Liquid–Liquid Equilibrium in an Aqueous Two-Phase System of

Article pubs.acs.org/jced

Liquid−Liquid Equilibrium in an Aqueous Two-Phase System of Polyethylene Glycol 6000, Sodium Sulfate, Water, Glucose, and Penicillin-G: Experimental and Thermodynamic Modeling Aliakbar Roosta,* Fateme Jafari, and Jafar Javanmardi Chemical Engineering, Oil and Gas Department, Shiraz University of Technology, Shiraz 711, Iran ABSTRACT: Aqueous two-phase systems can be applied to a penicillin fermentation process to extract the produced penicillin from the fermentation broth to achieve a higher production rate. Such systems contain a polymer and a salt or two polymers. In this study, the liquid−liquid equilibrium of the quinary system of polyethylene glycol 6000, sodium sulfate, glucose, penicillin G, and water at 298.15 K is investigated. The experimental partition coefficients of glucose and penicillin-G showed the high efficiency of the present extractive fermentation processes. Using the liquid−liquid equilibrium data, the parameters of the NRTL activity model were calculated for the quinary system. The average absolute relative deviation of 0.677 % between experimental and calculated mass fractions shows that the NRTL activity model has a good ability to predict the equilibrium concentrations of the present system. In addition, a simple and accurate turbidimetric method for the measurement of penicillin G is proposed.

1. INTRODUCTION Antibiotics such as penicillin are usually produced by microorganisms in fermentation processes. All antibiotics inhibit the growth of microorganisms by inhibiting protein synthesis. In this case, extracting the product from the fermentation medium increases the rate and/or yield of production. Extractive fermentation of penicillin is of particular interest in such systems. The extraction of biological materials such as medicines, proteins, and amino acids, should be performed in ways that do not change the biological structure of the material.1 Water− organic solvent systems are conventionally used in the separation of the materials in the liquid phase. However, these systems are not suitable because of adverse environmental effects; because, many organic solvents are difficult to recycle and their disposition is very expensive. Although exposure of most toxic solvents can cause occupational diseases. Aqueous two-phase systems (ATPS) are good alternatives to the water− organic solvent extraction systems. ATPS were first realized by a Swedish biochemist, Beijerinck.2 This system consists of water and two hydrophilic components leading to the formation of two liquid phases in equilibrium. These two components often include two polymers or a polymer and a salt. Both phases are rich in water and rich in one of the two components.3 The presence of high percentage of water (70−85%) in each phase makes the aqueous two-phase systems more efficient than the water-organic solvent systems for the purification of biomaterials. Another advantage of ATPS is the lower interfacial tension between the two phases (0.0001−0.1 dyn.cm−1) than the water-organic solvent systems (1−20 dyn.cm−1) resulting in © XXXX American Chemical Society

better and faster mass transfer between the two phases. The presence of polymers in these systems makes the particle structure and biological activity more stable.4 Other benefits of these systems include low viscosity, the possibility to choose material for isolation, and low cost.5 Generally, systems containing polymer and salt are better than two-polymer systems, because in addition to reducing costs, they have other advantages such as lower viscosity and interfacial tension which are promising features for industrial use.6 Among polymers, polyethylene glycol is widely used in these systems. This polymer is relatively inexpensive compared to other polymers and has a relatively good solubility in water. In addition to conventional polymer−polymer and polymer−salt systems, aqueous two-phase salt−alcohol systems (alcohol with a low molecular mass, such as ethanol, propanol) and salt-ionic liquids have recently been used to isolate enzymes.7−9 Despite the use of salt−alcohol systems for the isolation of enzymes, these systems are not suitable for many proteins. Such systems are limited for the separation of low molecular weight materials.8 ATP systems have been used to recover and purify different antibiotics, such as penicillin or ciprofloxacin. Liu et al.10 studied the extraction of penicillin G by using ATP systems containing an ionic liquid ([Bmim]Cl) and a salt (NaH2PO4). In this study, the effects of feed composition on partition coefficient and extraction yield of penicillin G were studied. Received: August 24, 2015 Accepted: November 9, 2015

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DOI: 10.1021/acs.jced.5b00715 J. Chem. Eng. Data XXXX, XXX, XXX−XXX

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Pazooki et al.5 studied the partitioning behavior of penicillin G acylase by using polyethylene glycol 20000 or 35000 and KH2PO4 or sodium citrate (C6H5Na3O7.5H2O). In this study, the effect of temperature, molecular weight of polymers, and polymer and salt concentrations on the distribution coefficient of penicillin G were studied. Mokhtarani et al.11 used polyethylene glycol (PEG)-Na2SO4 ATPS for separation and purification of ciprofloxacin. The results show that the partition coefficient of ciprofloxacin is highly dependent on the concentration of Na2SO4. In addition to antibiotics, some researchers have purified proteins, peptides, and amino acids by using ATPS. Grobmann et al.12 employed ATP systems of PEG 6000-K2HPO4 and PEG 35000- K2HPO4 for purification of amino acids glycine, Lglutamic acid, L-phenylalanine and L-lysine at 293 K. Saravanan et al.13 measured partition coefficients of soluble proteins from tannery wastewater in PEG-MgSO4 ATPS at 303.15 K. They investigated the effect of PEG and salt concentration, pH and PEG molecular weight on the partition coefficient of proteins. Su and Chiang investigated the extraction of lysozyme from chicken egg whites using PEG 6000-Na2SO4 ATPS. Qinhua Peng et al.14 measured and correlated the partition coefficients of lysozyme, bovine serum albumin (BSA) and DNA in ATP systems containing PEG 4000-K2HPO4 and PEG 4000KH2PO4 at 298 K. Barros et al.15 studied the separation of proteases expressed by Penicillium restrictum from Brazilian Savanna by using a PEG-NaPA aqueous two-phase system. In this study, the effects of PEG, NaPA, and the composition of the fermentation broth on the partition coefficient of protease were investigated. In the process of fermentation of antibiotics, the presence of product in the system reduces the production rate and production yield, due to its inhibitory effect.16 To solve this problem, we need to remove all or part of the product from the fermentation broth. In this regard, the aqueous two-phase systems can be used as an effective method in the separation of the product from the fermentation broth. Another advantage of removing the product from the medium culture is the increase in substrate concentration. In this study, equilibrium concentrations of glucose, penicillin G, polyethylene glycol, and sodium sulfate were measured at 298.15 K for different feed compositions, in order to investigate the effect of feed composition on the equilibrium concentrations. Then, the system was modeled using an NRTL activity model based on mass fraction.17 The partition coefficients of penicillin G and glucose are calculated in order to investigate the capability of the aqueous two-phase system in separating penicillin-G from the medium culture.

and penicillin G, while the bottom phase is rich in sulfate and glucose. It is worth mentioning that the two phases should be slowly separated to avoid the turbulence and mixing of the two phases. After separation, the mass, volume, and concentrations of the two phases were measured. The mass of each phase was measured by using a Sartorius analytical balance (model TE124S) with 0.0001 g precision. The volume of each phase was measured by a pipet with 0.02 mL precision. The concentration of all components in both phases were measured based on colorimetric methods, using a Cary-50 UV spectrophotometer (Varian, USA). The concentrations of polyethylene glycol 6000, sodium sulfate, and glucose were measured based on the methods proposed by Francois,18 Rossum and Villarruz,19 and Albalasmeh et al.,20 respectively. In addition, a simple method is proposed for measuring penicillin G concentration. In this regard, a solution of penicillin G in distilled water was prepared. The absorbance scan of the solution showed a peak in absorption at 290 nm, while the blank was deionized water. Therefore, a standard curve for penicillin G was prepared at 290 nm that is shown in Figure 1.

Figure 1. OD at 290 nm as a function of penicillin G concentration.

Furthermore, the effect of all components present in the solution on the method was studied. The results showed that the components present in the solution have no absorbance at 290 nm; accordingly, they did not affect the accuracy of the method. To evaluate the accuracy of measurements, the mass balance of all components was verified using mass, volume, and concentrations measured for each phase. If the material balance for each component was not satisfied with an accuracy of 0.5 %, the experiment was repeated. The experimental data for the quinary aqueous two-phase system at 298.15 K, related to 20 experiments are listed in Table 1, as mass percentage. The experiments were designed to cover a wide range of conditions that may occur in the penicillin fermentation process.

2. EXPERIMENTAL SECTION 2.1. Material. All reagents used were of analytical grade and purchased from Merck Co, with stated purities of > 99.9 % mass. In addition, double distilled water was used in all experiments. 2.2. Procedure. In this study, a quinary mixture consisting of polyethylene glycol 6000, sodium sulfate, water, glucose, and penicillin G was prepared using a Sartorius analytical balance (model TE124S) with 0.0001 g precision. After mixing well, the solution was transferred into a decanter for a period of 12 h at 298.15 K, which is the optimum temperature of the penicillin fermentation process. After this time, the interface of the two phases is clearly distinct, resulting in a more accurate separation of the two phases. The top phase is rich in polyethylene glycol

3. MODELING The ultimate goal of modeling a system is to get a correlation to describe the behavior of the system at different conditions. The selection of a suitable model depends on several factors, including the available information on the system, the complexity of the system, required accuracy, and time limitation. Two potential approaches can be employed for estimating the liquid−liquid equilibrium of ATP systems, artificial neural networks and thermodynamic modeling. Artificial neural networks have been successfully used to predict different thermodynamic properties.21−24 Some disadvantages of artificial neural networks method include the B

DOI: 10.1021/acs.jced.5b00715 J. Chem. Eng. Data XXXX, XXX, XXX−XXX

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Table 1. Experimental LLE Data (Weight Percent) For the Quinary Aqueous Two Phase System: Polyethylene Glycol 6000 (1), Sodium Sulfate (2) + Glucose (3) + Penicillin G (4) and Water at Temperature T = 298.15 K and Pressure p = 0.1 MPaa no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 a

feed

top phase

partition coefficients

bottom phase

100w1

100w2

100w3

100w4

100w1

100w2

100w3

100w4

100w1

100w2

100w3

100w4

K3

K4

22.428 13.876 17.239 9.929 13.870 12.301 10.517 10.469 12.176 9.645 12.789 11.807 12.748 12.510 12.077 10.164 10.537 11.431 11.792 11.857

7.226 9.903 7.782 10.037 7.906 8.063 8.231 7.851 6.895 7.488 6.139 6.263 5.759 5.684 5.699 6.210 6.015 5.817 5.628 5.535

0.322 0.521 0.719 0.632 0.915 0.586 1.118 1.303 1.511 1.733 1.915 2.084 2.297 2.490 2.656 2.835 2.923 1.444 1.801 2.385

0.031 0.035 0.041 0.045 0.051 0.039 0.054 0.062 0.064 0.070 0.074 0.080 0.083 0.089 0.102 0.107 0.024 0.038 0.093 0.076

36.857 34.693 32.680 30.905 29.150 27.680 26.084 24.672 23.343 22.088 20.921 19.820 18.775 17.790 16.861 15.965 15.163 14.643 13.846 13.068

1.841 2.040 2.237 2.440 2.634 2.843 3.029 3.224 3.416 3.607 3.800 3.991 4.181 4.370 4.560 4.743 4.941 5.225 5.402 5.568

0.239 0.371 0.587 0.506 0.806 0.528 1.038 1.247 1.486 1.736 1.939 2.132 2.361 2.571 2.756 3.044 3.139 1.506 1.883 2.506

0.042 0.059 0.059 0.077 0.072 0.055 0.077 0.084 0.079 0.089 0.084 0.091 0.091 0.096 0.111 0.123 0.027 0.041 0.098 0.079

0.646 0.733 0.904 1.154 1.467 1.852 2.276 2.755 3.278 3.842 4.442 5.077 5.740 6.431 7.150 7.892 8.669 9.606 10.397 11.183

16.125 15.061 14.092 13.277 12.469 11.816 11.107 10.493 9.921 9.390 8.896 8.437 8.002 7.595 7.214 6.856 6.527 6.307 5.984 5.670

0.472 0.629 0.890 0.694 1.031 0.640 1.181 1.357 1.571 1.757 1.952 2.097 2.304 2.482 2.632 2.782 2.866 1.435 1.779 2.333

0.012 0.019 0.021 0.031 0.032 0.027 0.041 0.049 0.051 0.060 0.062 0.070 0.073 0.079 0.092 0.099 0.022 0.036 0.089 0.074

0.505 0.589 0.660 0.728 0.782 0.826 0.879 0.919 0.946 0.988 0.994 1.016 1.025 1.036 1.047 1.094 1.096 1.049 1.059 1.074

3.469 3.108 2.775 2.506 2.248 2.022 1.861 1.706 1.565 1.480 1.368 1.309 1.255 1.224 1.206 1.235 1.210 1.128 1.100 1.068

Expanded uncertainties Uc with a level of confidence of 95 % are Uc(w) = ± 0.003, Uc(T) = ± 0.1.

need for large amounts of experimental data, a low rate of convergence, and the model parameters may be not the optimal parameters, because they are determined through trial and error.24 However, the main advantage is that there is no need for theoretical knowledge of the system.22 The main advantage of thermodynamic models is the need for less experimental data in determining the model parameters, and also the simple thermodynamic models can describe the complex behavior of the system. For thermodynamic modeling of liquid−liquid equilibrium systems, activity coefficients are needed. Different activity models are available in the literature for calculating such coefficients. According to the literature, the NRTL activity coefficient model based on the mass fraction is one of the most successful models for aqueous two-phase systems.25−28 In this study, the NRTL activity coefficient model based on mass fraction was used for modeling of liquid−liquid equilibrium aqueous two phase systems.17 Because of the high molecular weight and consequently the lower mole fraction of polymers, the NRTL model based on mass fraction is used in this study. The equations of the NRTL activity coefficient model based on the mass fraction for multicomponent systems are shown in eqs 1 to 4. N

ln γi =

∑j=1

AARD% =

Top Phase Bottom Phase (γi w w) = (γi w w) i i

100 2D(N − 1)

D N−1

⎡ |(w te − w tc)| ij ij

∑∑⎢ i=1 j=1

⎢⎣

wijte

(5)

where D denotes the number of LLE data sets; te = top, experiment; tc = top, calculated; be = bottom, experiment; and bc = bottom calculated values.

N

αij = αji

(4)

|(wijbe − wijbc)| ⎤ ⎥ + ⎥⎦ wijbe

Mj

and

⎛ wj ⎞ ⎜ ⎟ ⎝ Mj ⎠

where N, M, w, γ, and γw are the number of components, molar mass, weight fraction, activity coefficient, and activity coefficient based on the mass fraction, respectively. In this model, there are three adjustable parameters of τij, τji, and αij, where τij and τji are related to the characteristic energy of interaction between the molecules of type i and j, and αij is related to the nonrandomness of the mixture. The parameters are calculated by minimizing the average absolute relative deviation (AARD %) between the calculated and the experimental mass fractions, as shown in eq 5. In this regard, the parameters are calculated using the Genetic Algorithm (as an optimization method), combined with the flash calculations based on the Rachford− Rice equations.29

N Gjiwj ∑j=1 M j

Gij = exp( −αijτij)

N

Mi ∑ j

τjiGjiwj

⎡ ⎛ N τkjGkjwk ⎞⎤ ∑k = 1 M ⎟⎥ wG ⎢ ⎜ j ij k + ∑⎢ τij − Gkjwk ⎟⎥ N Gkjwk ⎜ N ⎜ ∑k = 1 M ⎟⎠⎥ j = 1 ⎢ Mj ∑k = 1 Mk ⎝ ⎣ ⎦ k

γi

γi w =

4. RESULTS AND DISCUSSION The measured experimental data, including the mass percentages in the feed and the two equilibrium phases of liquid−liquid ATP system alongside penicillin G, and glucose Bottom distribution coefficients (Ki = ((wTop ))) for 20 data i )/(wi sets are reported in Table 1. As can be seen, the distribution

(1) (2) (3) C

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of glucose and penicillin G approach unity as the PEG and salt compositions in the feed are decreased, as seen in Table 1. Using experimental data for the liquid−liquid equilibrium system, the binary molecular interaction parameters are estimated for the NRTL activity model. All the nonrandomness parameters are considered to be 0.3. Due to the small mass fractions of glucose and penicillin G, at first, the parameters of the NRTL model for a ternary system of water, polyethylene glycol, and sodium sulfate were obtained. Then, the parameters are used as the initial guess to calculate the parameters of the quinary system. The estimated parameters of the NRTL activity coefficient model based on the mass fraction are listed in Table 2. The NRTL activity model with the estimated parameters was applied to calculate the LLE data. The accuracy of the model is evaluated by comparison between estimated and experimental mass fractions in top and bottom phases. In this regards, the AARD % (eq 5), the root-mean-square (RMS) error (eq 6), and the maximum absolute deviations (eq 7) of the model are calculated for the 20 data sets and are reported in Table 3.

Table 2. Estimated NRTL Parameters pair i−j

τij

τji

αij = αji

PEG 6000−Na2SO4 PEG 6000−water PEG 6000−glucose PEG 6000−penicillin G Na2SO4−water Na2SO4−glucose Na2SO4−penicillin G water−glucose water−penicillin G glucose−penicillin G

5.0169 −6.5107 8.1566 11.8203 −4.0428 −6.4474 1.8685 −6.0379 −2.0707 −7.7455

1.6822 33.9208 0.527 −0.9209 8.7771 5.6918 2.5514 9.9466 −3.0749 1.7217

0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

Table 3. Comparison between Estimated Results and Experimental Data top phase

bottom phase

no.

AARD %

Δwmax

AARD %

Δwmax

100RMS

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Overall

1.0235 1.0641 1.0770 1.0718 1.0747 1.0842 1.0678 1.0522 1.0219 0.9806 0.9776 0.9528 0.9318 0.9024 0.8708 0.7509 0.7397 1.2020 1.1920 1.1376 1.0087

0.0151 0.0148 0.0141 0.0132 0.0125 0.0120 0.0111 0.0103 0.0095 0.0086 0.0081 0.0074 0.0068 0.0062 0.0056 0.0041 0.0038 0.0072 0.0068 0.0061

0.0742 0.0643 0.0541 0.1419 0.1162 0.1280 0.1407 0.1342 0.2591 0.2622 0.2787 0.2671 0.2967 0.3103 0.3118 0.2505 0.2859 0.2410 0.2645 0.2728 0.3447

0.0021 0.0010 0.0012 0.0008 0.0006 0.0006 0.0006 0.0006 0.0010 0.0010 0.0010 0.0010 0.0011 0.0011 0.0011 0.0011 0.0011 0.0011 0.0013 0.0012

0.5415 0.5261 0.5014 0.4715 0.4462 0.4275 0.3981 0.3718 0.3442 0.3146 0.2988 0.2788 0.2620 0.2448 0.2288 0.2077 0.1987 0.2696 0.2580 0.2384 0.3442

D

RMS =

N−1

∑i ∑ j

((wij te − wij tc)2 + (wij be − wij bc)2 ) 2DN (6)

where te = top, experiment; tc = top, calculated; be = bottom, experiment; and bc = bottom calculated values. Δwmax = max{|(wiP,exp − wiP,calc)|}

(7)

where exp denotes experimental value and calc denotes calculated value. As can be seen in Table 3, all the deviations in the model show good agreement between the model and experimental data. Overall AARD % and overall RMS for the 20 data sets are 0.677 % and 0.3442, respectively. Furthermore, Figure 2 compares the predicted and experimental values of mass fractions for both the top and bottom phases. In addition, some important statistics error such as correlation coefficient (R2), mean squared error (MSE), minimum absolute percentage error (MIAPE), and maximum absolute percentage error (MAAPE) are used to evaluate the performance of the model for each component separately.30,31 The results listed in Table 4 show the excellent predictive performance of the model for both phases. The NRTL model is used to investigate the effect of feed composition on the distribution coefficients of penicillin G and

coefficient of penicillin G in all series is greater than one. In fact, penicillin G is accumulated in the upper phase that is rich in PEG; while, glucose accumulated in the lower phase that is the fermentation broth. However, the distribution coefficients

Figure 2. Experimental vs predicted phase compositions (mass fraction): (a) bottom phase; (b) top phase. D

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Table 4. Statistical Analysis of NRTL Model for Prediction of Equilibrium Mass Fractions Two Phases top phase

a

bottom phase

parameters

PEG

Na2SO4

glucose

penicillin G

PEG

Na2SO4

glucose

penicillin G

R2a MAAPEb MIAPEc RMSEd

0.9993 4.9424 2.4751 0.0097

0.9933 5.6527 3.5175 0.0017

0.9977 9.550 0.3494 0.0014

0.9927 5.8153 0.1505 0.00001

0.9995 12.096 0.1050 0.0007

0.9970 2.3327 0.5659 0.0017

0.9913 6.1350 0.2597 0.0008

0.9977 4.9677 0.0033 0.00001

Correlation coefficient: D

R2 = 1 − b

∑i = 1 (wi ,exp − wi ,calc)2 D

∑i = 1 (wi ,calc − average(wi ,calc))2

Maximum absolute percentage error:

⎛ |wi ,exp − wi ,calc| ⎞ ⎟⎟100 MAAPE = max⎜⎜ wi ,exp ⎝ ⎠ c

Minimum absolute percentage error:

⎛ |wi ,exp − wi ,calc| ⎞ ⎟⎟100 MIAPE = min⎜⎜ wi ,exp ⎝ ⎠ d

Root mean square error:

1 D

RMSE =

D

∑ (wi ,exp − wi ,calc)2 i=1

seen in this table, an increase of sulfate concentration in the feed leads to an increase of the distribution coefficient of penicillin G and decrease of the distribution coefficient of glucose. In other words, the selectivity ratio of penicillin G to glucose increases with concentrations of PEG and sulfate. According to the results, the present model can accurately predict the LLE data of interest. The results of the model can be used in the separation process of penicillin G, or in the process of extractive fermentation of penicillin G in order to increase the production rate and production yield.

Table 5. Partition Coefficients of Glucose (K3) and Penicillin G (K4) at Various Amounts of PEG and Constant Amounts of Na2SO4 (8.2 g), Water (80.1 g), Glucose (1.1 g), and Penicillin G (0.05 g) PEG in feed (g)

K3

K4

10.5 15.5 20.5 25.5 30.5 35.5

0.821 0.822 0.822 0.823 0.826 0.826

1.924 2.203 2.538 2.931 3.381 3.901

5. CONCLUSIONS In this study, experimental data for a liquid−liquid equilibrium ATP system, including the mass fractions of PEG, sodium sulfate, glucose, and penicillin G were measured at 298.15 K. Then, using the experimental data, the parameters of the NRTL model based on the mass fraction were estimated. By comparing the experimental data and the mass fractions calculated by the model, the model accuracy was verified. The overall RMS and the average absolute relative deviation are 0.3442 % and 0.677 %, respectively. The results show that the NRTL activity coefficient model has a good ability to predict the liquid−liquid equilibrium for this system. The distribution ratio of the penicillin G in this system gives an incentive to investigate the production of penicillin G in an extractive fermentation process.

Table 6. Partition Coefficients of Glucose (K3) and Penicillin G (K4) at Various Amounts of Na2SO4 and Constant Amounts of PEG (10.5 g), Water (80.1 g), Glucose (1.1 g) and Penicillin G (0.05 g) Na2SO4 in Feed (g)

K3

K4

8.2 11.2 14.2 17.2 20.2 23.2

0.821 0.819 0.814 0.582 0.475 0.402

1.924 2.553 3.217 3.316 3.235 3.078

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glucose, as presented in Tables 5 and 6. As seen in Table 5, increasing the amount of PEG in the feed leads to increase of the distribution coefficient of penicillin G, while it has no significant effect on the distribution coefficient of glucose. On the other words, with increasing PEG concentration in the feed, the concentration of penicillin G in the top phase increases. Table 6 represents the effect of sulfate concentration in the feed on the distribution coefficients of penicillin G and glucose. As

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: +98-7137354520. Fax: +98-7137354520. Notes

The authors declare no competing financial interest. E

DOI: 10.1021/acs.jced.5b00715 J. Chem. Eng. Data XXXX, XXX, XXX−XXX

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ACKNOWLEDGMENTS Thanks to Dr. Reza Khalifeh, Dr. Abdo-Reza Nekoei and Ms. Mahboubeh Sadeghi from the Department of Chemistry of Shiraz University of Technology for their cooperation in advancing laboratory objectives.

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DOI: 10.1021/acs.jced.5b00715 J. Chem. Eng. Data XXXX, XXX, XXX−XXX