Phase Equilibrium Involving Xylose, Water, and Ethylene Glycol or 1,2

Article pubs.acs.org/jced

Cite This: J. Chem. Eng. Data XXXX, XXX, XXX−XXX

Phase Equilibrium Involving Xylose, Water, and Ethylene Glycol or 1,2-Propylene Glycol at Different Temperatures Patrícia G. Machado,† Alessandro C. Galvão,*,† Weber S. Robazza,† Pedro F. Arce,‡ and Layze V. Barbosa† †

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Laboratory ApTherApplied Thermophysics, Department of Food and Chemical Engineering, Santa Catarina State UniversityUDESC, Pinhalzinho, Santa Catarina 89870-000, Brazil ‡ Engineering School of Lorena, Department of Chemical Engineering, University of São PauloUSP, Lorena, São Paulo 12602-810, Brazil S Supporting Information *

ABSTRACT: Xylose is an important molecule that can be used in the production of chemicals such as ethylene glycol and 1,2-propylene glycol. Experimental and theoretical information are fundamental for the development of conversion and separation processes. For the supply of solubility data, this work sought to study solid−liquid equilibrium of xylose in binary liquid solutions formed by water/ethylene glycol and water/1,2-propylene glycol in the temperature range between 293.15 and 323.15 K, covering the entire molar composition range of the binary solution. The Jouyban−Acree and NRTL models were applied in the correlation of experimental data, and the UNIFAC and ASOG models were applied in the prediction of experimental data. Xylose showed solubility in the pure components following the order water > ethylene glycol > 1,2-propylene glycol. Intermediate values of solubility are related to intermediate concentrations of the binary liquid mixture. The mathematical models were successfully applied, and new parameters of UNIFAC and ASOG are presented.

1. INTRODUCTION Biomass is an abundant and organic material of vegetable origin. The hemicelluloses present in this rich carbon source are made of different saccharides1,2 offering a great potential for sustainable applications. The use of biomass-derived substrates conducted in a biorefinery3 can lead to the production of energy and high-value-added products4 with lower carbon emissions.5 Xylose is a monosaccharide that can be produced in a biorefinery through a process of hydrolysis. This pentose sugar widely used in the food industry is also a substrate for the production of xylitol. Both xylose and xylitol have industrial applications in the development of resins and polymers.6,7 Moreover, xylitol is a chemical building block with potential to be used in the production of ethylene glycol and propylene glycol.8 Chemical substances such as ethylene glycol and propylene glycol participate in different features of daily life working as key compounds for the production of different materials. On the other hand, these compounds are currently produced from a nonrenewable source of carbon9 driving researchers to develop alternative routes for their production.10 The exploitation of lignocellulose-derived sugars such as xylose enabling its transformation into chemicals faces some challenges including high yields in the conversion and separation steps. The scientific nature of some demands is answered by experimental studies. In general, the development of an efficient process depends on the amount of a component dissolved in a liquid, conditions of temperature, pressure, and the distribution of the components when phases coexist under equilibrium.11 © XXXX American Chemical Society

Studies of solid−liquid equilibrium have been performed by different research groups to generate qualitative and quantitative information. However, studies on the solubility of xylose are still scarce in the literature. Table 1 presents a review Table 1. Published Studies on the Solubility of Xylose solventa

T/K

method

(a); (c); (f)

283−333

analytical

(a); (b); (e) (d) (a); (f)

295−353 313−333 278−298

synthetic analytical analytical

(a); (f) (a) (a)

298 298−348 293−303

analytical synthetic analytical

model UNIFAC, ASOG, UNIQUAC, NRTL, Nývlt, λh UNIQUAC COSMO-RS UNIFAC, UNIQUAC UNIQUAC UNIQUAC

reference 12 13 14 15 16 17 18

a

(a) water, (b) methanol, (c) ethanol, (d) ionic liquids, (e) water + methanol, (f) water + ethanol.

of the published studies, in which the experimental temperature range, the method for the quantification of the liquid composition, the use of pure solvents or binary solutions, and the modeling applied can be seen. Special Issue: Latin America Received: November 26, 2018 Accepted: March 19, 2019

A

DOI: 10.1021/acs.jced.8b01128 J. Chem. Eng. Data XXXX, XXX, XXX−XXX

Journal of Chemical & Engineering Data

Article

The experimental methodology was tested by checking the solubility of adipic acid in water at different temperatures. Table 3 compares the solubility of adipic acid in water with

For an illustration of the solubility of xylose, the published studies indicate a high solubility in water; for example, at 298.15 K the average solubility is approximately 57 wt %. The monosaccharide is also soluble in alcohol although the solubility in ethanol is 0.76 wt % at 298.15 K, and the solubility in methanol is 5.12 wt % at 315.55 K. As shown, the analytical method has been applied more frequently than the synthetic method. The measurements have been performed by gravimetric, refractometric, spectroscopic, or chromatographic techniques. The application of different mathematical models has been tested, and special attention has been given to the models UNIFAC and UNIQUAC. Because of the lack of experimental studies on the phase equilibrium concerning xylose and glycols, this study aimed to evaluate the solubility of xylose in binary liquid solutions formed by water/ethylene glycol and water/1,2-propylene glycol, covering the entire molar composition range at different temperatures. During the experiment, the refractometric method was employed to quantify the composition of the samples taken from the liquid phase in equilibrium with the solid phase. Moreover, the experimental results of solubility were correlated and predicted by different models.

Table 3. Solubility Expressed as Mole Fraction xs of Adipic Acid in Water as a Function of Temperature T at 0.1 MPa T/K 293.15 298.15 303.15 308.15 313.15 318.15 323.15

water

nlit D

ρexp/g cm−3

1.33299

a

0.9971

ethylene glycol

1.43159

1,2-propylene glycol

1.43262

1.3330 ; 1.33299e 1.4319a; 1.4315b 1.43247c; 1.43297d

1.1097 1.0327

0.0024 , 0.0031a, 0.0038a, 0.0048a, 0.0062a, 0.0084a, 0.0108a,

b

0.0023 , 0.0029b 0.0038b, 0.0050e 0.0063b, 0.0075e 0.0110b,

0.0023c 0.0038c, 0.0041d, 0.0043e 0.0063c, 0.0069d, 0.0061e 0.0106d, 0.0092e

Apelblat and Manzurola.31 bGaivoronskii and Granzhan.32 cDeng et al.33 dShen et al.34 eWang et al.;35 Standard uncertainties u are u(T) = 0.5 K, u(p) = 0.01 MPa, ur(xs) = 0.02.

published values. The propagation method was employed to estimate the experimental uncertainty of the solubility data expressed as mass fraction. Further information about the experimental technique applied to determine the liquid composition in equilibrium with the solid phase can be found in a work previously published.30

3. MODELING The importance of experimental studies for the improvement of chemical processes is undeniable. Furthermore, comparison of predicted results with experimental data is fundamental for the validation of solution theories and development of mathematical models.36 This work analyses the capability of Jouyban− Acree and NRTL models on correlating the experimental data and UNIFAC and ASOG models on predicting the solubility data. The Jouyban−Acree model37,38 is represented by eq 1, in which Ji are the adjustable coefficients, T represents the experimental temperature, x01 and x02 correspond to the solubility in pure components, x1 and x2 represent the mole fractions in the binary liquid mixture, and xs is the solubility expressed as mole fraction. Considering the adjustable parameters as a function of temperature, the model assumes a format of a linear system, and its resolution was conducted by least-squares minimization.

Table 2. Comparison of Experimental Data and Literature Values at 0.1 MPa: Refractive Index (nD) 293.15 K and Specific Mass (ρ) at 298.15 K nexp D

0.0023 0.0030 0.0037 0.0050 0.0066 0.0091 0.0117

a

a

2. EXPERIMENTAL SECTION In the experiments, the solvents were used without any further purification. The solids were previously dried in an oven for 1 h, at a temperature of 353 K, and then placed in a desiccator until the beginning of the experiments. Table S1 (Supporting Information) shows the origin and the purity of the reagents used. The quality of the solvents was attested by performing the refraction index analyses in a digital benchtop refractometer, and by evaluating the specific mass performed in a pycnometer previously calibrated with water. The results of the assays and the values available in the literature are presented in Table 2. The data confirms the quality of the purchased reagents.

component

xlit s

xs

ρlit/g cm−3 0.99702f; 0.9982g 1.1084h; 1.1096i 1.03259j; 1.03286k

2

ln xs = x1 ln x10 + x 2 ln x 20 + x1x 2 ∑ i=0

Ji T

(x1 − x 2)i

(1)

On the basis of the concepts of local composition and nonrandomness, the correlative NRTL model39 has two sets of adjustable parameters. The two dimensionless parameters τij and τji reflect mainly the energy change due to the interaction of different molecules, and these energy differences can be considered as dependent on the temperature. The second set of parameters αij and αji (usually αij is considered as equal to αji) can also be dependent on temperature, and their values provide the idea of ordered structures. The predictive UNIFAC40,41 model takes into account a group contribution concept in which the energy associated with molecular interactions is simplified as being the sum of the interaction energies between groups. Modifications of the original version deal with the use of a different combinatorial term, and the interaction parameters are assumed as temperature-dependent. The modified UNIFAC (Dortmund)42 used in this work considers that the dependency of the interaction

a

Tsierkezos and Molinou.19 bJiménez et al.20 cZivkovic et al.21 d Fontao and Iglesias.22 eCRC Handbook.23 fHerráez and Belda.24 g Morrone and Francesconi.25 hAlbuquerque et al.26 iLi et al.27 jBajic et al.28 kLi et al.;29 Standard uncertainties u are u(T) = 0.5 K, u(p) = 0.01 MPa, u(nD) = 6 × 10−3, ur(ρ) = 0.0005.

The experiment was conducted in jacketed glass cells coupled to a thermostatic bath. Solid and liquid substances were added inside the equilibrium cells. The mixtures were agitated for a period of 3 h to achieve saturation of the solvent. The mixtures remained without agitation for a period of 24 h so the phase equilibrium could occur. Three samples were removed for each cell in equilibrium. The aliquots were evaluated through refractometry in a digital benchtop refractometer. The values were converted into solubility results, using previously built calibration curves. B



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linked in a linear chain. Some parameters were predicted in this work, and others were taken from the literature. The interaction parameters calculated in this work were obtained by using the maximum likelihood method for the minimization of the objective function represented by eq 2 wherein the subscripts i and j refer to the number of components of the solution and the number of solubility experimental data respectively, and γi is the activity coefficient of component i. The superscripts EXP and CALC refer to the experimental data and solubility calculated by the models. OF =

∑ ∑ (ln γi EXP − ln γiCALC)2j i

(2)

j

The ability of the applied models to correlate or predict the experimental results was evaluated by the average relative deviation (ARD%) described by eq 3 wherein Np is the number of experimental points, xEXP is the experimental solubility, s and xCALC represents the values calculated by the models. s

Figure 1. Solubility expressed as mass fraction ws of xylose in water as a function of temperature: ●, this work; ▲, Gong et al.;15 ○, ́ et al.;12 △, Gray et al.;18 and ◇, Jónsdóttir et al.;17 □, Martinez 16 Gabas et al.

parameters with temperature follows a second-order polynomial. Some parameters of the model were predicted in this work, and others were taken from the literature. As in the UNIFAC model, the predictive ASOG43,44 model uses the group contribution concept, and the activity coefficient takes into account a combinatorial and a residual contribution. The method is based on the Flory−Huggins formalism for the combinatorial contribution and the Wilson equation for the residual contribution. For this work the following groups were selected: CPOH, cyclic polyalcohol (OH groups linked to consecutive carbon atoms); POH, OH groups linked to consecutive carbon atom in a linear chain; O, O group linked to carbon atoms in a cyclic polyalcohol; CH2, CH2 group

ARD% =

100 Np

Np

∑

xsEXP − xsCALC

i=1

xsEXP

(3)

The thermodynamic approach applied to model solid−liquid equilibrium was published previously,45 and a further discussion about the semiempirical models employed in this work and their equations are available in the literature.36,46 In accordance with the thermodynamic formalism, the solubility of a solid in a liquid is influenced by the activity coefficient of the solid solubilized γs and its ideal solubility. The calculations of ideal solubility depend on the fusion enthalpy ΔfusHs and the fusion temperature Tms of the solid. These values taken from the

Table 4. Solubilitya Expressed as Mass Fraction ws as a Function of Temperature and Mole Fraction of Water x2 in the Binary Liquid Mixture at 0.1 MPa ws x2

293.15 K

298.15 K

1.0000 0.8993 0.7988 0.6987 0.5999 0.5000 0.4001 0.3016 0.2044 0.1024 0.0000

0.5317 0.4344 0.3518 0.2905 0.2472 0.2062 0.1680 0.1445 0.1224 0.1023 0.0840

0.5545 0.4665 0.3908 0.3312 0.2813 0.2410 0.2057 0.1820 0.1598 0.1390 0.1126

1.0000 0.8998 0.7996 0.6998 0.6006 0.5005 0.4004 0.3024 0.2022 0.1016 0.0000

0.5317 0.3987 0.2990 0.2237 0.1743 0.1350 0.1001 0.0686 0.0523 0.0418 0.0337

0.5545 0.4336 0.3387 0.2675 0.2045 0.1628 0.1235 0.0959 0.0783 0.0643 0.0467

303.15 K

308.15 K

Xylose (s) + Water (2) + Ethylene Glycol (3) 0.5773 0.6031 0.4921 0.5193 0.4186 0.4485 0.3583 0.3890 0.3067 0.3355 0.2649 0.2919 0.2300 0.2561 0.2048 0.2309 0.1814 0.2062 0.1608 0.1846 0.1375 0.1630 Xylose (s) + Water (2) + 1,2-Propylene Glycol (3) 0.5773 0.6031 0.4622 0.4896 0.3688 0.3978 0.2980 0.3270 0.2315 0.2624 0.1850 0.2113 0.1409 0.1601 0.1115 0.1275 0.0900 0.1027 0.0741 0.0837 0.0550 0.0660

313.15 K

318.15 K

323.15 K

0.6359 0.5461 0.4787 0.4199 0.3661 0.3236 0.2858 0.2562 0.2307 0.2084 0.1871

0.6566 0.5733 0.5093 0.4521 0.3977 0.3513 0.3133 0.2818 0.2564 0.2316 0.2107

0.6714 0.6019 0.5404 0.4854 0.4297 0.3852 0.3478 0.3138 0.2897 0.2649 0.2398

0.6359 0.5186 0.4287 0.3605 0.2939 0.2381 0.1830 0.1454 0.1189 0.0966 0.0825

0.6566 0.5455 0.4606 0.3942 0.3243 0.2654 0.2043 0.1643 0.1359 0.1121 0.0952

0.6714 0.5751 0.4929 0.4278 0.3637 0.3000 0.2350 0.1929 0.1589 0.1319 0.1145

a

Standard uncertainties u are u(T) = 0.5 K, u(p) = 0.01 MPa, u(x2) = 0.02, ur(ws) = 0.03 C



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Figure 2. Solubility expressed as mole fraction xs of xylose as a function of mole fraction of water in the binary mixture (a) water (2) + ethylene glycol (3), and (b) water (2) + 1,2-propylene glycol (3) at different temperatures: ●, 293.15 K; ■, 298.15 K; ▲, 303.15 K; ◆, 308.15 K; ○, 313.15 K; □, 318.15 K; and ◇, 323.15 K. Solid lines represent the Jouyban−Acree model.

Table 5. Average Relative Deviation ARD% for the Fitting of the Activity Coefficient Models xylose (s) + water (2) + ethylene glycol (3)

xylose(s)+water(2)+1,2-propylene glycol (3)

T/K

ASOG

NRTL

UNIFAC

ASOG

NRTL

UNIFAC

293.15 298.15 303.15 308.15 313.15 318.15 323.15

2.21 1.23 2.31 0.98 0.55 0.57 0.29

2.18 0.93 0.90 0.92 0.67 0.56 0.60

5.90 3.79 2.83 3.77 1.29 1.58 1.07

2.68 1.82 1.43 1.58 1.35 1.23 1.13

4.02 2.49 2.58 2.23 2.02 1.80 1.54

6.30 3.93 3.48 3.08 2.66 1.98 2.16

Table 6. New UNIFAC (Dortmund) Model Parameters

ΔfusHs jij 1 1 zy jj − zzzz − ln γs j R j Tms Tz k {

Rk

Qk

CPOH POH H2O −O−

6.843 5.412 0.921 0.247

6.955 5.874 1.412 0.245

function of temperature and mole fraction of the solution free of xylose, is presented in Table 4, and Figure 2 presents the behavior of solubility data expressed as mole fraction. As it is observed, there is an increase in the solubility with increasing temperature for all concentrations of the binary solutions and for the temperatures investigated. The dependence of solubility with temperature can be explained by the endothermic fusion of xylose, and for this reason an increase of solubility is expected for higher temperatures.48 The dissolution process of a solid in a liquid depends on the affinity between solid and solvent. A polar solid such as xylose tends to have greater solubility in polar solvents. The polarity of a liquid is best represented by its dielectric constant, and therefore it is expected that a liquid with higher dielectric constant shows a better capability on dissolving a polar solid. The solvents applied in this work have a dielectric constant following the order water > ethylene glycol > 1,2-propylene glycol. As a result, the solubility of xylose is greater in water followed by ethylene glycol and 1,2-propylene glycol, respectively.

literature47 are, respectively, 31.7 kJ mol−1 and 420.0 K. The dependence of solubility with temperature, melting properties of the pure solid, and activity coefficient of the solubilized solid is described by eq 4, and its application considers that the solid phase is formed just by the solid undissolved. ln xs =

group k

(4)

4. RESULTS AND DISCUSSION The experimental solubility of xylose expressed as mass fraction (experimental uncertainty ±0.0038) in pure water is presented in Figure 1. The solubility in a ternary mixture, as a

Figure 3. Behavior of the models ···, NRTL; , ASOG; and ---, UNIFAC against experimental points for the mixtures involving (a) water (2) + ethylene glycol (3) and (b) water (2) + 1,2-propylene glycol (3): ●, 293.15 K; ■, 298.15 K; ▲, 303.15 K; ◆, 308.15 K; ○, 313.15 K; □, 318.15 K; ◇, 323.15 K. D



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Table 7. New UNIFAC (Dortmund) Group Interaction Parameters m

n

amn/K

bmn

cmn/K−1

anm/K

bnm

cnm/K−1

CPOH CPOH CPOH CPOH H2O

H2O POH -OCH2 POH

−185.211 −954.214 1231.208 519.912 −528.741

−15.745 −11.740 −6.125 −3.414 2.411

0.028 0.069 0.003 0.010 0.008

−1129.307 108.674 −2541.512 740.622 156.374

−5.458 8.745 −1.422 1.741 −1.412

−0.154 0.041 0.007 0.008 0.001

Table 10. Nonrandomness Parameter, α, for the NRTL Model

Table 8. ASOG Group Interaction Parameters k

l

mk,l

ml,k

nk,l

nl,k

CPOH CPOH CPOH CPOH H2O H2O H2O CH2

H2O POH O CH2 POH O CH2 O

1.263 2.107 3.672 1.989 1.521 −21.632 0.525 −0.126

0.283 −0.096 2.593 0.786 −6.013 3.125 −0.285 −0.562

1.463 2.983 −6.322 2.241 −275.847 −1.256 −2298.365 33.412

2.170 0.708 −11.466 −0.008 1593.154 −2.458 −281.412 163.412

mixture

α

xylose (s) + water (2) + ethylene glycol (3) xylose (s) + water (2) + propylene glycol (3)

0.3828−30.3742/T 0.4904−61.7458/T

parameters between CPOH and the other groups (H2O, POH, O, and CH2) were obtained from the literature. The interaction parameters between the groups H2O−O, H2O−CH2, and CH2−O were calculated, and all the group interaction parameters of ASOG are presented in Table 8. Table 9 shows the temperature dependence of the binary energy interaction parameter of the NRTL model, while Table 10 shows the temperature dependence of the nonrandomness parameter for the ternary solutions under study. The compositions of the liquid phase determined by the NRTL, ASOG, and UNIFAC models are presented in Table S3 (Supporting Information).

Regarding the solubility of xylose in binary mixtures, intermediate values were observed in comparison with solubility in pure solvents. The addition of an antisolvent forming mixtures of water + ethylene glycol or water + 1,2propylene glycol produces a solution with dielectric constant dependent on the composition. Consequently, the reduction of the dielectric constant leads to a decrease of xylose solubility. The application of the Jouyban−Acree model assuming the adjustable coefficients as functions of the temperature proved that the model is an excellent tool for correlating solubility data generating just three adjustable parameters with ARD% ranging from 0.18 to 1.79. Table S2 (Supporting Information) presents the values of the adjustable parameters and the values of ARD% obtained after fit of the models to experimental data. Regarding the activity coefficient models (NRTL, ASOG, and UNIFAC), Table 5 exhibits the values of ARD% for the fitting, and Figure 3 shows the behavior of the models. It was observed that the ability of correlating or predicting the solubility results depends on the ternary mixture under investigation. For mixtures of xylose + water + ethylene glycol, the best performance was obtained by NRTL followed, respectively, by ASOG and UNIFAC. For mixtures of xylose + water + 1,2-propylene glycol, the best performance was obtained by ASOG followed, respectively, by NRTL and UNIFAC. This capability was found by calculating the average value of ARD for the seven temperatures studied, and the results were 0.97% for NRTL, 1.16% for ASOG, and 2.89% for UNIFAC considering the mixture involving ethylene glycol and 1.60% for ASOG, 2.38% for NRTL, and 3.37% for UNIFAC considering the mixture involving propylene glycol. Table 6 presents the new parameters associated with the volume and surface of the molecules, and Table 7 presents the new interaction parameters for groups of molecules for the UNIFAC model. For the ASOG model the interaction

5. CONCLUSION The solubility of xylose in the mixtures formed by water/ ethylene glycol and water/1,2-propylene glycol was found to be dependent on the temperature and the molar composition of the binary liquid solution. The dependence of solubility on the temperature is related to the enthalpy of fusion of xylose. The dependence of solubility on the concentration of the binary solution is related to the dielectric constant of the mixture. The solubility of xylose is higher in water followed by ethylene glycol and 1,2-propylene glycol, respectively. The solubility of xylose in binary liquid mixtures present intermediate values regarding the solubility in pure components. The Jouyban−Acree model with three adjustable parameters as a function of temperature is an excellent option for the correlation of the solubility data. The semiempirical model NRTL is able to correlate the experimental data, and the parameters are dependent on temperature. The group contribution models UNIFAC (Dortmund) and ASOG were able to predict the solubility data, although the performance of ASOG was better than that of UNIFAC.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jced.8b01128.

Table 9. NRTL Binary Interaction Parametersa (τij) i/j

(1)

(2)

(3)

(4)

(1) (2) (3) (4)

0.00 −1928.41 + 253.08/T −2586.41 + 169.41/T −2655.74 + 323.74/T

−2012.74 + 148.21/T 0.00 −1701.54 + 142.85/T −2153.41 + 186.41/T

−1651.84 + 70.63/T −1443.41 + 110.36/T 0.00 −1707.32 + 240.58/T

−2606.74 + 91.08/T −1783.04 + 121.28/T −2104.08 + 250.74/T 0.00

a

(1) xylose, (2) water, (3) ethylene glycol, (4) propylene glycol. E



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Source and purity of the chemicals used in this work, coefficients and average relative deviation for the fitting of the Jouyban−Acree model, and composition of the ternary liquid mixture calculated by the thermodynamic modeling (PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Alessandro C. Galvão: 0000-0002-8255-4511 Pedro F. Arce: 0000-0002-4687-5297 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors wish to thank FAPESC (Fundaçaõ de Amparo à Pesquisa e Inovaçaõ do Estado de Santa Catarina, Grant 2017TR727) and FAPESP (Fundaçaõ de Amparo à Pesquisa do Estado de São Paulo, Grant 2015/05155-8) for the financial support.

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Phase Equilibrium Involving Xylose, Water, and Ethylene Glycol or 1,2

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