Functionalized Graphene Sheets Embedded in Chitosan

Article pubs.acs.org/IECR

Functionalized Graphene Sheets Embedded in Chitosan Nanocomposite Membranes for Ethanol and Isopropanol Dehydration via Pervaporation Suhas P. Dharupaneedi,† Raghu V. Anjanapura,*,† Jeong M. Han,‡ and Tejraj M. Aminabhavi§ †

Materials Science Division, Poornaprajna Institute of Scientific Research, Bengaluru 562-110, India Department of Chemistry, Energy Harvest-Storage Research Centre, University of Ulsan, Ulsan 680-749, Korea § Soniya College of Pharmacy, S. R. Nagar, Dharwad 580-002, India ‡

S Supporting Information *

ABSTRACT: Pervaporation is an important alternative membrane separation process compared to the distillation technique, and a relatively high separation factor is required to lower the energy demand. Solution processable nanocomposite membranes prepared by incorporating functionalized graphene sheets (FGS) loaded in various concentrations into the chitosan matrix have been employed for the pervaporative dehydration of ethanol and isopropanol. Incorporation of FGS leads to an increase of surface hydrophilicity of the chitosan membranes along with an increase in membrane tortuosity that was favorable to the selective permeation of water molecules. The nanocomposite membrane containing 2.5 wt % FGS gave the highest selectivities of 7781 and 1093 for isopropanol−water and ethanol−water mixtures, respectively, when tested for 10 wt % water-containing feed mixture. Membranes were characterized by wide-angle XRD, SEM, contact angle, and optical profilometry techniques. The Flory−Huggins theory was employed to estimate the polymer−solvent interaction parameter. Diffusion values and Arrhenius activation energy parameters provided quantitative evidence for the observed increase in water selectivity at higher loading of FGS.

1. INTRODUCTION Ethanol and isopropanol are important members of the alcohol family that find a myriad of applications including in paints, coatings, adhesives, cosmetics, pharmaceuticals, and as cleansing agents in semiconductor industries.1 Of these, ethanol is a renewable “biofuel”; as per the European Union, fuel grade ethanol should contain at least 98.7 wt % ethanol.2−5 In many industrial applications, high-purity ethanol (EtOH) or isopropanol (IPA) is required; both are miscible with water in all proportions and form azeotropes at low water concentrations. For separating these mixtures, distillation can be used, but it is effective only to a certain limit (i.e., at a concentration of 80−85 wt % of alcohol), after which the method becomes costly, especially near the azeotropic point (EtOH 95.6 wt %; IPA 87.7 wt %).6 Therefore, to get high-purity alcohols, the binary mixture has to be concentrated to 80−85 wt % followed by azeotropic distillation, molecular sieve adsorption, etc., methods.7−11 Among the other purification techniques, pervaporation (PV) appears to be the method with the most potential to separate liquids as it has a low energy consumption and is easily performed,12 but its success depends on developing a suitable membrane that can selectively separate the component liquid from its mixture.13,14 Usually, hydrophilic membranes prepared from polymers such as poly(vinyl alcohol), chitosan, sodium alginate, polyacrylonitrile, and polyimide, have been widely employed in alcohol dehydration studies.10,15−17 In the past decade, efforts to develop durable PV membranes include those of cross-linked polymers, blends, grafts, and nanocomposite membranes.10 To develop nanocomposite © XXXX American Chemical Society

membranes, suitable nanofillers have been added in a polymer matrix.18−20 Fillers such as clay,21 heteropolyacid,22 zeolite,23 mesoporous silica,24 single-walled aluminosilicate nanotubes,25 zeolite immedazolite framework (ZIF),26 titanate nanotubes,27 carbon nanotubes,28 and graphene29 have also been employed. Of these, graphene that consists of sp2 hybridized carbon atoms linked to one another in a honeycomb lattice has a high surface area30 of 2600 m2 g−1 along with excellent mechanical stability, high electrical and thermal conductivities, and selective mass transport.31 The nascent form of graphene is hydrophobic, but its oxide is hydrophilic, due to the presence of oxygenated functional groups. The focus of our research reported here originated from the work of Nair et al.,31 who used submicrometer thick membranes prepared by using graphene oxide that showed much higher permeance to water compared to organics. This prompted us to develop nanocomposite membranes of chitosan by loading functionalized graphene sheets (FGS) as a filler to test for PV dehydration of water−ethanol or water− isopropanol mixtures. Previously,29 we have employed FGSloaded sodium alginate (NaAlg) nanocomposite membranes for the PV dehydration of isopropanol that performed well at 2 wt % of filler loading. In continuation of this work, we now report the development of nanocomposite membranes of chitosan (CS), because the presence of −OH and −NH3+ Received: July 11, 2014 Revised: August 25, 2014 Accepted: August 25, 2014

A

dx.doi.org/10.1021/ie502751h | Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Industrial & Engineering Chemistry Research

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solutions at least four to five times, followed by soaking in methanol for 24 h (to remove the unreacted glutaraldehyde). Using the above procedure, 1, 2, 2.5, and 3 wt % of FGS-loaded nanocomposite membranes were prepared and designated CS1, CS-2, CS-2.5, and CS-3, respectively. The cross-linked nascent CS membrane designated CS was prepared in the same manner, but without adding FGS. All of the membranes were black in color with an average thickness of around 84 μm as measured by a micrometer screw gauge and cross-sectional SEM. Wide-Angle X-ray Diffraction. To understand the influence of filler loading on solid state morphology of chitosan, XRD studies were carried out using a Bruker D-2 α-phaser. The X-rays of wavelength 1.54 Å were generated by Cu Kα radiation source and filtered through nickel windows. The membrane samples were crushed under a liquid nitrogen environment, and powdered samples were scanned in the range of 2θ = 5−60° at the scanning rate of 2°/min. Field Emission Scanning Electron Microscopy. The surface and cross-sectional SEM micrographs of all of the membranes were taken using an FE-SEM Zeiss Ultra 55 (at the Centre for Nano Science and Electronics, Indian Institute of Science, Bangalore, India). This information is critical to assess the interaction of filler particles with the chitosan matrix. Because chitosan is nonconductive to electrons and to improve the image quality, all of the samples were coated with a conductive layer of sputtered gold. Contact Angle. The relative hydrophilicity of the membranes was quantified by measurements of water contact angle as per Sessile drop method using Data Physics OCA-20 at 25 °C. Perfectly dried membrane samples were cut in dimensions of 1 cm × 7 cm and were adhered to a clean glass slide; a 2 μL deionized water droplet was placed on the membrane sample, and the droplet image was captured within 10 s using a CCD camera. A total of five measurements were taken at different regions of the membrane to calculate the average value. Optical Profilometry. The membrane surface profile (surface roughness) was studied using a noncontact mode optical profilometer “TalySurf CCI” (available at the Centre for Nano Science and Engineering, Indian Institute of Science) by mounting the samples on a clean glass slide prior to the measurement. The maximum scan ranges for the profilometer in x, y, and z directions were 337, 337, and 31 μm, respectively, with a resolution of 0.01 nm in the z direction. From the topological images, the roughness value (Rq) was calculated. Thermogravimetric Analysis (TGA). Thermal stability and degradation behavior of nascent chitosan as well as its composite membranes were evaluated by thermogravimetry (TGA Q500). The nonoxidative degradation was carried out under a constent flow of nitrogen gas at 50 mL min−1, over the temperature range of 40−600 °C with a ramp rate of 10 °C min−1. For each analysis, about 6−9 mg of liquid nitrogen crushed membrane samples was taken in an aluminum pan. Equilibrium Swelling. Equilibrium swelling of the membranes was performed gravimetrically at 30 °C by soaking the mixtures of EtOH or IPA with water. A preweighed (Wd) and circularly cut membrane sample (diameter = 2.5 cm) was taken in an airtight test bottle containing 30 cm3 of the test sample (10, 20, 30, and 40 wt % of water in each alcoholcontaining mixture). Membranes were soaked in such test solvent media for 48 h, which were taken from the test solvent and wiped with soft tissue paper wraps to remove the surface-

functionalities in CS allows for both hydrophilic and electrostatic interactions. The physicochemical interactions between the filler FGS nanoparticles and the CS matrix were assessed by wide-angle X-ray diffraction (W-XRD), contact angle, and field emission scanning electron microscopy (FE-SEM) techniques. Separation characteristics of the membranes were discussed in terms of sorption and diffusion principles as well as the Arrhenius activation energy parameters for permeation and diffusion processes.

2. EXPERIMENTAL METHODS Materials. Natural graphite (HC-908) was purchased from Hyundai Coma Co. Ltd., South Korea. Chitosan (extra pure) was procured from SRL Co. Ltd., Mumbai, India. Isopropanol (IPA), glacial acetic acid (100%), concentrated (35%) HCl, nitric acid, potassium chloride, and glutaraldehyde were all procured from s d fine chemicals, Mumbai, India. Absolute ethanol (EtOH) (99.9%) was purchased from Commercial Alcohols, Brampton, Canada. All other chemicals used in this work were of reagent grade and used without further purification. Double-distilled water was used throughout the study. Preparation of Functionalized Graphene Sheets. We have followed the modified Brodie method for preparing FGS using graphite as a precursor.32 For this, 200 mL of ice-cooled fuming nitric acid was taken in a round-bottom flask, to which 10 g of graphite was added under constant stirring. Gradually, the temperature was brought to room temperature by adding potassium chloride. Consequently, the reactants were mixed thoroughly for 24 h and then transferred to a beaker containing 3 L of double-distilled water. The product so obtained was filtered, washed with a copious amount of water until it turns neutral, and dried in a hot air oven at 100 °C. Thus, the obtained graphite oxide had the empirical formula C10O3.45H1.58 as tested by elemental analysis. To convert graphite oxide to individual graphene sheets, the dried graphite oxide was introduced into a quartz tube under a constant flow of nitrogen gas for 5 min. The quartz tube was introduced into a furnace maintained at 1100 °C, where the layers of graphite oxide were split into individual graphene sheets by releasing CO2 gas. Hence, prepared graphene sheets have the empirical formula C10O0.78H0.38. The morphology and composition of FGS were examined by SEM, EDX, XRD, FTIR, and BET studies. Membrane Fabrication. Chitosan nanocomposite membranes were prepared by solution casting followed by solvent evaporation. In a typical procedure, viscous chitosan solution was prepared by mixing chitosan, acetic acid, and water in the ratio of 3:3:94, and the mixture was allowed to stand overnight to get rid of air bubbles. In a separate beaker, the required amount of FGS was taken in 20 mL of water, stirred, and sonicated for 2 h. Later, 10% of CS solution was added and stirred for 12 h, followed by a transfer of the remaining CS solution along with dropwise addition of 1 mL of glacial acetic acid to facilitate the mixing process. The entire mixture was then stirred magnetically for 24 h and the resulting solution was poured onto a perfectly aligned clean glass plate kept in a dustfree environment and dried under ambient conditions. The dried membranes were removed from the glass plate and immersed in a cross-linking bath containing a water−acetone mixture (30:70) along with 2.5 mL of glutaraldehyde (crosslinking agent) and 1 mL of HCl as a catalyst. The cross-linked membranes were repeatedly rinsed with water and methanol B



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Figure 1. (a) FE-SEM image of FGS; (b, inset) SEM image at higher resolution; (c) EDX image of FGS; (d) EDX analysis showing percent composition of FGS; (e) FTIR spectrum of FGS; (f) XRD of FGS.

the average values were considered. For each experimental condition, at least two identical measurements were taken. The membranes were found to be stable for almost 8 h of continuous operation. The driving force normalized parameters, namely, permeability (PGi ) and selectivity (αij) were calculated as per the procedure suggested by Baker and Wijmans explained below.34 Permeability (PGi ) was calculated using

adhered liquid droplets to measure the swollen membrane weight (Ws). Using these data, percent equilibrium swelling was calculated as ⎛ W − Wd ⎞ equilibrium swelling (%) = ⎜ s ⎟ × 100 ⎝ Wd ⎠

(1)

Pervaporation. All of the PV experiments were conducted in an indigenously built stainless steel unit as per the design described elsewhere.33 In a typical setup, membranes were mounted in the PV unit with the help of two Teflon O-rings supported onto a porous stainless steel disk. The top surface of the membrane was in direct contact with the feed tank (total capacity ≈ 500 mL) in which 70 g of the test solvent was taken. The feed tank is a double-walled stainless steel container in which the required temperature of the feed mixture was maintained by pumping preheated water through the outer jacket of the feed tank using a thermostatic bath (Grant, UK, model GD 120). During the experiment, the top surface of the membrane was maintained at atmospheric pressure, whereas the bottom surface was maintained at a reduced pressure of >5 mbar using a double-stage Telstar vacuum pump. Permeate was collected in two glass traps maintained at the liquid nitrogen environment after attaining a steady state equilibrium, and compositions of the feed and permeate mixtures were analyzed by refractive index as well as gas chromatography (GC) measurements. To measure the refractive index, a refractometer (Mettler-Toledo) with an accuracy of ±0.0001 unit was used, from which standard graphs of refractive index versus known mixture composition were constructed. For GC measurements, a chromatograph (Thermofisher, Trace-700) coupled with a TCD detector and a Porapack Q column was used. Here, 1 μL of the sample was taken along with carrier nitrogen gas (with a flow rate of 1.5 μL min−1) at the oven temperature of 150 °C. For each composition, three separate sample readings were taken, and

⎞ ⎛ l ⎟ PiG = DiK iG = ji ⎜⎜ f p⎟ ⎝ pi − pi ⎠

(2)

where the subscript i represents either EtOH, IPA, or water; Di and KGi are diffusion and sorption coefficients of the ith component, respectively; l indicates the membrane thickness, ji is the molar flux of the ith component; and pfi and ppi are ith component vapor pressures of the feed and permeate, respectively. Permeability data are reported in Barrer units (1 Barrer = 1 × 10−10 cm3 (STP) cm/cm2·s·cm·Hg). By using flux (Ji) in g/m2·h obtained from the PV experiment and the molar volume (vi) [22.4l (STP)/mol] and molecular weight (mi) of the ith component, the molar flux (ji) was calculated as

⎛ J vi ⎞ ji = ⎜ i ⎟ ⎝ mi ⎠

(3)

Using the van Laar equation, partial vapor pressure (pfi) was calculated as

pif = xiγipis

(4)

where xi, γi, and psi are molar concentration, activity coefficient, and saturated vapor pressure, respectively, of the ith component. The saturated vapor pressure, psi was computed using the Antoine equation given as C



log pis =

⎛ A − B⎞ ⎜ ⎟ ⎝T + C ⎠

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linkage arising between the CS chains, leading to ordered arrangement. With increasing FGS loading, the peak intensity gradually decreases, suggesting a molecular level dispersion of FGS in the CS matrix. Field Emission Scanning Electron Microscopy. SEM images of FGS are bright due to their conducting property, but those of nascent CS show a smooth surface and the image is dark due to the nonconductive nature of chitosan (see Figure 3a). Bright spots observed in CS nanocomposite membranes as can be seen in Figure 3b−g are due to the presence of FGS. The cross-sectional SEM images of CS-1, CS-2.5, and CS-3 shown in Figure 3c,e,g, respectively exhibit a densely packed matrix with grooves,39 with a membrane thickness of around 84 μm. However, the top surfaces of all CS nanocomposite membranes shown in Figure 3b,d,f are rough, in contrast to the smooth surfaces observed for nascent CS membrane (Figure 3a), but these bright spots increase with increasing FGS loading. However, higher resolution images shown in Figure 3b,d,f reveal the dispersion of FGS nanoparticles in CS-1 and CS-2.5, but for CS-3, agglomeration is observed, suggesting optimum performance of the membranes occurring at 2.5 wt % FGS loading, above which particle agglomeration might result in a reduced performance of the membrane. Contact Angle. Water contact angle data were measured to assess the relative hydrophilicity of the nanocomposite membranes, which is inversely proportional to the contact angle; the lower the contact angle, the more hydrophilic the membrane and vice versa. Contact angle data shown in Figure 4 decrease systematically with increasing FGS loading showing the sequence 88° (CS) > 85° (CS-1) > 83° (CS-2.5) > 79° (CS-3). This pattern is due to the combined influence of enhanced surface hydrophilicity and roughness with FGS loading. Optical Profilometer. Topological images of CS, CS-1, CS-2.5, and CS-3 membranes obtained from the optical profilometer with their respective Rq values shown in Figure 5a−d, respectively, give a general description of the surface roughness in terms of Rq (height) values.16 From the topographs, we can see that with an increase of FGS loading, surface roughness values also increased, suggesting increased surface area of the nanocomposite membrane. Thermogravimetric Analysis. Thermal analysis was performed to understand the pyrolysis mechanism, which shows a combination of release of moisture followed by breaking of the main polymer chains. The nonoxidative degradation curves of nascent CS, CS-1, CS-2.5, and CS-3 are presented in Figure 6, for which release of moisture occurs around 100 °C with a weight loss of 10−15 wt %. The polymer chain breaking starts at around 230 °C with a major weight loss of 30−40 wt %. As the FGS loading increases in the matrix, the thermal stability improves due to hydrophilic and electrostatic interactions between CS and FGS. Equilibrium Swelling (ES). ES data of the membrane can be used to assess the membrane performance. In this study, the effect of FGS loading on ES of IPA−water and EtOH−water mixtures as displayed in Figure 7 suggests higher swelling with increasing concentration of water in the feed, which shows the water-selective nature of the membranes. Moreover, ES of CS increased with increasing loading of FGS. Hence, higher filler loading facilitated a larger free volume toward solvent diffusion. The membranes exhibit a higher ES for the IPA−water mixture than with the EtOH−water mixture due to highly polar nature

(5)

Here, A, B, and C are Antoine constants obtained from the literature,35 and T is the temperature in Kelvin. Membrane selectivity was then calculated as the ratio of permeabilites of components i (water) and j (EtOH or IPA). αij =

PGi

PiG PjG

(6)

PGj

and refer to permeability of water and alcohol (EtOH or IPA), respectively.

3. RESULTS AND DISCUSSION Characterization of FGS. The FE-SEM (Figure 1a,b) images of FGS reveal a bright crumpled paper-like surface with many wrinkles and folding that are typical of graphene surfaces.36 To evaluate the percent oxygen content of the graphene surface, SEM-EDX analysis was carried out. The green dots shown in Figure 1c suggest the presence of oxygen on the graphene surface, and as shown in Figure 1d, the oxygen on the graphene surface was 21% (atomic weight), suggesting an oxygen-rich nature of FGS. The FTIR spectrum of FGS in Figure 1e shows broad absorption bands at 3430, 1540, and 1230 cm−1 corresponding to oxygen-containing functional groups of FGS, revealing successful oxidation of graphene. The XRD of FGS in Figure 1f shows a peak at 2θ of 26.3°, due to the two-dimensional (0 0 2) plane of graphene. On the basis of the d value, the spacing was found to be 0.339 nm, similar to that reported by Choi et al.37 The surface of FGS as measured by the BET method using the nitrogen adsorption technique in a dry state was found to be 428 m2 g−1. Considering that a single layer of graphene has a surface area of 2600 m2 g−1, the recorded surface area suggests the presence of six-layered FGS. As a consequence of the large oxygen-rich hydrophilic surface along with a smaller layer spacing of 0.339 nm, FGS incorporation into a chitosan matrix is expected to enhance the overall performance of the nanocomposite membrane. Wide-Angle X-ray Diffraction. Figure 2 depicts XRD plots of nascent CS and its composites, in which CS displays two peaks, of which one is a sharp peak at 2θ of 10−12°, whereas the other is a weak peak observed38 at 18−22°. These are hydrated semicrystalline peaks, mainly due to H-bonding

Figure 2. XRD of nascent CS and its nanocomposite membranes with different loadings of FGS, showing a decrease in peak intensity with an increase in FGS loading. D



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Figure 3. FE-SEM (top view) of (a) nascent CS, (b) CS-1, (d) CS-2.5, and (f) CS-3 (insets b, d, and f, SEM images at higher resolution) and FESEM (cross-sectional view) of (c) CS-1, (e) CS-2.5, and (g) CS-3.

supported from the contact angle and optical profilometer data. The diffusion selectivity of water arises from two factors. First, the presence of polar groups and the anionic charge on FGS particles will establish multiple interactions with the chitosan matrix. As a consequence of this, polymer free volume would decrease, leading to enhanced tortuous pathways inside the membrane. Second, the FGS layers act as water-selective channels.40 Both permeability and selectivity values increase linearly with increasing FGS loading up to 2.5 wt %; above this limit only permeability increases with a decrease in selectivity, because the membranes seem to attain saturation limit at 2.5 wt % of FGS loading, above which agglomeration results. Effect of Feed Water Content. As shown in Table 1, an increase in the water composition of the feed mixture increases the permeability with a decrease in selectivity. For CS-2 membrane with IPA−water feed, increase in water concentration from 10 to 40 wt % increases the permeability values from 101 to 149 Barrer, but selectivity drops from 3221 to 211. Similarly, an increase in feed water composition in the case of EtOH−water mixture decreases the selectivity value from 547 to 235. Such a decrease in membrane performance at high water concentration of the feed mixture is due to enhanced dissolution of water molecules. Also, increase in feed water concentration would result in an increase of driving force for permeation, because the presence of polar groups on the membrane surface would absorb more water molecules at higher feed water composition. In any case, increase in water concentration would enhance the membrane swelling process, resulting in an enlarged free volume (as also supported by equilibrium swelling). Thus, an increase of free volume would

Figure 4. Change in water contact angle (θ°) of the nanocomposite membranes as a function of wt % of FGS loading, showing an increase in hydrophlicity with an increase in FGS loading.

of EtOH forming an associated molecule with water, which is more difficult to permeate through the matrix. Effect of Functionalized Graphene Sheet Loading on Pervaporation Performance. FE-SEM studies indicate uniform dispersion of FGS nanoparticles in the CS matrix. With regard to the membrane performance, in the case of nanocomposite membranes, permeability and selectivity values for IPA and EtOH are higher than the nascent CS membrane as shown in Table 1 as well as displayed in Figure 8. This is attributed to the enhancement of sorption and diffusion selectivity of the membranes toward water molecules. Water sorption selectivity increased due to the polar nature of FGS, resulting in an increase of membrane hydrophilicity; this is also E



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Figure 5. Optical profilometer topological images of (a) nascent CS, (b) CS-1, (c) CS-2.5, and (d) CS-3, showing an increase in surface roughness with an increase in FGS loading.

Figure 6. Nonoxidative TGA curves of CS, CS-1, CS-2.5, and CS-3, showing an increase in thermal stability with FGS loading.

increase the permeation of water and both the alcohols, thereby resulting in enhanced permeability at the cost of selectivity. Effect of Temperature. An increase of temperature results in an increase in the nonselective permeation of feed molecules. For 10 wt % of IPA−water mixture, in the case of CS-2 membrane (see Figure 9a) as the temperature increases from 30 to 60 °C, the selectivity decreases from 3221 to 496, but permeability increases from 101 to 119 Barrer. Similar results were observed for the CS-2.5 membrane with 10 wt % of water in EtOH feed mixtures, for which a gradual increase in temperature from 30 to 50 °C results in an increase of permeability from 84 to 108 Barrer, but selectivity values decrease from 1093 to 584 as shown in Figure 9b. The increase in feed temperature led to an increase in partial vapor pressure of the feed mixture as alcohols with a low boiling point have higher values of vapor pressures than water. Therefore, an increase in feed temperature results in an increase of permeation of alcohols compared to that of water. In addition, segmental movement of polymer chains also increases with an increase in temperature, resulting in an increase of free volume, which reduces the diffusion resistance of the membranes to

Figure 7. Equilibrium swelling (%) of membranes for binary mixtures of (a) 10 wt % water−IPA and (b) 10 wt % water−EtOH.

alcohols. However, the mobility of alcohols and water increases with temperature, resulting in an increased transport of liquids across the membrane. The temperature dependence of permeability and diffusivity followed the Arrhenius trends, and these data were used to calculate the activation energy values for permeation and diffusion processes. EtOH−Water versus IPA−Water Feed Mixtures. As shown in Table 1, the membranes exhibit higher permeability and selectivity for the IPA−water mixture than for the EtOH− water mixture. A decrease in selectivity for the EtOH−water F



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Table 1. Relevant Data on Membrane Performance for IPA−Water and EtOH−Water Mixtures IPA−water feed water (wt %)

ES (wt %)

αij

Dw × 1013 (m2 s−1)

nascent CS

10 20 30 40

22 30 36 44

381 283 178 142

3.50 4.68 6.37 8.66

CS-1

10 20 30 40

28 34 39 47

1209 681 357 223

CS-2

10 20 30 40

34 39 43 50

CS-2.5

10 20 30 40

CS-3

10 20 30 40

membrane

EtOH−water DIPA × 1016 (m2 s−1)

ES (wt %)

αij

Dw × 1013 (m2 s−1)

DEtOH × 1015 (m2 s−1)

11 19 42 71

19 24 29 34

237 207 182 171

2.48 3.27 4.61 6.93

12 18 29 47

3.84 4.97 6.51 9.07

4 8 21 48

23 28 32 36

493 435 352 288

3.18 4.03 5.24 7.08

7 11 17 29

3221 538 298 211

4.39 5.88 7.53 9.67

1 10 23 42

26 30 34 39

547 360 298 235

3.49 4.31 5.62 7.66

6 10 16 28

36 41 45 52

7711 725 396 283

4.49 6.10 8.03 10.17

0.6 9 24 42

29 32 36 42

1093 510 379 318

3.68 4.42 6.00 7.94

4 9 18 29

38 44 48 54

4202 703 382 269

5.00 6.56 8.55 11.37

1 11 26 50

32 34 38 44

624 462 369 296

4.31 5.22 6.52 8.55

8 13 20 34

Figure 8. Effect of filler loading on permeability (Barrer) and selectivity of nascent chitosan and FGS-loaded chitosan nanocomposite membranes for 10 wt % water containing IPA mixture at 30 °C.

mixture is due to the smaller size and higher polarity of EtOH than IPA. As a consequence, EtOH has a lower sorption and diffusion resistivity than IPA. Furthermore, higher polarity of EtOH would result in a molecular association, thereby reducing its transport across the membrane.

4. THEORETICAL CALCULATIONS Diffusion Coefficient. According to sorption−diffusion principles, diffusion is a kinetic parameter, which determines the rate at which liquid molecules permeate through the membrane; the diffusion coefficients through the membrane can be calculated using41

Figure 9. Temperature dependence of permeability (Barrer) and selectivity for (a) CS-2 at 10 wt % of water in IPA and (b) for CS-2.5 at 10 wt % of water in EtOH, showing a decrease in selectivity with an increase in temperature.

G



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Table 2. Thermodynamic Data on Membrane−Permeate Systems F−H parameters membrane

χ1p

χ2p(IPA)

χ2p(EtOH)

δ p (J1/2·cm−3/2)

Mc (g/mol)

Ve × 103 (mol/cm3)

nascent CS CS-1 CS-2 CS-2.5 CS-3

−20.37 −30.95 −35.50 −38.99 −36.63

−3.30 −3.07 −2.78 −2.59 −2.62

−3.70 −3.40 −3.05 −2.80 −2.88

101.6 113.9 118.6 121.9 119.7

442 249 215 193 217

3.48 6.53 7.86 8.88 8.19

Di =

Pi Ki

where Wm is the weight of dry membrane and Vb is the volume of benzene displaced. Then, eq 9 can be simplified to calculate the F−H interaction parameter χip as43

(7)

where Pi is permeation flux/unit area (kg/m2·s) and Ki is the sorption coefficient of the ith component (water or IPA or EtOH) inside the membrane matrix (m3(STP) m−3·mmHg−1). The values of Ki were calculated using

Ki =

Ci pi

⎛ ln ϕ + (1 − ϕ) ⎞ i i ⎟ χi p = −⎜⎜ ⎟ 2 ⎝ (1 − ϕi) ⎠

From the calculated values of χip, information about polymer−solvent interaction (i stands for water, EtOH, or IPA) was obtained. The χip values were also used to calculate the solubility parameter (δ) values using44

(8)

Here, Ci stands for liquid concentration inside the membrane and pi is partial pressure of the ith component. Diffusion coefficients of water are 2 orders of magnitude higher than those of both EtOH and IPA as shown in Table 1 due to the inherent water-selective nature of the nanocomposite membranes. At increased FGS loading, a linear increase in diffusion selectivity of water with a corresponding decrease in alcohol selectivity was observed, due to a decrease in free volume and increase in tortuous pathways. At FGS loading of >2.5 wt %, increase in permeation of both water and alcohols is observed, due to agglomeration of FGS nanoparticles at higher loading, and such membranes showed better selectivity for IPA−water than for EtOH−water, due to the smaller size and higher polarity of EtOH than of IPA. Increase of both feed water concentration and temperature showed an adverse effect on diffusion selectivity of the nanocomposite membranes due to an increase in free volume caused by the increase in segmental movement of the polymer chains coupled with enhanced swelling of the membrane. Interaction Parameter (χ) and Solubility Parameter (δ). In a PV process, sorption is a thermodynamic quantity, which determines the extent of membrane−solvent interaction. Sorption coefficients calculated from the swelling data were employed to calculate the activity of solvent αi, using the Flory−Huggin theory:42 ln αi = ln ϕi + (1 − ϕi) + χi p (1 − ϕi)2

χi p = 0.35 +

⎡ (χ − 0.35)RT ⎤1/2 ip ⎥ δp = δi ± ⎢ Vi ⎢⎣ ⎥⎦

(13)

(14)

Here, the δ value for water was taken as 47.8 J1/2·cm−3/2, and sorption data were measured in EtOH, IPA, or water, separately, but i = water values matched better for the determination of membrane solvent solubility δp, and these data were used to calculate the solubility parameter (δ). As per the Flory−Huggins theory, lower values of χ indicate stronger interaction16 (see data compiled in Table 2). For all of the membranes, χ1p values are quite higher than χ2p by an order of magnitude for both EtOH and IPA. The δp values also show increasing trends with increasing FGS loading, indicating enhanced membrane solubility. As per contact angle data, these changes can be attributed to the high density of polar groups on the FGS-loaded membrane surface as the nanocomposite membranes interact more selectively with the more polar water molecules than the less polar alcohols. However, this trend is reversed at high FGS loading (2.5 wt %, a decrease in interface interaction due to selfagglomeration of FGS nanoparticles might have resulted in a reversal of this trend. Arrhenius Activation Energy. The temperature dependency of permeation and diffusion was analyzed by the Arrhenius equation by calculating the activation parameters using

⎛ −E ⎞ X = Xo exp⎜ x ⎟ ⎝ RT ⎠

ΔHs

(a) IPA−Water 29.76 −21.54 47.68 28.79 −21.11 68.48 27.53 −20.39 80.63 24.48 −18.64 106.82 25.55 −18.77 81.24 (b) EtOH−Water 13.00 −0.97 18.50 9.85 −0.36 21.52 8.88 −0.59 27.46 7.52 −1.07 40.90 8.03 −0.44 37.89

(17)

where Ex is the activation energy for permeation or diffusion, R is the universal gas constant, T is the temperature in Kelvin, X is permeability (P) or diffusion coefficient (D), and Xo is a constant representing the pre-exponential factor, Po or Do. Using the above relationship and Arrhenius plots of ln(Pw/l) or ln Dw as the y-axis versus 1/T as the x-axis, the lines are drawn using the best fit method (not displayed to avoid overcrowding of plots). The activation energy values (kJ/mol) for permeation of water (Epw), IPA (EpIPA), and EtOH (EpEtOH) as well as diffusion of water (EDw), IPA (EDIPA) and EtOH (EDEtOH) were calculated. The resulting data presented in Table 3a,b, respectively, showed lower barrier limit for water permeation but higher barrier limit for alcohol permeation, due to the high density of polar groups on the membrane surface. By increasing FGS loading, nanocomposite membranes showed lower water energy barrier and higher alcohol resistance. Thus, the nanocomposite membranes showed better diffusion resistance to alcohols in IPA−water mixture than in EtOH−water mixture. As explained earlier, this could be the result of the smaller size and higher polarity of EtOH, and the membranes showed lesser sorption and diffusion resistance values to these molecules. The heat of sorption (ΔHs) values were calculated from the Arrhenius permeability and diffusion energy data as46

5. CONCLUSIONS This work reports the fabrication and characterization of FGSincorporated chitosan nanocomposite membranes for testing their potential to alcohol dehydration via pervaporation technique. The H-bonding interaction between polar groups and electrostatic interactions between counterion charges of FGS and chitosan resulted in a good interface interaction as validated by FE-SEM and XRD measurements. Water contact angle data combined with optical profilometry have confirmed I



Article

Table 4. Comparison of Water Permeability and Selectivity Data of the Present Membranes with Literature Data for Binary Mixtures of EtOH−Water and IPA−Water membranea PI/SPI/Ultem (3 wt % SPI)−POSS modification free standing GO nano ZIF-7 (5 wt %)/chitosan m-toludine−H-treated polyamide carbon molecular sieve membrane NaAlg−mPTA-10 2.5 wt % of FGS loaded on chitosan ZIF-90 (30 wt %) P-84 RTO-treated CPA-5 ceramic polyamide (TFC) poly(ethylenimine/poly(acrylic acid) with TiO2 in UV irradiation free-standing GO membrane NaAlg−mPTA-10 2.5 wt % of FGS-loaded on chitosan

feed water (wt %) (a) EtOH−Water 15 15 10 10 10 10 10 (b) IPA−Water 15 10 15 5 30 10 10

total permeability (Barrer)

selectivity

ref

418 138 137 929 1390 130 84

107 227 2885 916 607 1920 1093

47 48 49 50 51 52 present work

46 2457 26 1027 176 377 103

9,256 290 2095 11573 1843 9256 7711

53 54 55 56 40 52 present work

a PI, polyimide; SPI, sulfated polyimide; POSS, polyhedral oligosilsequioxane; ZIF, zeolite imidazolate framework; mPTA, modified (by ammonium carbonate) phosphotungstic acid; CPA-5, high rejection reverse osmosis membrane; RTO, treatment room temperature and oven; TFC, thin film composite.

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that with increasing FGS loading, membrane affinity to water increases. Interaction parameter (χ) studies also show that with increasing FGS loading, a progressive increase in water affinity with a decrease in alcohol affinity was observed. However, diffusion resistance to alcohols increased with increasing FGS loading, due to the formation of tortuous pathways. At 2.5 wt % FGS loading as the optimum concentration, any further increase in FGS loading resulted in a decrease of membrane performance. Nanocomposite membranes showed higher values for the separation of IPA−water mixture than for EtOH−water mixture, due to the formation of stronger associated molecules with EtOH.

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ASSOCIATED CONTENT

S Supporting Information *

Detailed pervaporation readings for dehydration of IPA−water and EtOH−water mixtures. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*(R.V.A.) E-mail: [email protected]. Mail (present address): Centre for Emerging Technologies, Jain Global Campus, Jain University, Ramanagaram 562 112, India. Funding

Financial assistance from the Department of Atomic Energy (DAE), Board of Research in Nuclear Sciences (BRNS) (Grant 2013/34/4/BRNS), and Admar Mutt Education Foundation (AMEF), Bangalore, is gratefully acknowledged. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS S.P.D. is thankful to Manipal University for permitting him to publish this research as part of his Ph.D. program. REFERENCES

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