A Monte Carlo Simulation Study - ACS Publications - American

Article Cite This: Langmuir 2017, 33, 11146-11155

pubs.acs.org/Langmuir

Pharmaceutical Removal from Water Effluents by Adsorption on Activated Carbons: A Monte Carlo Simulation Study Daniel Bahamon†,‡ and Lourdes F. Vega*,†,§ †

Alya Technology & Innovation, C/Tres Creus, 236, Centre de Promoció Empresarial, 08203 Sabadell, Barcelona, Spain Departament de Ciència de Materials i Química Física & Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, C. Martí i Franquès 1, 08028 Barcelona, Spain § Gas Research Center and Chemical Engineering Department, Khalifa University of Science and Technology - The Petroleum Institute, P.O. Box 2533, Abu Dhabi, United Arab Emirates ‡

S Supporting Information *

ABSTRACT: Adsorption on activated carbons of five pharmaceutical molecules (ibuprofen, diclofenac, naproxen, paracetamol, and amoxicillin) in aqueous mixtures has been investigated by molecular simulations using the Grand Canonical Monte Carlo (GCMC) method. A virtual nanoporous carbon model based on polyaromatic units with defects and polar-oxygenated sites was used for this purpose. The simulation results show excellent agreement with available experimental data. The adsorption capacities of the carbons for the five drugs were quite different and were linked, essentially, to their molecular dimensions and atom affinities. The uptake behavior follows the trend PRM > DCF, NPX > IBP > AMX in all the studied structures. This work is a further step in order to describe macroscopic adsorption performance of activated carbons in drug removal applications.

■

INTRODUCTION Concern and awareness of potential problems related to water pollution due to emerging contaminants have been of growing interest for the past few decades.1 Many of these compounds are at concentrations of mg/L or below in aquatic effluents, and are hardly biodegradable even at the outlet of wastewater treatment plants.2 Pharmaceuticals represent an overgrowing fraction of these trace emerging contaminants, and the occurrence of several of these compounds have been reported in sewage treatment plant effluents;3−6 however, much on their potential impact on the environment and human health are still not completely known.7 Wastewater often contains a mixture of organic and inorganic compounds, as well as solids and soluble materials, and because of this diverse feature, no universal strategy of remediation is feasible. In fact, different processes are used for the treatment of wastewaters (e.g., ultrasonic irradiation, advanced oxidation, electrochemical degradation),8−10 but these technologies are either economically unfavorable or technically complicated, which make them difficult to be used in practice, or not completely effective to eliminate the majority of these compounds.11 Adsorption has been a prominent method of treating aqueous effluents in industrial processes for a variety of separation and purification purposes.12−14 It has been cited by the US EPA as one of the best available control technologies15 because of its convenience, mainly due to the availability of high surface area © 2017 American Chemical Society

and the combination of well-developed pore structure and surface functional group properties.16 Experimental studies have reported the removal of pharmaceuticals from aqueous solutions by using activated carbons.11,12,16−23 Most of these works have been usually performed from an analytical chemistry point of view, providing a detailed textural or surface chemistry characterization of the activated carbons, as well as the adsorption isotherms. However, in most cases, the evaluation is not performed by screening adsorbent textural structures but by comparing different processes to obtain an activated carbon with random properties. Exceptions are the series of papers recently published by Bandosz and collaborators,20−23 where they showed that, depending on the chemical affinity between a pharmaceutical molecule and the carbon surface, important transformations of the adsorbed species referred to as reactive adsorption take place.20 This is mainly owing to the oxygen groups incorporated to the carbon matrix. These species, besides attracting polar molecules, also react with functional groups of the pollutants, especially with amines, resulting in very strong Special Issue: Tribute to Keith Gubbins, Pioneer in the Theory of Liquids Received: June 10, 2017 Revised: July 31, 2017 Published: August 1, 2017 11146

DOI: 10.1021/acs.langmuir.7b01967 Langmuir 2017, 33, 11146−11155

Article

Langmuir

consumed medicines all over the world. It is an analgesic and antipyretic (to reduce fever) drug widely used in the treatment of rheumatic disorders, pain, and fever. Diclofenac (2-(2,6-dichloranilino) phenylacetic acid) is a NSAID taken to reduce inflammation and as an analgesic reducing pain in certain conditions. It is often used to treat chronic pain associated with cancer, in particular, if inflammation is also present. Similarly, naproxen (6-metoxi-methyl-2-naphtalene acetic acid) is a pharmaceutical widely used in the treatment of rheumatoid arthritis, spondylitis, and osteoarthritis. Two other typically used drug molecules were analyzed: paracetamol (PRM), and amoxicillin (AMX). Paracetamol, also known as acetaminophen, is a non-NSAID antipyretic agent used for the relief of fever, headaches, and other minor aches and pains. It is typically used for mild to moderate pain and has relatively little anti-inflammatory activity, unlike other common analgesics such as ibuprofen (although it has similar effects in the treatment of headache).34 The fifth and final molecule chosen is amoxicillin: a β-Lactam antibiotic, with properties to treat bacterial infections. It may also be used for strep throat, pneumonia, skin infections and urinary tract infections, among others.35 The molecular structures of these pharmaceuticals can be found in Figure 1, while the chemical formula and some physicochemical properties of each adsorbate are depicted in Table 1.

adsorption forces/covalent bonds. Moreover, the ability of a carbon surface to activate oxygen results in the partial oxidation of the adsorbed species. In a later publication23 they investigated the competing adsorption behavior of multicomponent pharmaceutical aqueous mixtures in activated carbons, focusing on the adsorption mechanism. Overall, the competitive adsorption causes a decrease in the amount adsorbed of all pharmaceuticals on all adsorbents, and one adsorbent’s performance did not stand out above the others. In addition to the experimental studies, a thorough modeling analysis accounting for the effect of the main thermodynamic parameters would represent an invaluable tool for the design and optimization of an adsorption system devoted to this application. In this regard, molecular simulations, especially the Grand Canonical Monte Carlo (GCMC) technique is a well-suited tool to predict the performance of different types of molecules on adsorbent materials24 and to complement the experimental work. By fixing the chemical potential, volume and temperature of the system, GCMC directly provides the number of molecules adsorbed as a function of the pressure at fixed temperatures, i.e., the adsorption isotherms. Moreover, the method also allows identifying preferential adsorption sites and special behaviors such as coadsorption or competition for preferential sites. Although there are several molecular simulation studies of water adsorption in carbon pores,25−27 computational studies on the removal of pharmaceuticals often report adsorption capacities from a single component and not in solution.28 To the best of our knowledge, only a few computational studies are devoted to drug removal from aqueous solutions on activated carbons or similar materials.29,30 Simulations of large guest molecules such as drugs dissolved in water into porous materials present some difficulties such as the fitting of the molecules into the pores, and the inclusion of water in the system with the corresponding interactions. This work belongs to a long-term project on understanding the adsorption mechanism of micropollutants in order to design the best procedure for their removal from aqueous solutions30,31 The fundamental understanding is obtained by using molecular simulations of realistic materials and pharmaceuticals and comparing the microscopic behavior of the system to the macroscopic, experimental properties. Thus, in the present work we have evaluated, by molecular simulations, the adsorption of selected highly consumed pharmaceutical compounds as pollutants contented in water on activated carbons of varied characteristics, in order to compare their removal capacities. As probe molecules, ibuprofen, paracetamol, naproxen, amoxicillin, and diclofenac have been selected because they are widely used and ubiquitously detected in aqueous environments.3,11,32 The first four drug molecules have similar dimensions but different chemical groups and hence different properties, which make them interesting in order to perform an affinity screening, while the amoxicillin molecule was chosen for a comparative size behavior. We have used “realistic” nanoporous carbon models from our previous work,30 based on units of polyaromatic molecules with a different number of rings, defects, and polaroxygenated sites.

■

Figure 1. Molecular structures of pharmaceutical molecules studied: (a) ibuprofen, (b) diclofenac, (c) naproxen, (d) paracetamol, and (e) amoxicillin. In addition, the atom charges for the drug molecules, as obtained from the charge equilibration method (Qeq) are provided in Figure S1 in the Supporting Information. All molecules have similar dimensions, ranging from 10 to 14 Å in length, 6−7 Å in height, and around 4 Å in width. The exception is the amoxicillin molecule, with around 1.3 times longer chain. Activated Carbon Model. The most concise definition of an activated carbon (AC), which includes a wide range of amorphous-based materials, is a material prepared to exhibit a high developed porosity, variable characteristics of surface chemistry, and a large surface area.36 Experimental techniques have shown that the structure of ACs is disordered and isotropic, and due to its complexity, the detailed atomic structure is still poorly understood.37,38 The first efforts on molecular models of carbons involved the use of the slit-pore model.39 Due to its simplicity and easy implementation, this model has been widely used in the literature; however, the model is unable to capture all the great structural and chemical complexity of ACs. In this regard, the use of well-defined building blocks or periodic platelets structures as part of the model material has been gaining relevance.40−42 Complementary approaches include realistic models of ACs such as Reverse Monte Carlo and Hybrid Reverse Monte Carlo simulation techniques, which reconstruct realistic disordered porous carbon structures by fitting experimental diffraction data of real materials.43−46 Works with respect to the adsorption dependence of the platelets dimensions, packing schemes, defects in the structure, and location of oxygenated groups have been previously reported in the literature.41,47 In all cases, results show very small differences between the different

METHODOLOGY/MATERIALS

Pharmaceutical Molecules. Nonsteroidal anti-inflammatory acid drugs (NSAIDs) comprise one of the major classes of pharmaceuticals commonly consumed in both prescription and nonprescription drugs.33 Three of these NSAIDs were included in this study: ibuprofen (IBP), diclofenac (DCF), and naproxen (NPX). Ibuprofen is one of the most 11147


Article

Langmuir Table 1. Properties of the Molecules Investigated in This Work molecular formulas CAS number molecular weight pKa approx. dim. (length, height, width), Å

ibuprofen (IBP)

diclofenac (DCF)

naproxen (NPX)

paracetamol (PRM)

amoxicillin (AMX)

C13H18O2 15687-27-1 206.28 4.91 10.5 × 6 × 4

C14H11Cl2NO2 15307-86-5 296.14 4.15 11 × 8 × 5

C14H14O3 22204-53-1 230.26 4.15 12 × 6.5 × 4.5

C8H9NO2 103-90-2 151.16 9.50 9.5 × 6 × 3

C16H19N3O5S 26787-78-0 365.4 3.23 13.5 × 6.5 × 8

Figure 2. Structural units/platelets used to recreate the activated carbon models. O atoms in red and C atoms in gray (H atoms omitted). comparisons performed, demonstrating that the most important features are the characteristic parameters of the activated carbon (pore volume, surface area) and the oxidation grade (O/C or %O). In this study, the modeling approach we used is an extension of the model packing idealized three-dimensional platelets structures first developed by Segarra and Glandt.42 We extended the model by considering a disordered array of functionalized curved sheets, and also including polar groups and defects. The platelets were packed in random positions and orientations in a periodic simulation cell, with a carbon density chosen similar to the experimental materials and preventing overlapping of polyaromatic layers in the final structure.48,49 The model is sufficiently flexible to tune parameters in a systematic way, and allows many essential features of ACs, such as high surface area, disorder and heterogeneity of the structure. Details can be found in our previous work.30 A typical AC obtained with this technique is depicted in Figure 2. To check the influence of carbon surface oxidation, we have randomly introduced oxygenated groups into the initial structure. A variety of chemical groups such as carbonyl, carboxyl, and hydroxyl were included on the platelets surfaces. Nevertheless, it should be stated that several studies suggest that, on average, the interaction appears to be roughly independent of the type of active surface group in which the oxygen is incorporated.50,51 The obtained materials were subsequently characterized for further evaluation of the adsorption of several pharmaceuticals, for both pure and in solution conditions, by using GCMC simulations. Simulation Details. The commercial software Materials Studio52 was used for the adsorption simulations. Calculation of the average number of adsorbate molecules fluctuating during the simulation, for a range of chemical potentialspressureand at fixed volume and temperature, allows the construction of the adsorption isotherm. All probed movements (insertion, deletion, and translation/rotation) were taken with equal probability.

The molecular simulations were performed with the Configurational Bias Monte Carlo (CBMC) approach.53 With this technique, before translations and rotation of adsorbed molecules, CBMC allows growing the molecule inside the framework atom by atom searching for the best configuration so that the energetically unfavorable overlaps are avoided and correcting for the bias. One hundred growth step cycles were attempted for each replica state by creation and/or rotation of a randomly chosen molecule with equal probabilities, before one configuration swap and/or displacement were performed. For each system, 106 initialization Monte Carlo cycles were run for phase equilibration, and an additional 106 were run for data collection of equilibrium properties. Models and force fields used were taken from the literature, allowing transferability for the entire group of different molecules and structures. The carbon platelets were simulated rigid, with each carbon atom of the ACs modeled as a Lennard-Jones (L-J) site. Water molecules were modeled with the TIP4P/200554 force field, while drug molecules were constructed and modeled as flexible with charges obtained from the charge equilibration method (Qeq).55 L-J parameters and charges for the polar surface sites for hydroxyl, carboxyl, and carbonyl groups were adopted from the OPLS-AA potential model.56 For structural parameters validation with nitrogen, values were taken from the TraPPE force field.57 Intermolecular potential parameters can be found in their respective works. The transferability of the chosen force fields for the current work is validated in the next section by comparison with available experimental data. Lorentz−Berthelot combining rules were used to compute interactions between unlike species, electrostatic interactions were calculated by the Ewald summation method, and liquid phase fugacities and activity coefficients were used to relate chemical potential with vapor pressures. It should be mentioned that, experimentally, the anionic form of the drug molecules is dominant in solution when 11148


Article

Langmuir Table 2. Specification of Platelet Structure Content in Each AC Model %O surface area (m2/g) pore volume (cm3/g) mean pore diameter (nm)

AC-1

AC-2

AC-3

AC-4

AC-5

AC-6

AC-7

AC-8

AC-9

AC-10

-1060.8 0.44 0.89

2.9% 978.5 0.46 0.81

10.1% 969.9 0.49 0.78

9.7% 2158.9 0.81 1.23

3.2% 2082.4 0.78 1.17

-2109.1 0.77 1.29

9.0% 1533.9 0.63 0.88

3.3% 1455.6 0.62 0.94

-475.1 0.31 0.66

3.2% 550.3 0.38 0.73

pH > pKa. Hence, molecules evaluated were compared with experimental data at acidic pH, where they are mainly nondissociated and preferentially adsorbed in a neutral form.58

attributed to smaller pore volume created by the decrease in the specific surface areas after oxidation and by created carbon defects.64 The void space filled by the functional groups included in the surface leads to a more pronounced development of porosity, related with the narrowing of the microporous size distribution (see AC-3, AC-4, and AC-7). Nevertheless, although applying virtual oxidation practically does not change the geometric PSD of carbons, it does modify the mean pore diameter. As surface areas and/or pore volumes increase, the mean pore diameter of the AC is shifted to higher values. Results also show a minimum pore diameter of 0.66 nm for AC-9. Considering the molecular dimensions of the evaluated drug molecules (shown in Table 1), even this small pore diameter allows the adsorbates to access the microporosity region of all solids. Adsorption Calculations. Figure 4 shows the pure adsorption uptake of the studied drug molecules on the AC representations at room conditions (300 K and 100 kPa). The relationship between surface area and free volume space demonstrate that, as expected, higher parameter values allow higher adsorption uptakes. No specific site preference was seen for pharmaceuticals: molecules were either placed on the curved surface of the platelet or at oxygenated-sites. Results indicate that there is a difference in the adsorption affinity toward the five drugs tested. PRM, NPX, and DCF show the stronger carbon−drug interactions, and, for instance, the maximum adsorption capacity of IBP was approximately 2 times lower in mass than that for DCF. Specific points related to the ACs with higher oxygen content (i.e., AC-3, AC-4, and AC-7) are shown as filled points, in order to determine the behavior of the pharmaceuticals upon surface functionalization. It is noted that the adsorption capacities of DCF and AMX are reduced, which can be explained for the slight increase in the population of narrow micropores, as mentioned before, reducing the accessibility to smaller pores in the activated carbons. Similar results were experimentally obtained by Nielsen and Bandosz22 for the removal of sulfamethoxazole and trimethoprim from water using fish waste-derived adsorbents. As previously mentioned, several authors have performed simulations for pure water in different models of ACs,25,40,41,51 including one study using the same force field employed in this study for polar groups.26 Water adsorption at saturation mostly depends on the surface area/pore volume of the structure. For a detailed discussion on water behavior with different percentages of oxygen, the reader is referred to our previous work.30 Adsorption isotherms of pure water in the different structures studied in this work are presented in Figure S5 in the Supporting Information for completeness. As it can be observed in the snapshots, water molecules prefer to adsorb close to the polar groups of the activated carbon at lower pressures. Since aqueous simulations of this work were performed at ambient conditions (i.e., 100 kPa and 300 K), saturation has been achieved and water molecules fill the void spaces of the crystal. The relevance of this behavior for the drug solutions will be discussed in the next sections.

■

RESULTS AND DISCUSSION Textural Characteristics of ACs. The textural characteristics of the ACs simulated in this work are summarized in Table 2 (see also Figure S2 in the Supporting Information). More detailed information regarding oxygen-groups compositions in each one of the models can be seen in Table S1 in the Supporting Information. Ten different structures were simulated (named AC-1 to AC-10). The elemental compositions of oxygen-to-carbon ratio in the systems, as well as the calculated BET surface areas and pore volumes, were taken to agree within the ranges reported in the literature.16,25,59−62 Moreover, additional modifications in parameters were evaluated in some of the structures as a screening method to tune and identify key variables for removal capacities in these ACs. The pore size distribution (PSD) for each structure, shown in Figure 3, was obtained from the nitrogen simulated isotherms at 77 K (see Figure S3 in the Supporting Information) and

Figure 3. PSD obtained for the 10 ACs simulated. Normalized curves are shifted 1 unit according to the surface area: SBET ∼ 500 m2/g (bottom), SBET ∼ 1000−1500 m2/g (middle), and SBET ∼ 2000 m2/g (up).

non-local density functional theory (NLDFT) techniques.63 RDFs between carbon atoms in the evaluated ACs can also be found in the Supporting Information (Figure S4). It can be seen that porosity is mainly composed of micropores, with a significant fraction of the pores smaller than 2 nm: PSD peaks of our model carbons are shifted to smaller pore sizes and slightly capture mesoporous regions, often present in disordered carbons. For instance, the PSD for AC-6, with one of the highest surface areas, also has a broad distribution with a mean pore size of nearly 1.3 nm. The distribution for AC-4 is comparatively narrower and a small fraction of pores are in the mesoporous range, and can be 11149


Article

Langmuir

Figure 4. Relationship between predicted adsorption uptake (in mmol of the adsorbate per gram of adsorbent), pore volumes, and surface areas for selected drug molecules in pure state: circles for IBP, squares for DCF, asterisks for NPX, triangles for PRM and diamonds for AMX (Filled points correspond to ACs with higher oxygen content); on the right side, two typical void spaces recreated according to the surface area: 2000 m2/g (up) and 500 m2/g (bottom) approx.

Pharmaceutical Adsorption in Aqueous Solutions. The adsorption isotherms of each drug molecule on ACs were studied on aqueous solution and are shown in Figure 5. Only 6 of the 10 structures are shown for the sake of clarity. For more detailed information on all isotherms, the reader is referred to Figure S6 in the Supporting Information. As mentioned before, higher surface areas and pore volumes allow higher uptakes. Results shown in Figure 5 confirm that activated carbons are highly selective for these pharmaceutical molecules: selectivities obtained for drug molecules over water for 100 ppm of pharmaceutical concentration are on the order of 3000 to 7000. In addition, the saturation uptakes are similar to those presented in Figure 4, indicating no strong competition of the solvent for the active sites.35 Figure 5 shows a sudden rise in the amount adsorbed at low concentrations for all related-structures, values that approach a plateau ascribed to the formation of a complete monolayer. The patterns and adsorption capacities are in agreement with the experimental data found in the literature.59,60,65,66 However, the experimental values are so few that trends or isotherm shapes are very difficult to identify, although it seems that the shape of the isotherms is not correctly predicted by the simulations in some cases; further systematic experimental studies are needed to overcome these gaps. In spite of it, the predicted trends obtained by GCMC are valid at least qualitatively in terms of separation by sizes of different molecules with similar chemical groups. IBP, NPX, and DCF show similar adsorption trends, with higher or lower values in each of the structures depending on their interaction with the polar groups. The two other drugs behave very differently. According to Moreno-Castilla et al.,67 the characteristics that mainly influence the adsorption process are the molecular size of the molecule, the solubility in water, pKa, and the nature of the substituent in the benzene ring. From our results, a direct correlation appears between the adsorption capacity and molecule dimensions. The maximum adsorption capacities values follow the sequence: AMX < IBP < NPX < DCF < PRM. Note that in mg/g, the values vary because of differences in the molecular weight of the pharmaceuticals (e.g., uptake in AC-3:130 mg/g for IBP < 188 mg/g for DCF < 241 mg/g for NPX). By comparing the pKa values (see Table 1), it was expected that PRM and IBP would be the most adsorbed molecules because they have the lowest electrostatic repulsive interaction

between the deprotonated form and the negative charge of the adsorbent. Therefore, it is possible to ascribe the low IBP uptake to the chemical structure of its molecule, which may favor aggregation between them through hydrophobic interactions and make them difficult to adsorb. Moreover, the chlorine group is an electron-withdrawing group. Diclofenac presents two chlorine groups, thus, it may present the highest affinity to the p-electrons in activated carbons. Nevertheless, it should be noted that its adsorption capacities are reduced upon surface functionalization, due to the slight increase in the population of narrow micropores that blocks the entrance to these higher dimension molecules. A snapshot of the five pharmaceutical molecules adsorbed in one of these structures in aqueous solution is presented in Figure S6b in the Supporting Information. A systematic study involving a larger list of conditions will be necessary for a thorough investigation about the correlation between adsorbate characteristics and the extent of adsorption on activated carbon. Parametrization. The adsorption data onto activated carbon for the drugs considered in this work adjusts to Langmuir and Freundlich models. The linearized fittings are provided in Figure S7 of the Supporting Information. In general, best fitting values were obtained with the Langmuir equation in terms of R2 and χ2. The corresponding parameter values are shown in Table 3. The Langmuir constant (b) is a measure of the adsorption affinity or heterogeneity of the surface.68 Its value increases as the oxygen content increases in the AC (see values for AC-1, AC-2, and AC-3). In addition, AC structures with similar O/C ratio also show similar b constant values; this is the case for AC-2, AC-5, and AC-10. Taking into account the above-mentioned factors, a linear regression analysis was performed to determine the influence of these two characteristics: functionalization grade (%O) and void space (surface area), in the amount of adsorbed drugs.69 Results showed that the highest statistical relation exists between surface area on qsat parameter and oxygen-content with b constant. The effect is described in eq 1: qsat , b = f (SBET , %O) q= 11150

qsatb·Ce 1 + b·Ce

=

(1)

(α1SBET)(α2[%O] + α3) ·Ce 1 + (α2[%O] + α3) ·Ce DOI: 10.1021/acs.langmuir.7b01967 Langmuir 2017, 33, 11146−11155

Article

Langmuir

Figure 5. Adsorption uptake in aqueous mixture of (a) IBP, (b) NPX, (c) DCF, (d) PRM, and (e) AMX, according to the concentration in the aqueous solution (AC-1 in circles, AC-2 in squares, AC-3 in triangles, AC-4 in crosses, AC-5 in diamonds, and AC-10 in asterisks). Closed symbols represent experimental data: circles from Mansouri et al.,60 diamonds from Baccar et al.,65 and squares from Galhetas et al.66

obtained values for α2 are 1.0167, 1.3778, 0.5968, 0.4735 and 0.8801%O·L/mg. A good correlation is obtained for all molecules (see Figure S8 in the Supporting Information).

where the αi are correlation parameters. Values obtained for α1 in IBP, DCF, NPX, PRM, and AMX are 0.1287, 0.1968, 0.1964, 0.1641, and 0.1082 mg/m2, respectively. Similarly, 11151


Article

Langmuir

Table 3. Fitting Parameters of the Equilibrium Isotherms to the Langmuir and Freundlich Models, Linear Regression Coefficient of Determination, R2, and Nonlinear χ2 Test Analysis66 LANGMUIR EQUATIONa IBP AC-1 AC-2 AC-3 AC-4 AC-5 AC-10 DCF AC-1 AC-2 AC-3 AC-4 AC-5 AC-10 NPX AC-1 AC-2 AC-3 AC-4 AC-5 AC-10 PRM AC-1 AC-2 AC-3 AC-4 AC-5 AC-10 AMX AC-1 AC-2 AC-3 AC-4 AC-5 AC-10 a

Langmuir: q =

FREUNDLICH EQUATIONa χ

1/n

R2

χ2

49.35 70.65 52.16 113.38 111.31 33.35

4.3605 5.1292 4.7759 5.5546 5.0702 5.6622

0.8604 0.8988 0.8437 0.8262 0.9323 0.9385

6.89 3.86 5.98 8.57 4.75 1.24

1.64 3.47 3.20 3.14 2.69 2.50

85.49 88.23 82.37 186.06 178.91 39.58

4.2955 4.6579 4.8922 6.1294 5.1599 5.6348

0.8677 0.7894 0.8212 0.8070 0.8671 0.8405

11.99 15.43 9.82 13.56 12.47 2.78

0.9987 0.9998 0.9997 0.9997 0.9999 0.9988

0.81 0.18 2.29 2.12 3.39 1.51

73.34 79.69 77.65 148.71 143.72 42.12

3.6323 3.7621 3.6019 5.1060 5.0215 4.2192

0.9158 0.9267 0.9136 0.9543 0.9370 0.8811

10.78 8.46 12.16 4.20 5.41 5.52

0.1243 0.1330 0.1807 0.1530 0.1307 0.1307

0.9996 0.9991 0.9991 0.9997 0.9992 0.9992

1.80 2.47 3.69 3.86 1.49 0.68

49.92 63.46 54.76 109.70 96.98 25.74

3.4942 3.9318 4.2635 4.0541 3.9385 4.0850

0.9324 0.8794 0.9057 0.8880 0.9194 0.9533

6.59 11.33 5.89 14.44 9.89 1.44

0.2445 0.3747 0.3885 0.3885 0.3947 0.3747

0.9999 0.9999 0.9998 0.9999 0.9997 0.9992

2.91 0.09 0.08 2.19 1.25 0.17

75.94 54.87 63.41 118.58 102.72 12.12

6.0185 5.9438 6.0292 6.4862 6.3996 5.4703

0.8657 0.8820 0.8632 0.7642 0.7909 0.9169

4.44 2.97 3.75 9.92 8.03 0.51

2

2

qsat (mg/g)

b (L/mg)

R

140.56 170.95 132.06 249.35 273.87 73.81

0.1693 0.2045 0.2819 0.2894 0.2069 0.2314

0.9992 0.9997 0.9996 0.9998 0.9995 0.9998

0.96 0.52 3.98 0.98 1.72 1.01

253.29 235.39 210.16 372.41 416.93 91.70

0.1289 0.1769 0.2285 0.3254 0.2182 0.1769

0.9994 0.9991 0.9997 0.9999 0.9999 0.9998

274.00 277.12 274.43 361.01 356.36 128.56

0.0967 0.1149 0.1271 0.2082 0.1782 0.1149

185.92 210.94 163.03 346.37 321.13 79.46 160.72 112.54 128.53 227.07 203.07 26.27 qsat b . Ce 1 + b . Ce

KF (mg

·L /g)

1−1/n

1/n

Freundlich: q = KFC1/n e

Table 4. Adsorption Uptakes (in mg/g) and Selectivities for IBP-over-Drug(2), in Different AC Structuresa mixture/material

AC-1

AC-2

AC-3

AC-4

AC-5

AC-10

80ppmIBP/20ppmAMX

112/40 [0.7] 41/124 [1.3] 104/32 [0.8] 36/118 [1.2]

140/38 [0.9] 54/130 [1.7] 128/34 [0.9] 32/126 [1.0]

107/33 [0.8] 45/124 [1.5] 115/31 [0.9] 47/149 [1.3]

206/62 [0.8] 56/218 [1.0] 212/66 [0.8] 58/246 [0.9]

238/83 [0.7] 71/202 [1.4] 218/69 [0.8] 55/226 [1.0]

59/18 [0.8] 13/47 [1.1] 66/20 [0.8] 17/80 [0.9]

20ppmIBP/80ppmAMX 80ppmIBP/20ppmDCF 20ppmIBP/80ppmDCF a

Values at a total pressure of 100 kPa and 300 K. Selectivity = (qIBP/cIBP)/(qdrug#2/cdrug#2), in brackets.

Note that α3 values can be determined for a 0% oxygen-content in the b constant parametrizations. According to this result, IBP and AMX uptakes are less prone to change with surface area in these evaluated ACs, due to lower α1 values. Moreover, IBP and DCF show higher affinity for AC oxygen content, determined by a higher slope in the b constant versus %O plot (i.e., α2 values).

Selective Removal of Pharmaceuticals in Aqueous Mixtures. Selectivity provides a good indication on how efficient the separation in a multicomponent mixture can be. Since even at low concentrations, the activated carbons are capable of removing mixture traces of drugs that can be present in wastewater after the primary and secondary treatments, we have simulated two ternary mixtures (i.e., two drug molecules 11152


Langmuir plus water): IBP+AMX and IBP+DCF. The conditions evaluated correspond to a mixture containing 20 ppm IBP/80 ppm of the second component, and vice versa. The main results are presented in Table 4. Uptakes decreased for all carbons compared to their corresponding values from single-component solutions data due to the competitive adsorption of both components in the mixture.23 This descend is larger for AMX than for IBP and DCF, indicating that main competitive effects are associated with the molecule dimensions and the affinity of the individual compounds for the carbon adsorbent.59 In general, the mass composition of the adsorbed phase in the different materials is in the range of 20%IBP/10%drug#2/70% H2O for the 80ppmIBP mixtures, and vice versa for the other ternary mixture, with slightly higher proportional IBP uptake in ACs with less oxygen content. In addition, according to the selectivities obtained, no higher affinity for one drug molecule with respect to the others was found in this study.

ACKNOWLEDGMENTS

■

REFERENCES

(1) Smith, S. W. Chiral Toxicology: It’s the Same Thing···Only Different. Toxicol. Sci. 2009, 110, 4−30. (2) Miège, C.; Choubert, J. M.; Ribeiro, L.; Eusèbe, M.; Coquery, M. Fate of pharmaceuticals and personal care products in wastewater treatment plants-conception of a database and first results. Environ. Pollut. 2009, 157, 1721−1726. (3) Ternes, T. A.; Meisenheimer, M.; Mcdowell, D.; Sacher, F.; Brauch, H.-J.; Haist-Gulde, B.; Preuss, G.; Wilme, U.; Zulei-Seibert, N. Removal of Pharmaceuticals during Drinking Water Treatment. Environ. Sci. Technol. 2002, 36, 3855−3863. (4) Miao, X. S.; Bishay, F.; Chen, M.; Metcalfe, C. D. Occurrence of Antimicrobials in the Final Effluents of Wastewater Treatment Plants in Canada. Environ. Sci. Technol. 2004, 38 (13), 3533−3541. (5) Kolpin, D. W.; Furlong, E. T.; Meyer, M. T.; Thurman, E. M.; Zaugg, S. D.; Barber, L. B.; Buxton, H. T. Pharmaceuticals, Hormones, and Other Organic Wastewater Contaminants in U.S. Streams, 1999− 2000: A National Reconnaissance. Environ. Sci. Technol. 2002, 36 (6), 1202−1211. (6) Soulet, B.; Tauxe, A.; Tarradellas, J. Analysis of Acidic Drugs in Swiss Wastewaters. Int. J. Environ. Anal. Chem. 2002, 82 (10), 659−667. (7) Nuzhdin, A. L.; Dybtsev, D. N.; Bryliakov, K. P.; Talsi, E. P.; Fedin, V. P. Enantioselective Chromatographic Resolution and One-Pot Synthesis of Enantiomerically Pure Sulfoxides over a Homochiral Zn− Organic Framework. J. Am. Chem. Soc. 2007, 129, 12958−12959. (8) Mendez-Arriaga, F.; Torres-Palma, R. A.; Petrier, C.; Esplugas, S.; Gimenez, J.; Pulgarin, C. Ultrasonic treatment of water contaminated with ibuprofen. Water Res. 2008, 42, 4243−4248. (9) Ciriaco, L.; Anjo, C.; Correia, J.; Pacheco, M. J.; Lopes, A. Electrochemical degradation of ibuprofen on Ti/Pt/PbO2 and Si/BDD electrodes. Electrochim. Acta 2009, 54, 1464−1472. (10) Madhavan, J.; Grieser, F.; Ashokkumar, M. Combined advanced oxidation processes for the synergistic degradation of ibuprofen in aqueous environments. J. Hazard. Mater. 2010, 178 (1−3), 202−208. (11) Snyder, S. A.; Adham, S.; Redding, A. M.; Cannon, F. S.; DeCarolis, J.; Oppenheimer, J.; Wert, E. C.; Yoon, Y. Role of membranes and activated carbon in the removal of endocrine disruptors and pharmaceuticals. Desalination 2007, 202, 156−181. (12) Behera, S. K.; Oh, S. Y.; Park, H. S. Sorption of triclosan onto activated carbon, kaolinite and montmorillonite: effects of pH, ionic strength, and humic acid. J. Hazard. Mater. 2010, 179, 684−691. (13) Dabrowski, A.; Podkoscielny, P.; Hubicki, Z.; Barczak, M. Adsorption of phenolic compounds by activated carbon - a critical review. Chemosphere 2005, 58, 1049−1070. (14) Pan, B.; Pan, Bi.; Zhang, W.; Zhang, Q.; Zhang, Q.; Zheng, S. Adsorptive removal of phenol from aqueous phase by using a porous acrylic ester polymer. J. Hazard. Mater. 2008, 157 (2−3), 293−299. (15) Derbyshire, F.; Jagtoyen, M.; Andrews, R.; Rao, A.; MartinGullon, I.; Grulke, E. Chemistry and Physics of Carbon; Marcel Dekker: New York, 2001; Vol. 27, pp 1−66. (16) Mestre, A. S.; Pires, J.; Nogueira, J. M. F.; Carvalho, A. P. Activated carbons for the adsorption of ibuprofen. Carbon 2007, 45, 1979−1988. (17) Limousy, L.; Ghouma, I.; Ouederni, A.; Jeguirim, M. Amoxicillin removal from aqueous solution using activated carbon prepared by

CONCLUSIONS We have carried out equilibrium adsorption studies on the removal of several types of pharmaceuticals as pollutants from water effluents by GCMC simulations with realistic models of activated carbons. It has been observed that the activated carbon models are highly selective for pharmaceuticals, and the adsorption increases with the concentration in aqueous solution. In the equilibrium study, it was found that the Langmuir model provides good fitting, in agreement with a monolayer adsorption for the five considered pharmaceuticals. Results confirm the role of surface sites in the change of adsorption capacities. Comparatively, paracetamol shows the higher adsorption uptakes in all ACs, while amoxicillin shows the lowest values mainly due to its higher molecular dimensions. In addition, IBP is less adsorbed than DCF and NPX, and the effect is more pronounced in hydrophobic carbons (not functionalized). Results from the simulations are in good agreement with available experimental data, reinforcing the appropriateness of molecular modeling tools to understand the competing effects of these systems, and hence, to help in the design and optimization of adsorbent materials for this separation process. ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.langmuir.7b01967. Atom charges for drug molecules, radial distribution functions of obtained ACs, N2 adsorption isotherms and Langmuir and Freundlich fitting of pharmaceuticals in evaluated ACs (PDF)

■

■

This work is highly inspired by Keith Gubbins’ early contributions in developing realistic activated carbons, and more importantly, on the use of statistical mechanics and molecular simulations to gain fundamental understanding for practical implementations in real engineering problems. We deeply thank him for his inspiring work and influence in this field. This work was initiated in the framework of the NUCLI project Bioquim_Rescue: RD12-1-0018, financed by ACCIÓ (Catalan Government); this support is gratefully acknowledged. Part of the initial work of this study was carried out at MATGAS 2000 A.I.E.; access to the computer resources is gratefully appreciated.

■

■

Article

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Telephone: +97126075626. ORCID

Daniel Bahamon: 0000-0001-5473-1202 Lourdes F. Vega: 0000-0002-7609-4184 Notes

The authors declare no competing financial interest. 11153


Article

Langmuir chemical activation of olive stone. Environ. Sci. Pollut. Res. 2017, 24 (11), 9993−10004. (18) Dubey, S. P.; Dwivedi, A. D.; Sillanpäa,̈ M.; Gopal, K. Artemisia vulgaris-derived mesoporous honeycomb-shaped activated carbon for ibuprofen adsorption. Chem. Eng. J. 2010, 165, 537−544. (19) Makila, E.; Kivela, H.; Shrestha, N.; Correia, A.; Kaasalainen, M.; Kukk, E.; Hirvonen, J.; Santos, H. A.; Salonen, J. Influence of Surface Chemistry on Ibuprofen Adsorption and Confinement in Mesoporous Silicon Microparticles. Langmuir 2016, 32, 13020−13029. (20) Nielsen, L.; Biggs, M. J.; Skinner, W.; Bandosz, T. J. The effects of activated carbon surface features on the reactive adsorption of carbamazepine and sulfamethoxazole. Carbon 2014, 80, 419−432. (21) Nielsen, L.; Zhang, P.; Bandosz, T. J. Adsorption of carbamazepine on sludge/fish waste derived adsorbents: Effect of surface chemistry and texture. Chem. Eng. J. 2015, 267, 170−181. (22) Nielsen, L.; Bandosz, T. J. Analysis of sulfamethoxazole and trimethoprim adsorption on sewage sludge and fish waste derived adsorbents. Microporous Mesoporous Mater. 2016, 220, 58−72. (23) Nielsen, L.; Bandosz, T. J. Analysis of the competitive adsorption of pharmaceuticals on waste derived materials. Chem. Eng. J. 2016, 287, 139−147. (24) Allen, M. P.; Tildesley, D. J. Computer Simulation of Liquids; Clarendon Press: Oxford, UK, 1987; 408 p. (25) Furmaniak, S.; Gauden, P. A.; Terzyk, A. P.; Rychlicki, G.; Wesołowski, R. P.; Kowalczyk, P. Heterogeneous Do−Do model of water adsorption on carbons. J. Colloid Interface Sci. 2005, 290, 1−13. (26) Jorge, M.; Schumacher, C.; Seaton, N. A. Simulation Study of the Effect of the Chemical Heterogeneity of Activated Carbon on Water Adsorption. Langmuir 2002, 18, 9296−9306. (27) Ulberg, D. E.; Gubbins, K. E. Water adsorption in microporous graphitic carbons. Mol. Phys. 1995, 84, 1139−1153. (28) Bernini, M. C.; Fairen-Jimenez, D.; Pasinetti, M.; Ramirez-Pastor, A. J.; Snurr, R. Q. Screening of bio-compatible metal−organic frameworks as potential drug carriers using Monte Carlo simulations. J. Mater. Chem. B 2014, 2, 766−774. (29) Mohanambe, L.; Vasudevan, S. Anionic Clays Containing AntiInflammatory Drug Molecules: Comparison of Molecular Dynamics Simulation and Measurements. J. Phys. Chem. B 2005, 109, 15651− 15658. (30) Bahamon, D.; Carro, L.; Guri, S.; Vega, L. F. Computational study of ibuprofen removal from water by adsorption in realistic activated carbons. J. Colloid Interface Sci. 2017, 498, 323−334. (31) Bahamon, D.; Vega, L. F. Pharmaceuticals removal from water effluents by adsorption in activated carbons using Monte Carlo simulations. Proceedings of the 27th European Symposium on Computer Aided Process Engineering − ESCAPE 27, 2017. Computer Aided Chemical Engineering book series. (accepted). (32) Halling-Sørensen, B.; Nors Nielsen, N.; Lanzky, P. F.; Ingerslev, F.; HoltenLützhøft, H. C.; Jørgensen, S. E. Occurrence, fate and effects of pharmaceutical substances in the environment–a review. Chemosphere 1998, 36, 357−393. (33) Ikehata, K.; Naghashkar, N. J.; El-Din, M. G. Degradation of Aqueous Pharmaceuticals by Ozonation and Advanced Oxidation Processes: A Review. Ozone: Sci. Eng. 2006, 28 (6), 353−414. (34) Johnson, K. A.; Plumb, R. Investigating the human metabolism of acetaminophen using UPLC and exact mass oa-TOF MS. J. Pharm. Biomed. Anal. 2005, 39, 805−810. (35) Putra, E. K.; Pranowo, R.; Sunarso, J.; Indraswati, N.; Ismadji, S. Performance of activated carbon and bentonite for adsorption of amoxicillin from wastewater: Mechanisms, isotherms and kinetics. Water Res. 2009, 43, 2419−2430. (36) Dias, J. M.; Alvim-Ferraz, M. C. M.; Almeida, M. F.; Rivera-Utrilla, J.; Sánchez-Polo, M. Wate materials for activated carbon preparation and its use in aqueous-phase treatment: A review. J. Environ. Manage. 2007, 85, 833−846. (37) Szczygielska, A.; Burian, A.; Dore, J. C. Paracrystalline structure of activated carbons. J. Phys.: Condens. Matter 2001, 13, 5545−5561.

(38) Harris, P. J. F.; Burian, A.; Duber, S. High-resolution electron microscopy of a microporous carbon. Philos. Mag. Lett. 2000, 80, 381− 386. (39) Steele, W. A. The Interaction of Gases with Solid Surfaces; Pergamon Press: Oxford, UK, 1974; 349 p. (40) Liu, J. C.; Monson, P. A. Monte Carlo Simulation Study of Water Adsorption in Activated Carbon. Ind. Eng. Chem. Res. 2006, 45, 5649− 5656. (41) Di Biase, E.; Sarkisov, L. Systematic development of predictive molecular models of high surface area activated carbons for adsorption applications. Carbon 2013, 64, 262−280. (42) Segarra, E. I.; Glandt, E. D. Model microporous carbons: microstructure, surface polarity and gas adsorption. Chem. Eng. Sci. 1994, 49, 2953−2965. (43) Pikunic, J.; Clinard, C.; Cohaut, N.; Gubbins, K. E.; Guet, J. M.; Pellenq, R. J. M.; Rannou, I.; Rouzaud, J.-N. Structural Modeling of Porous Carbons: Constrained Reverse Monte Carlo Method. Langmuir 2003, 19, 8565−8582. (44) Kowalczyk, P.; Terzyk, A. P.; Gauden, P. A.; Furmaniak, S.; Wisniewski, M.; Burian, A.; Hawelek, L.; Kaneko, K.; Neimark, A. V. Carbon Molecular Sieves: Reconstruction of Atomistic Structural Models with Experimental Constraints. J. Phys. Chem. C 2014, 118, 12996−13007. (45) O’Malley, B.; Snook, I.; McCulloch, D. Reverse Monte Carlo analysis of the structure of glassy carbon using electron-microscopy data. Phys. Rev. B: Condens. Matter Mater. Phys. 1998, 57, 14148−14157. (46) Thomson, K. T.; Gubbins, K. E. Modeling Structural Morphology of Microporous Carbons by Reverse Monte Carlo. Langmuir 2000, 16, 5761−5773. (47) Gonciaruk, A.; Siperstein, F. R. In silico designed microporous carbons. Carbon 2015, 88, 185−195. (48) Larsen, G. S.; Lin, P.; Hart, K. E.; Colina, C. M. Molecular Simulations of PIM-1-like Polymers of Intrinsic Microporosity. Macromolecules 2011, 44, 6944−6951. (49) Albesa, A. G.; Rafti, M.; Rawat, D. S.; Vicente, J. L.; Migone, A. D. Ethane/ethylene adsorption on carbon nanotubes: temperature and size effects on separation capacity. Langmuir 2012, 28, 1824−1832. (50) Brennan, J. K.; Bandosz, T. J.; Thomson, K. T.; Gubbins, K. E. Water in porous carbons. Colloids Surf., A 2001, 187, 539−568. (51) Muller, E. A.; Hung, F. R.; Gubbins, K. E. Adsorption of Water Vapor−methane mixtures on activated carbons. Langmuir 2000, 16, 5418−5424. (52) Materials Studio v6.1; Accelrys Software, Inc: San Diego, CA, 2013. (53) Siepmann, J. I.; Frenkel, D. Configurational bias Monte Carlo: a new sampling scheme for flexible chains. Mol. Phys. 1992, 75, 59−70. (54) Abascal, J. L. F.; Vega, C. A. A general purpose model for the condensed phases of water: TIP4P/2005. J. Chem. Phys. 2005, 123, 234505. (55) Wilmer, C. E.; Kim, K. C.; Snurr, R. Q. An Extended Charge Equilibration Method. J. Phys. Chem. Lett. 2012, 3, 2506−2511. (56) Jorgensen, W. L.; Maxwell, D. S.; Tirado-Rives, J. Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids. J. Am. Chem. Soc. 1996, 118, 11225−11236. (57) Potoff, J. J.; Siepmann, J. I. Vapor−liquid equilibria of mixtures containing alkanes, carbon dioxide, and nitrogen. AIChE J. 2001, 47, 1676−1682. (58) Domínguez, J. R.; González, T.; Palo, P. E.; Cuerda-Correa, M. Removal of common pharmaceuticals present in surface waters by Amberlite XAD-7 acrylic-ester-resin: Influence of pH and presence of other drugs. Desalination 2011, 269, 231−238. (59) Guedidi, H.; Reinert, L.; Leveque, J. M.; Soneda, Y.; Bellakhal, N.; Duclaux, L. The effects of the surface oxidation of activated carbon, the solution pH and the temperature on adsorption of ibuprofen. Carbon 2013, 54, 432−443. (60) Mansouri, H.; Carmona, R. J.; Gomis-Berenguer, A.; SouissiNajar, S.; Ouederni, A.; Ania, C. O. Competitive adsorption of ibuprofen 11154


Article

Langmuir and amoxicillin mixtures from aqueous solution on activated carbons. J. Colloid Interface Sci. 2015, 449, 252−260. (61) MacDonald, J. A. F.; Evans, M. J. B.; Liang, S.; Meech, S. E.; Norman, P. R.; Pears, L. Chlorine and oxygen on the carbon surface. Carbon 2000, 38, 1825−1830. (62) Bradley, R. H.; Rand, B. On the Physical Adsorption of Vapors by Microporous Carbons. J. Colloid Interface Sci. 1995, 169, 168−176. (63) Jagiello, J.; Olivier, J. P. 2D-NLDFT Adsorption Models for Carbon Slit-Shaped Pores with Surface Energetical Heterogeneity and Geometrical Corrugation. Carbon 2013, 55, 70−80. (64) Herdes, C.; Santos, M. A.; Medina, F.; Vega, L. F. Pore Size Distribution Analysis of Selected Hexagonal Mesoporous Silicas by Grand Canonical Monte Carlo Simulations. Langmuir 2005, 21, 8733− 8742. (65) Baccar, R.; Sarrà, M.; Bouzid, J.; Feki, M.; Blánquez, P. Removal of pharmaceutical compounds by activated carbon prepared from agricultural by-product. Chem. Eng. J. 2012, 211−212, 310−317. (66) Galhetas, M.; Andrade, M. A.; Mestre, A. S.; Kangni-foli, E.; Villa de Brito, M. J.; Pinto, M. L.; Lopes, H.; Carvalho, A. P. The influence of the textural properties of activated carbons on acetaminophen adsorption at different temperatures. Phys. Chem. Chem. Phys. 2015, 17, 12340. (67) Moreno-Castilla, C. Adsorption of organic molecules from aqueous solutions of carbon materials. Carbon 2004, 42, 83−94. (68) Cho, H. H.; Huang, H.; Schwab, K. Effects of Solution Chemistry on the Adsorption of Ibuprofen and Triclosan onto Carbon Nanotubes. Langmuir 2011, 27, 12960−12967. (69) Kwiatkowski, M. Application of fast multivariant identification technique of adsorption systems to analyze influence of production process conditions on obtained microporous structure parameters of carbonaceous adsorbents. Microporous Mesoporous Mater. 2008, 115, 314−331.

11155


A Monte Carlo Simulation Study - ACS Publications - American

Recommend Documents