsorption capacity at the breaking point for NPX was the largest (Figure 2C). This suggests stronger interactions between NPX and the material surface compared to SA and ClofA. From these three adsorbates, NPX has the largest electron density, more aromatic rings, and greater hydrophobicity (see Table 1). These could result in an increased attraction (electrostatic interactions) and a greater ability to form π-metal coordination complexes with the material. Despite this, the adsorption capacities are not the largest, probably due to steric effects (i.e., NPX is the largest molecule). When considering both breakthrough point and saturation adsorption capacities (Table 4), it is evident that the dynamic uptake capacities are smaller than the equilibrium ones. This is probably due to greater mass transfer resistance. Additionally, although the small pellet size used favors the adsorption capacity (due to an increase in external surface area), it also increases the film and inter-pellet diffusion resistance.47 Moreover, it should be mentioned that the pelletization process of these adsorbents slightly affected their textural properties (i.e., surface area and pore size), which in turn decreased adsorption capacity (see Figures S3 and S4, and Table S1).

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It should be mentioned that similar behavior and tendencies were observed for dynamic experiments performed at larger bed inlet concentrations (Figure S5). However, the volume of solution treated by the beds at these concentrations was significantly smaller. For the SBA-15 beds, the processed volume decreased between 60 - 88%, while for the CuNH2_g_SBA-15 it decreased between 28 – 60 % compared to experiments carried out at smaller inlet concentrations (Figure 2). This is due to a higher concentration gradient that results in an increase in the mass transfer coefficient.32, 36, 48 This causes more molecules to compete for the same available adsorption sites, thus causing faster bed saturation and an earlier breakthrough.

3-3 Multi-Component CEC Fixed-bed Adsorption Unmodified and Cu-modified SBA-15 beds were also tested with feeds containing mixtures of CECs (Figures 2B and 2D). Similar selectivity trends were observed when compared to the results obtained with single component feeds (Figures 2A and 2C). However, the adsorption capacities in these dynamic mixed-fed experiments were smaller than those estimated for single component CEC (Table 4). In addition, the multi-component breakthrough concentration profiles also revealed the presence of competition between these CECs for the adsorption sites, particularly in the case of unmodified SBA-15. Inspection of the bed outlet concentration profiles shows a decrease in CBZ adsorption capacity at the breakthrough point of ca. 27 %, whereas for the CuNH2_g_SBA-15 material a decrease of only 15 % in NPX adsorption capacity was observed. Surprisingly, an increase in adsorption capacity for SA and ClofA at the saturation point was found in CuNH2_g_SBA-15 (Figure 2C vs. Figure 2D). For SA, an increase of sevenfold in adsorption capacity was obtained, while for ClofA an increase of two-fold was achieved. This was also observed during dynamic tests performed with a mixture of CECs at larger bed

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inlet concentrations (Table S2). However, the increase was not as drastic (ca. 10 – 35 % of increase). These increases may be due to a synergistic effect between the CECs (intra-molecular hydrogen bonding) paired with the specific interactions contained in this modified material through the amino-groups and copper cations. Such behavior has already been documented in previous reports for adsorbents that also contain copper centers.30, 40 Another explanation for this behavior may be a higher resistance to mass transport, which is evident from the slopes of the mass transfer zone in the breakthrough concentration profiles (as compared to single compound experiments). For instance, the competitive adsorption between CEC molecules may have had an influence on the driving force to mass transfer, thus decreasing their diffusion rate.33,

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This

decrease in transport might be giving molecules more time to interact and bind, thus increasing the adsorption capacity at the saturation point. Dale and co-workers observed such behavior during arsenic breakthrough adsorption tests in the presence of competing ions onto nano-iron (hydr)oxide hybrid ion exchange resin beds. The presence of other ions created a hopping effect for the arsenate oxyanion over the material’s surface, affecting the intra-pellet surface diffusion mass transport.49 Similarly, Sotelo et al. conducted breakthrough adsorption tests for caffeine in the presence of diclofenac molecules onto granular activated carbon (GAC) beds. It was found that the competition of diclofenac molecules for the adsorption sites decreased the diffusion rate of caffeine molecules, which led to flatter, more asymmetrical breakthrough curves.33

3-4 Parameters for Fixed-Bed Adsorption To shed light onto the effectiveness of the SBA-15 based fixed-beds, the mass transfer zone (MTZ) and the fractional bed utilization (FBU) were estimated using Eqs. S2 and S3. MTZ has a maximum value corresponding to the bed height; a decrease in mass transfer resistance leads to

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the ideal condition where MTZ value equals zero, and the breakthrough curve is a step-function. In other words, the smaller the MTZ value, the greater the degree of utilization of a bed. Likewise, a larger FBU value is interpreted as a better usage of the adsorption capacity of the adsorbent. Particular attention was given to CBZ adsorption onto unmodified SBA-15 bed, and NPX onto the Cu-modified SBA-15 bed because both of these showcased the largest adsorption capacities at breakthrough. According to Table 4, MTZ values of 3.0 and 5.6 cm were obtained for both fixed-beds fed with CECs as single components. Whereas, for the beds fed with CECs mixtures, MTZ values of 4.9 and 6.3 cm were found.

These numbers evidence that the

unmodified SBA-15 bed has a greater degree of fractional bed utilization (i.e., 70 % for single component and 51 % for multi-component) compared to the CuNH2_g_SBA-15 bed (i.e., 44 % for single component and 37 % for multi-component). The unmodified SBA-15 bed also shows steeper breakthrough concentration profiles than the Cu-modified bed, which qualitatively matches well with these MTZ and FBU results. These results may suggest that a greater masstransfer resistance is taking place in CuNH2_g_SBA-15 beds. Since both unmodified and Cumodified SBA-15 beds were run under similar experimental conditions, this resistance could be due to intrinsic characteristics of the Cu-modified material, rather than the bed design used. For instance, CuNH2_g_SBA-15 exhibits textural properties (i.e., surface area, pore volume and pore size) that are perhaps less favorable (Table S1).

3-5 Modeling of Fixed-Bed Concentration Profiles for Single Component CEC Adsorption The concentration profiles shown in Figure 2 were each fitted with the Yoon-Nelson (Eq. 4), Clrk (Eq. 5), and the modified dose-response (MDR) (Eq. 8) models. These models have been

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extensively used to describe and predict breakthrough curves for adsorption systems involving aqueous phases.32, 34, 47, 50, 51 The Yoon-Nelson model is a relatively simple model based on the assumption that the rate of decrease in uptake of each adsorbate molecule is proportional to the probability of both adsorbate adsorption and breakthrough on the adsorbent. It can be represented by:

ln

C (t ) = kYN ( t − τ YN ) Ci − C

(4)

where kYN is a rate constant (min-1) and τYN is the time (min) required to achieve 50 % adsorbate breakthrough. The Clark model was developed to predict the concentration profiles for organic compound adsorption onto granular activated carbon systems. However, this model has been successfully employed to make predictions for a variety of adsorbent systems. This model assumes that the adsorption behavior of pollutants follows a Freundlich adsorption isotherm profile, and the adsorption rate is determined by an external mass transfer step. The equation that describes the Clark model is given by the following equation:

 C  i    C (t ) 

n−1

− 1 = AC e− rCt (5)

where AC and rC are the so called Clark constants, and n is the Freundlich’s isotherm heterogeneity parameter. For this work, the corresponding Freundlich’s heterogeneity parameters are displayed in Table 2. Clark constant are given by Eqs. 6 and 7, where qo is the adsorptive capacity normalized by bed volume, k is the adsorption rate coefficient, ν is the fluid superficial velocity, and z is the bed depth.

AC = e

15

 kqo z   ν 


(6)


rC = kCi

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(7)

The MDR model (also known as the Yan model) was originally developed for pharmacology studies and recently it has been used to describe adsorption of metals. Furthermore, it has been found that this model makes correct prediction of effluent concentrations at time zero (i.e., C(0) = 0). The basic form of the model is given by the equation:

C (t ) Ci

= 1−

1  Qt  1+   bMDR 

a MDR

(8)

where aMDR and bMDR are the model parameters. In this case, the bMDR is associated to the maximum adsorption capacity (q0) and it’s given by the following equation:

bMDR =

qo m Ci

(9)

The fitting of the data was done to predict the breakthrough points in these SBA-15 fixed-beds and for the particular CECs, and to estimate parameters that are direct descriptors of adsorption capacities and rate coefficients. Fits of these model data are shown in Figures 3 and 4. Meanwhile, Tables S3 – S5 present the data generated from the model fits as well as a direct comparison between predicted and observed adsorption capacities at bed saturation. Average relative error (ARE) (Eq. S5) and the modified Marquardt’s percent standard deviation (MPSD) (Eq. S6) were used to estimate deviations between the predicted and experimental data. In general, the Yoon-Nelson model appears to be more suitable to describe the concentration profiles in unmodified SBA-15 fixed-beds. In contrast, the Clark and MDR models, both gave a

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good representation of the concentration profiles CuNH2_g_SBA-15 beds with similar precision. However, the Clark model should be considered more appropriate model since it relies on both mass transfer coefficient and Freundlich isotherms to make predictions.

3-6 Adsorption of Parent/Metabolite CEC Mixtures Equilibrium adsorption tests were also performed to elucidate the performance of these SBA15 adsorbents. CBZ, CFN, and NPX were the parent compounds chosen for evaluation along with some of their corresponding metabolites (i.e., SA and ClofA are already metabolites). The evaluated metabolic byproducts were carbamazepine-10,11-epoxide (EP-CBZ), paraxanthine (PX), and O-desmethylnaproxen (O-DNPX). These were chosen based on the frequency of detection in the environment, toxicity levels, and quantification reliability (chemical stability) during mass spectroscopy analyses. Recent studies have found that, in addition to the parent compounds, these metabolites are very persistent throughout the water treatment process and bio-accumulate easily in aquatic organisms (e.g., phytoplankton, fish, shellfish, etc.) and plants.26,

52-54

Not only has it been shown that these compounds can be taken up by these

organisms, but also these organisms can metabolize the parent compounds and produce metabolites through enzymatic reactions, in the same way the human body does.54-57 This situation has created a larger concentration of metabolic byproducts compared to the parent compounds, which has created warnings about the health risks associated with the consumption of water and crops with high content of these CECs and metabolites.26, 58-63 For instance, studies by Malchi and coworkers found that CECs were taken up and metabolized within sweet potato and carrot crops irrigated with treated wastewater.55 Additionally, it was determined via the threshold of toxicological concern (TTC) approach,64 that by consuming just two carrots (for an

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adult) or half a carrot (for a child) per day, the daily exposure limit for metabolic compounds (such as EP-CBZ or lamotrigine) was reached. Figure 5 shows the adsorption data gathered for the CBZ/Ep-CBZ, NPX/O-DNPX, and CFN/PX pair CEC mixtures, respectively. Although the data are not shown here, it should be noted that the CBZ/Ep-CBZ resulted in no adsorption onto CuNH2_g_SBA-15, whereas the NPX/ O-DNPX resulted in no adsorption onto the unmodified SBA-15 material. In general, both adsorbents presented more adsorption capacity and selectivity for metabolites. An analysis of the data also reveals competition between both the parent and metabolic compounds for adsorption sites. When comparing the adsorption profiles of the parent compounds in these binary experiments with those obtained seen during the adsorption of single component parent compounds (Figure 5 vs. Figures 1A and 1C), it is noted that the adsorption capacity decreased by about 24, 55, and 30 % for CBZ, CFN and NPX, respectively. The superior selectivity toward metabolic CECs is probably due to the presence of additional functional groups (i.e., hydroxyl groups, epoxides, and secondary instead of tertiary amides). These additional moieties not only increase the probability for interaction with the adsorbent effective surface sites, but also allow the metabolites to adsorb strongly through more hydrogen bonds and/or coordination complexes. These findings, however, indicate that further studies are needed to fully understand the governing adsorption mechanisms under these treatment scenarios. 3-7 Possible Governing Adsorption Mechanism To shed light on the possible mechanisms that govern the adsorption of acidic CECs onto CuNH2_g_SBA-15, we performed XPS studies on fresh and spent samples. The adsorption tests in these studies were carried out at high concentrations of 10 mg L-1 (see Supplementary

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Material). Also, since the largest equilibrium adsorption loading uptakes in CuNH2_g_SBA-15 are observed for SA,35 it was used as a model CEC for this portion of the work. Figure 6 shows stacks of XPS spectra that focus on O, C, N and Cu regions. The spectral data presented here are the averages of several, representative sample points, and the data include error bars. It should be noted that XPS tests also confirmed the homogeneous incorporation of the moieties and the nonleaching of both the amino-organic groups as well as metal cations onto the external surface of SBA-15 after one SA adsorption cycle (spectral data not shown here). The XPS spectra of the O 1s were deconvoluted into three peaks with binding energy values located at 532.83, 531.76, and 530.85 eV. The first is attributed to the oxygen contained in the adsorbent framework (i.e., siloxanes, SiO2).65 The second peak is attributed to the oxygen at the terminal silanol groups (SiOH), while the third one is attributed to some of these terminals interacting with the metal cations through the hydroxyl groups (Cu-OH).66 These results suggest that the copper cations are interacting with the external surface of the adsorbent matrix. Upon adsorption of SA, the peak located at 531.76 eV shifts up by 0.18 eV, and this could be attributed to hydrogen bond interactions between the terminal silanol groups with the carboxylic and hydroxyl groups of SA. In this case, the free electron pair of O (in the terminal silanol) could be attracted by the strong positive charge of H contained in SA, thus decreasing the electronic density of O and increasing its binding energy. In contrast, for the peak at 530.85 eV, a significant shift of 1.16 eV to lower binding energy was observed. This decrease in binding energy is very likely due to the coordination of SA with copper cations, which at the same time is interacting with the terminal silanols. This metal coordination complex could be causing an increase in the electron density cloud toward the oxygen atom thus decreasing its binding energy. In the case of the characteristic peak of the oxygen contained in the matrix (SiO2), no drastic

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displacement was observed. However, upon adsorption of SA, the area under the curve for this peak increased. These changes could be simply attributed to the loading of the SA molecules onto the material; both the hydroxyl (-OH) and carboxyl ((C=O)OH) groups contained in SA overlay with the same binding energy environment (532-533 eV).66 The spectrum for C 1s shows three peaks at binding energy of 286.99, 284.82, and 283.0 eV. These are attributed to the environments C-NH2, C-C, and C-Si, respectively, and confirms the presence of the hydrocarbon chains onto the surface of SBA-15.67 Upon adsorption of SA, all these peaks shift down by 0.27, 0.10 and 0.77 eV. On the other hand, there was also an increase in the area under the peak curves at 286.72 eV and 284.72 eV; this can be attributed to the presence of the additional environments C=C (284-285 eV), C=O and C-OH (region 286-287 eV) contained in SA.66, 67 The N 1s spectrum shows two peaks at binding energies of 399.59 and 397.96 eV. The first is attributed to primary amines interacting with the copper cation (NH2-Cu).43,

66

The second is

attributed to free primary amines (R-NH2), which suggests that a small portion of the amines is not interacting with the copper cations.67, 68 However, after adsorption with SA, it was both peaks moved to higher binding energies by 0.20 and 0.22 eV. This suggests that both the NH2-Cu coordination complex and the small portion of previously free amines are now interacting with the SA molecules. In the case of the free amines, these could be interacting through hydrogen bonds, whereas the NH2-Cu complex could be interacting through a coordination bond with copper. In both cases, these interactions decrease the electron density of N, effectively increasing its binding energy. The Cu 2P3/2 spectrum shows two peaks located at binding energies of 935.65 and 933.0 eV, and their corresponding shakeup satellite peaks at 941.58 and 943.84 eV; these are

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representative of the oxidation state (+2) of copper.69, 70 Peaks 935.65 and 933.0 are attributed to copper interacting with hydroxyl groups (OH-Cu(II)) and with primary amines (NH2-Cu(II)), respectively.39, 71 This outcome confirms what was observed previously in the spectra gathered for N and O, as well as what was suggested in our previous work through compositional analysis of these materials.35 In both cases, the ability of copper to interact with both the SiO2 surface and the amine groups was confirmed. After the uptake of SA, these peaks were displaced toward higher energies, confirming the interactions of these complexes with the SA molecules. SA is plausibly donating electrons to the copper and, in turn, copper is back bonding electrons to SA to alleviate its excess negative charge. Therefore, the electron density in copper is reduced while the binding energy is increased. In general, the XPS tests confirm that both the adsorbent matrix and the incorporated functional groups (i.e., amine and copper cations) both participate in the adsorption of acidic CECs. Based on the above, it can be stated that the possible adsorption mechanisms are mainly controlled by electrostatic interactions, hydrogen bonds, and coordination bonds. This gives CuNH2_g_SBA-15 multidimensionality to interact with acidic CECs while increasing adsorption capacity and reducing the effect of competition by other contaminants.

3-8 Feasibility and Regeneration Potential of the Composite Adsorbent Other essential aspects when assessing the potential of these composite adsorbents, apart from adsorption performance, is the economic feasibility of producing and regenerating these materials for use in multiple cycles. For example, adsorbents produced from inexpensive raw materials, such as carbonaceous materials, tend to be very attractive for this type of application. Although its adsorption performance tends to exceed among many other engineered adsorbents,

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the energy required to produce high quality activated carbon and to thermally regenerate them is very expensive.72 On the contrary, transition metal-containing porous composite adsorbents do not require high energy requirements for their production, are highly stable, both in terms of structure stability and textural properties, and regeneration could be accessed with minimal energy consumption and simple engineering means. Figure S6 and Table S6 show evidence of structural and textural properties integrity of CuNH2_g_SBA-15 of materials upon adsorption tests. Meanwhile, Hernandez-Maldonado and co-workers have already reported that pH swings could provide one viable alternative for the regeneration of adsorbents containing functionalities similar to those employed here.45 For instance, at acidic conditions, no adsorption of NPX onto copper amino grafted SBA-15 material was observed. Therefore, this alternative probably promises to be a more economical and accessible option compared to a thermal one. However, more experiments should be conducted before final conclusions can be reached regarding the recovery and re-utilization of these composite adsorbents.

4. Conclusion SBA-15 adsorbent materials are capable of removing specific CECs at low and high concentration levels, and both under both equilibrium and non-equilibrium conditions. The adsorption capacities and selectivity toward CECs are also significantly enhanced when amino groups and copper cations are anchored onto the SBA-15 pore surface. Multi-component adsorption tests revealed a remarkable selectivity toward CBZ and NPX for SBA-15 and CuNH2_g_SBA-15, respectively. Although each adsorbent material exhibited affinities toward specific families of CECs, the Cu-modified material displayed the best overall adsorption capacity under equilibrium conditions. Adsorption of CEC mixtures (i.e., multi-component

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adsorption) reduced the capacity in both adsorbents, but CEC competition for adsorption sites was less prominent in CuNH2_g_SBA-15. This was attributed to the adsorbent ability to interact with the CECs through several, simultaneous adsorption mechanisms (i.e., electrostatic interaction, hydrogen bonds, and metal coordination complexes), which were confirmed via XPS analyses. Meanwhile, fixed bed adsorption tests revealed significant mass-transfer resistance in the case of CuNH2_g_SBA-15 compared to SBA-15. As a result, its adsorption capacity and its degree of bed utilization were reduced. Further studies will be needed to optimize particle and bed dimensions to find opportunities to deal with those limitations. Finally, equilibrium adsorption studies for mixtures of parent CECs and metabolites showed a greater adsorption capacity and remarkable selectivity toward the latter. To the best of our knowledge, equilibrium adsorption tests of CEC parent/metabolite binary mixtures have never been reported in literature, making these results ones of significant value to the understanding of the influence of the metabolites on overall CEC adsorption capacities and selectivity in porous materials.

Acknowledgements This work was supported by the National Science Foundation (NSF) under Award No. HRD1345156 (CREST Phase 2 Program) and Puerto Rico NASA Space Grant Graduate Fellowship Program. We also wish to acknowledge support from the Puerto Rico Institute for Functional Materials Program under the NSF Award No. EPS-1002410 for access to the XPS instrumentation.

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Supporting Information Additional details on: (1) SBA-15 synthesis and post-synthesis modification procedures, (2) characterization methods, (3) single-point equilibrium tests for elucidation of adsorption mechanism, (4) fractional uptake tests, (5) diffusion phenomenological model, (6) dynamic adsorption parameters, and (7) equations used for statistical analyses. The supporting information also includes: Figure S1 - fractional uptake data, Figure S2 - zeta potential data, Figure S3 - X-ray diffraction data for powder and pellet versions of the adsorbents, Figure S4 nitrogen adsorption data gathered at -196 ˚C, Figure S5 – additional fixed-bed concentration profiles, Figure S6 - X-ray diffraction data for fresh and spent adsorbents, Table S1 - structural and textural properties of adsorbents, Table S2 - equilibrium and dynamic adsorption capacities, and bed parameters, Tables S3-S5 – fixed-bed models fit data, and Table S6 - structural and textural properties of fresh and spent adsorbents. This material is available free of charge via the Internet at http://pubs.acs.org.

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List of Abbreviations Abbreviation ARE CBZ CEC CFN Ci ClofA CuNH2_g_SBA-15 EP-CBZ FBU GAC H MDR MOF MPSD MTZ NPX O-DNPX PX Q qb qe qs RRMSE SA SBA-15 XPS

Description Average relative error Carbamazepine Contaminants of emerging concern Caffeine Initial concentration Clofibric acid Copper amino grafted SBA-15 adsorbent Carbamazepine-10,11-epoxide Fractional bed utilization Granular activated carbon Bed depth Modified dose-response model Metal organic framework Modified Marquardt’s percent standard deviation Mass transfer zone Naproxen O-desmethylnaproxen Paraxanthine average flow rate Adsorbed amount at the bed breakthrough point Adsorbed amount at equilibrium Adsorbed amount at the bed saturation point Residual root mean square error Salicylic acid Mesoporous silica adsorbent X-ray photoelectron spectroscopy

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Table 1. Physicochemical properties for selected CECs. CECs

Therapeutic group

MW

pKaa

Log Kowa

CBZ

Antiepileptic

236.09

13.94

1.89

EP-CBZ

CBZ metabolite

252.27

NE

1.97

CFN

Nervous Stimulant

194.08

14.0/0.6

-0.07

PX

CFN metabolite

180.17

10.76

-0.39

ClofA

Lipid regulator Clofibrate metabolite

214.65

3.18

-0.99

SA

Analgesic and antiinflammatory Acetylsalicylic Acid metabolite

138.12

2.97

2.26

NPX

Analgesic and antiinflammatory

230.09

4.20

2.84

O-DNPX

NPX metabolite

216.24

4.34/9.78 3.9

a

Chemical Structure

Values for the pKa, Log Kow and molecular dimensions gathered from the literature 35, 73-75 NE = nonexistent at pH range 1–14.

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Table 2. Freundlich isotherm model parameters for CECs adsorption

Adsorbent

Freundlich

Equilibrium Test

CECs

Single-component SBA-15 Multi-component

Single-component CuNH2_g_SBA-15 Multi-component

nF

RRMSEa

CBZ CFN ClofA SA NPX

KF (µ µg L1/n g-1) 0.57 0.25 0.15 0.19 0.16

1.23 1.24 1.43 1.92 1.29

1.49 0.30 0.05 0.14 0.28

CBZ CFN ClofA SA NPX CBZ CFN ClofA SA NPX

0.04 0.04 nd nd nd 0.03 0.04 0.36 0.39 0.41

0.81 0.95 nd nd nd 1.21 2.64 0.91 1.01 1.06

0.30 0.30 nd nd nd 0.09 0.01 0.05 1.91 0.39


nd nd 0.44 0.76 0.14

nd nd 1.02 1.09 0.83

nd nd 0.05 2.02 1.39

1-1/n

nd = not determined a Eq S4

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Table 3. CECs equilibrium adsorption capacities (qe) for different adsorbent materials CEC

Adsorbent

SBA-15 MCM-41 HMS SBA-15 CBZ CuNH _g_SBA-15 2 Y-zeolite Mordenite zeolite SBA-15 Y-zeolite CFN Mordenite zeolite SBA-15 HMS M-HMS A-HMS ClofA PAC SBA-15 CuNH2_g_SBA-15 Kaolinite HMS M-HMS A-HMS PAC NPX SBA-15 CuNH2_g_SBA-15 Y-zeolite Mordenite zeolite CuNH2_g_SBA-15 Ci = initial concentration

Ci (ug L-1) 100.0 100.0 100.0 100.0 100.0 0.6 0.6 1.0 0.7 0.7 1.0 100 100 100 100 100 100 53 100 100 100 100 100 100 0.6 0.6 1.0

pH 7 7 7 6 6 6 6 7 7 7 7 6 6 3 7 7 7 7 6 6 6

40

qe (µg g-1) 30.9 29.9 27.6 18.5 1.1 0.7 2.6 0.2 0.3 0.8 0.1 12.8 10.7 34.5 23.3 3.6 34.3 0.1 14.6 37.4 12.8 41.9 6.1 27.6 0.2 4.6 1.1


Single or Multi Component Single Single Single Single Single Multiple Multiple Multiple Multiple Multiple Multiple Single Single Single Single Single Single Single Single Single Single Single Single Single Multiple Multiple Multiple

Reference 76

This work This work 77

This work 77

This work 37


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This work

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Table 4. CECs equilibrium and dynamic adsorption capacities, and bed parameters. Dynamic adsorption data gathered for Ci = 10 µg L-1, Q = 1.5 mL min-1, and H = 10.0 cm.

Adsorbent

Feed

CECs

qe (µ µg g-1)

Singlecomponent (Ci=10 µg L-1)

CBZ CFN ClofA SA

2.35 1.81 0.83 0.66

3.0 6.5 nd nd

NPX

0.74 0.72 0.63 nd nd

9.1

SBA-15 Multicomponent (Ci=10 µg L-1)

Singlecomponent (Ci=10 µg L-1) CuNH2_g_SBA-15 Multicomponent (Ci=10 µg L-1)


nd


0.10 0.10 3.50 2.69 2.63 nd nd 2.40 1.90


2.10

MTZ (cm)

0.70 0.35 nd nd 0.10

0.01

0.09 2.32 0.40 0.01 0.01 0.08

4.9 nd nd nd nd

0.51 nd nd nd nd

1.19 nd nd nd nd

nd nd nd nd 5.6

nd nd nd nd 0.44

nd nd nd nd 0.47

0.12 0.21 0.35 0.25 1.08

nd nd 8.7 nd 6.3

nd nd 0.13 nd 0.37

nd nd 0.09 nd 0.40

0.07 0.07 0.72 1.80 1.07

nd = not determined.

41

FBU

Dynamic qB qS -1 (µ µg g ) (µ µg g-1) 1.62 2.32 0.33 0.95 nd 0.01 nd 0.01



List of Figures Figure 1

CECs single component (A and C) and multi-component (B and D) equilibrium adsorption isotherms for SBA-15 and CuNH2_g_SBA-15 adsorbents. Data contain error bars. Lines represent Freundlich model fits. Data gathered at 25 ˚C and neutral pH.

Figure 2

Breakthrough of selected CECs in fixed-beds with SBA-15 or CuNH2_g_SBA-15, for water feed at 25 ̊C and neutral pH. CECs fed as (A and C) single components and (B and D) fed as a mixture. Data gathered for Ci = 10 µg L-1, Q = 1.5 mL min-1, and H = 10.0 cm. Solid lines are for visual representation of the data. Abscissa units correspond to water volume processed per mass of adsorbent bed.

Figure 3

Experimental and model breakthrough profiles for CECs fed as single components into a fixed-bed with SBA-15. Data gathered for Ci = 10 µg L-1 and Q = 1.5 mL min-1, and H = 10.0 cm. Abscissa units correspond to water volume processed per mass of adsorbent bed.

Figure 4

Experimental and model breakthrough profiles for CECs fed as single components into a fixed-bed with CuNH2_g_SBA-15. Data gathered for Ci = 10 µg L-1 and Q = 1.5 mL min-1, and H = 10.0 cm. Abscissa units correspond to water volume processed per mass of adsorbent bed.

Figure 5

Equilibrium adsorption isotherms of binary mixtures of CBZ/EP-CBZ in SBA-15 (A), NPX/O-DNPX in CuNH2_g_SBA-15 (B), CFN/PX in SBA-15 (C), and CFN/PX in CuNH2_g_SBA-15 (D). Data contain error bars. Lines represent Freundlich model fits. Data were gathered at 25 ˚C and neutral pH.

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Figure 6

XPS spectra of O1s, C1s, N1s, and Cu2p for CuNH2_g_SBA-15 before (A, C, E, and G) and after SA adsorption (B, D, F, and H) from a 10 mg L-1 solution. Fitted spectra and deconvoluted peak profiles are shown in continuous black and dotted lines, respectively.

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Figure 1

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Figure 2

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Figure 3

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Figure 6

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Table of Contents and Abstract Graphics

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Figure 1 279x237mm (200 x 200 DPI)



Figure 2 279x199mm (200 x 200 DPI)


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Figure 3 214x381mm (200 x 200 DPI)



Figure 4 254x790mm (200 x 200 DPI)


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Figure 5 279x237mm (200 x 200 DPI)



Figure 6 236x279mm (200 x 200 DPI)


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TOC Graphic 346x187mm (72 x 72 DPI)


Adsorption of Contaminants of Emerging Concern ... - ACS Publications

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