Thermodynamic Study of Molecular Interactions in Eutectic Mixtures

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Thermodynamic Study of Molecular Interactions in Eutectic Mixtures Containing Camphene Marcin Okuniewski, Kamil Paduszy#ski, and Urszula Maria Domanska J. Phys. Chem. B, Just Accepted Manuscript • DOI: 10.1021/acs.jpcb.6b10034 • Publication Date (Web): 23 Nov 2016 Downloaded from http://pubs.acs.org on December 2, 2016

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The Journal of Physical Chemistry

Thermodynamic Study of Molecular Interactions in Eutectic Mixtures Containing Camphene Marcin Okuniewski,† Kamil Paduszyński,∗,† and Urszula Domańska†,‡ † Department of Physical Chemistry, Faculty of Chemistry Warsaw University of Technology, Noakowskiego 3, 00-664 Warsaw, Poland ‡ Thermodynamic Research Unit, School of Chemical Engineering University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4001, South Africa E-mail: [email protected]

Phone: +48 (22) 234 56 40

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Abstract Terpenes are an abundant and diverse class of chemicals having numerous applications in different areas of chemistry. Therefore, a detailed knowledge of physical and thermodynamic properties of terpenes and their mixtures with other compounds is highly desired. This paper reports both a thermodynamic study on solid-liquid equilibrium (SLE) phase diagrams in binary systems formed by (±)-camphene (a representative terpene) and one of the following solvents: n-decane, n-dodecane, 1-decanol, 1-dodecanol, phenylmethanol, 2-phenylethanol, 2-cyclohexylethanol. The observed trends in the measured SLE data are discussed in terms of structure (alkyl chain length, aromacity) of the solvent and molecular interactions. Modeling of the considered SLE phase diagrams with three well-established thermodynamic models, namely, modified UNIFAC (Dortmund), perturbed-chain statistical associating fluid theory (PC-SAFT) and conductor-like screening model for real solvents (COSMO-RS), is presented. A comparative analysis of their performance is given in terms of average absolute deviations between predicted and experimental SLE temperature.


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Introduction The World Health Organization (WHO) estimates that majority of people worldwide (including 80% of population of Africa) relies on herbs in their primary health care. 1 All herbal medicines contain a great wealth of the chemical compounds known as terpenes. Terpenes form the largest and the most structurally diverse class of natural products, synthesized in many living organisms (mostly by plants). They have a variety of roles in mediating beneficial and antagonistic interactions between different species and play key roles in their living functions. 2 Thus far, more than 55,000 terpenes have been isolated from essential oils of both terrestrial and marine plants. 3 Besides plants, terpenes can be found in an array of secondary metabolites produced by certain bacteria and fungi. Both terpenes and their oxygenated derivatives called terpenoids (alcohols, ketones, esters) have found many applications since the Egyptians civilization because of their widespread abundance and diversity in properties and biological activity. 4 Nowadays, many terpenes are applied in several areas of chemical industry, for example as medicines, 5 or additives enhancing drug delivery systems 6 as well as cosmetics, or perfumes, or food additives/preservatives. 7 Terpenes have also found many applications in agriculture, for instance as insecticidal agents. 8 Terpenes are unsaturated hydrocarbons built of a number of isoprene units so that their general stoichiometric formula is (C5 H10 )n , where n > 1. In particular, the most commonly known terpene is rubber, which is actually a polyterpene composed of repeating units of isoprene. Isomeric terpenes can be substantially different in terms of their molecular structure, e.g. in terms of degree of branching or number of cycles. A general classification of terpenes is, however, based on the number of isoprene units, i.e. one can encounter monoterpenes (n = 2, e.g. camphene, limonene, myrcene), sesquiterpenes (n = 3, e.g. farnesene, humulene), diterpenes (n = 4, e.g. phytane), and so on. Molecular diversity of terpenes is the main reason why getting to know the rules governing their various properties emerges as a very important and challenging scientific task. For example, thermodynamic properties of mixtures of terpenes with organic solvents like solid-liquid equilibrium (SLE) phase diagrams are very important from the point of view of an isolation of terpenes 3 ACS Paragon Plus Environment


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from natural products and their further processing. Besides reliable and accurate measurements, development of versatile tools for thermodynamic modeling of systems involving terpenes should also be perceived as very important. This is due to the fact that predictive thermodynamic models usually give physical insight into the SLE behavior observed experimentally. The most important benefit from using them is, however, that they allow to provide reasonable estimation of a property of interest (e.g. SLE phase diagram) for new systems, or extrapolate the properties for known systems to conditions outside the range of available experimental data. On the other hand, thermodynamic models can be use to calculate properties that cannot be measured directly, e.g. activity coefficients. Unfortunately, only several papers on thermodynamic properties of terpenes and their modeling have been published so far. In particular, we addressed this issue in our previous papers, wherein the SLE phase diagrams of binary systems composed of one of common terpenoids (menthol and thymol) and organic solvents were measured and predicted by using leading thermodynamic theories. 9,10 In this work we extend our investigations to binary mixtures containing camphene. Camphene is a bicyclic monoterpene that can be isolated from essential oils produced by nutmeg (major constituent, up to 80%), bergamot, turpentine, cypress, camphor, citronella, neroli, ginger and valerian. It is a colorless to white solid compound with an pungent aroma. It is commonly used in the preparation of fragrances and as a food additive for flavoring. Furthermore, camphene has been disclosed for its biological benefits of anti-cancer, anti-inflammatory, antifungal, anti-gastric ulcers. Moreover, camphene was used in treatment for cardiovascular disease and as adjuvant. 11 The anti-fungal activity of essential oils containing camphene can also be utilized to preserve food, drugs and cosmetic products. 12 Motivated by all these applications, Štejfa et al. 13 carried out a comprehensive thermodynamic characterization of pure (±)-camphene. In the case of mixtures containing camphene, however, thermodynamic data (including SLE) are rare and scarce. 14–16 In particular, only several data sets of temperature-dependent solubility of camphene in aromatic hydrocarbons have been deposited in the Dortmund Data Bank. 14 A study of aqueous solutions of monoterpenes presented by Weidenhamer et al. 15 revealed that camphene is extremely hydrophopic compound, completely soluble in water at concentrations below 23 ppm.


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Finally, simple eutectic SLE phase diagram was observed for mixture {camphene + 1,8-cineole}. 16 Besides liquid solvents, solubility of camphene in subcritical and supercritical CO2 as well as the idea extraction of camphene from natural products using supercritical CO2 have been recently published. 17,18 This paper presents both experimental and computational thermodynamic study of SLE phase diagrams in binary mixtures composed of (±)-camphene and one of the following organic solvents: n-decane, n-dodecane, 1-decanol, 1-dodecanol, phenylmethanol, 2-phenylethanol and 2cyclohexylethanol. Selection of the listed solvents has been done to check the effects of their chemical structure (i.e. alkyl chain length, aromacity, hydrogen bonding) and different kinds of molecular interactions on the observed SLE behavior. Furthermore, modeling of the considered systems by means of modern thermodynamic approaches, namely, modified UNIFAC (Dortmund), perturbed-chain statistical associating fluid theory (PC-SAFT) and conductor-like screening model for real solvents (COSMO-RS), is presented. Evaluation of the models is given in terms of average absolute deviation between calculated and measured SLE temperature. An influence of complexity of molecular model of real system on the quality of predictions is also discussed.



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Experimental Section Chemicals Chemical names, CAS registry numbers, suppliers as well as a brief summary of purity and purification procedures for all the compounds considered in this work are summarized in Table 1. The sample of (±)-camphene was purchased from Sigma-Aldrich. The initial mass fraction purity certified by the supplier was 0.95. In order to remove residual water and other impurities that could affect further SLE phase diagrams determinations, the sample was dried in molten state under vacuum at 343 K for approximately 24 h. The solvents studied, i.e. n-decane (C10 ), n-dodecane (C12 ), 1-decanol (C10 OH), 1-dodecanol (C12 OH) and phenylmethanol (PMA, benzyl alcohol), 2phenylethanol (2PEA) and 2-cyclohexylethanol (2CEA), were purchased from Sigma-Aldrich and used without any further purification, except storing over molecular sieves for several days prior to the SLE measurements. Final purity of the solvents was analyzed by gas chromatography, whereas the water content in all the chemicals was determined with Karl-Fischer titration.

Differential Scanning Calorimetry The basic thermal characteristics of pure (±)-camphene, i.e. fusion temperature and fusion enthalpy at normal pressure, were determined with differential scanning calorimetry (DSC). DSC 1 STARa System with Liquid Nitrogen Cooling System from Mettler Toledo was used. Prior to the measurements the apparatus was calibrated with a 0.999999 mass fraction purity indium sample. Measurements were performed in heating/cooling/heating regime, with the temperature scanning rate of ±10 K · min−1 . Thermal properties were determined from the second heating scan. We checked that applying lower heating/cooling rate does not significantly affect the onset temperature and area of the fusion peak. The standard uncertainties of the fusion temperature and enthalpy have been estimated to be 0.1 K and 0.1 kJ · mol−1 , respectively.


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SLE Phase Diagrams Synthetic (dynamic) method was applied in this work to determine SLE phase diagrams in the studied binary systems. All the details regarding this method can be found elsewhere. 9 As opposed to the static analytical solubility determination, the solid-liquid phase transition temperature for a mixture of a known composition is measured in this method. Heterogeneous samples of (±)-camphene and solvent were prepared at ambient conditions in glass cells equipped with Rotaflow valve by means of weighing with an uncertainty of 0.0001 g. The combined uncertainty in mole fraction of the obtained binary mixture did not exceed 0.0005. The sample was placed in a thermostating bath and heated very slowly (scanning rate not exceeding 2 K · h−1 ) with vigorous stirring inside the cell until the turbidity disappeared (visual detection). The temperature at which the phase transition occurred was measured with an electronic thermometer P 755 (DOSTMANN electronic GmbH) and recognized as the SLE temperature. The standard uncertainty of the SLE temperature measurements was judged to be less than 0.1 K.



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Theory SLE Calculations Following the fundamental rules of chemical thermodynamics, 19 the following relationship between SLE temperature T and binary mixture composition x 1 (where subscript 1 denotes the component forming the solid phase) can be derived:

ln x 1 = −

∆fus H10 1 * − 1 + − ln γ1, 0 R , T Tfus,1 -

(1)

0 where R denotes universal gas constant, whereas Tfus,1 and ∆fus H10 stand for fusion temperature and

fusion enthalpy at normal pressure. If the solid can be also formed by component 2 (as in the case of the most of mixtures presented in this work), then the subscripts in eq (1) should be updated from 1 to 2, keeping in mind that in binary system x 2 = 1 − x 1 . The key quantity in eq (1) is γ1 , i.e. the activity coefficient of solute dissolved in saturated solution. In general, it is a complex function of both T and x 1 and expresses the deviation of the system under study from ideal phase behavior, i.e. the case when γ1 (x 1,T ) ≡ 1. Given γ1 (x 1,T ), one can numerically solve eq (1) to get equilibrium temperature T for a fixed mole fraction x 1 . The relationship between activity coefficient, liquid composition and temperature can be calculated by using different thermodynamic models. The models tested in this work are briefly summarized in the following subsection.

Thermodynamic Models Modified UNIFAC (Dortmund) UNIFAC is the well-known group-contribution (GC) method for calculating activity coefficients in liquid mixtures. 20 The GC approach mimics the real mixture of molecules as a solution of independent functional groups. The main idea is that the activity coefficient of a molecular component is calculated based on differences between activity coefficients of functional groups of 8 ACS Paragon Plus Environment

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component in mixture and its pure state. In particular, differences in both groups’ geometry and the chemical nature are key factors affecting the combinatorial and residual contribution of the activity coefficient. Since the pioneering UNIFAC paper, 20 many modifications of the original model have been proposed. However, only the modified UNIFAC (Dortmund) of Gmehling et al. 21,22 can be perceived as the most successful version due to a relevant interest of both academia and industry in it. 14 This is mainly because of a broad range of applicability (a extensive list of functional groups, thus a wide range of chemicals that can be represented) as well as improved predictive capacity for temperature-dependent properties (like SLE) due to more accurate temperature dependence of binary interaction parameters (up to 6 parameters per a pair of groups), compared with the original UNIFAC (only 2 parameters per a pair of groups). 22 In the SLE calculations presented in this work, in-house Matlab code of the modified UNIFAC (Dortmund) was applied.

PC-SAFT PC-SAFT 23,24 is a molecular-based equation of state representing real molecules as molecular chains composed of spherical segments. The segments are assumed to be capable of interacting with each other via different kinds of potentials. Each kind of interactions is treated separately, so that the thermodynamics of fluid is expressed as a residual Helmholtz energy (Ares ) split into respective contributions. In the most basic formulation, the main working PC-SAFT expression is a˜ res =

Ares = a˜ hc + a˜ disp + a˜ assoc RT

(2)

where superscripts “hc”, “disp” and “assoc” stand for hard-chain formation, dispersive forces (e.g. van der Waals forces) and association, respectively. Given a˜ res as a function of temperature, density and mixture composition, any other thermodynamic property (including activity coefficients) can be calculated. 19 Expressions for each term from eq (2) can be found in the original PC-SAFT works by Gross and Sadowski. 23,24



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In PC-SAFT approach, each compound is represented by a number of parameters related to geometry of its molecules and interactions between them: 23,24 m, the number of segments forming the chain; σ, segment diameter; u/kB , square-well potential depth for physical interactions. In the case of associating compounds, one needs to specify so-called association scheme and two additional parameters corresponding to energy and volume of association between sites A and B, denoted by ǫ AB /kB and κ AB , respectively. All the PC-SAFT parameters are usually fitted to experimental pure-fluid data like saturated liquid density and/or vapor pressure. Alternatively, they can be estimated following GC strategy. 25 In the case of mixtures, cross terms representing the pairwise interactions between dislike segments are required. In particular, cross-dispersive term u12/kB assigned to short range interactions between segment of molecule 1 and segments of molecule 2 and corresponding segment diameter σ12 are usually estimated following the Lorentz-Berthelot combining rules √ LB u12 = u1u2 1 − k12

(3)

σ1 + σ2 2

(4)

σ12 =

LB stands for the binary interaction parameter (or binary correction), that can In eq (3) the symbol k12

be obtained by means of different approaches. For example, it can be set to 0. Then PC-SAFT is applied in entirely predictive fashion, since modeling of any mixture property can be carried out LB based on pure-fluid parameters only. Alternatively, k12 can be fitted to binary data, preferably on

the property other than that to be eventually modeled. In our previous paper, 9 we presented SLE LB adjusted to infinite dilution activity coefficients PC-SAFT predictions employing the values of k12

of dissolved solute in solvent, γ1∞ , predicted by using modified UNIFAC (Dortmund). Considering previous success of this “PC-SAFT-UNIFAC” approach in computing binary SLE in systems with menthol and thymol, 9 we decided to adopt it in this work, thus verify its versatility in modeling the mixtures with nonpolar terpene.


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COSMO-RS COSMO-RS 26–28 method is a solid theory requiring only several global adjustable parameters and information on electronic charge distribution around the molecule to calculate thermodynamic properties of liquid mixtures. In view of this approach, molecular interactions are represented by the interactions between segments forming the surface around the molecules. The segments are characterized by screening charge density (σ), which is obtained beforehand from quantum chemical single-molecule calculation coupled with continuum solvation model (COSMO). The spatial distribution of σ is transformed into a distribution function called σ-profile, which is then used as an input for statistical thermodynamic formulas allowing to calculate activity coefficients in the ensemble of molecules (real solvents, RS). 28 COSMO-RS SLE predictions presented in this paper were performed by using commercial COSMOtherm suite 29 purchased from COSMOlogic (Leverkusen, Germany). Up-to-date parameterization BP_TZVP_C30_1601 has been used. The molecular input files (COSMO-files) were extracted directly from the COSMObase utility also purchased from the COSMOlogic. 29 σ-profiles were obtained on the basis of σ calculated with TURBOMOLE program 30 on the density functional theory (DFT) level with Becke-Perdew (BP) functional 31–33 and triple-ζ valence polarized basis set (TZVP). 34



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Results and Discussion Experimental Data The values of fusion temperature and enthalpy of the compounds considered in this study are shown in Table 2. The measured DSC thermogram of (±)-camphene is presented in Figure 1. As seen, the compound discloses an usual thermal behavior with only endothermal and exothermal peaks observed during heating and cooling, respectively. Endothermal peak observed in the second heating scan was assigned to solid-liquid phase transition and its onset temperature 320.59 ± 0.1 K was treated as the fusion temperature. This value is in a very good agreement with the fusion temperature measured by using synthetic method as well as with the value marked as “accepted” in DIPPR data compilation. 35 It is also within the values reported recently by other authors. 13,36 The observed discrepancies between the measured and reference values may be attributed to purity of samples used in different studies as well as to the sample pretreatment protocol. Enthalpy of fusion of (±)-camphene was determined by integration of the observed peak and the resulting value was 3.12 ± 0.2 kJ · mol−1 . This value is in large disagreement with the that reported by Parks and Huffman 37 (31.8 kJ · mol−1 ) used in DIPPR tables as recommended value. 35 On the other hand it is consistent with the value published recently by Štejfa et al. 13 (2.7 ± 0.3 kJ · mol−1 ), suggesting that the value reported in reference 37 seems to be erroneous. All the measured binary SLE data points are listed in detail in Tables S1 to S7 in the Supporting Information along with the values of the experimental activity coefficients of solutes dissolved in saturated solutions. The resulting phase diagrams are presented in Figure 2 along with the solubility curves representing the ideal phase behavior. Simple eutectic phase diagrams with complete miscibility in the liquid phase were detected for all the systems under study. For all the solvents except 2CEA, complete SLE phase diagrams were determined including two solubility curves and eutectic point. SLE curves measured for the systems with n-alkanes are very close the ideal solubility line in the whole range of composition. In these systems both components are hydrocarbons, hence it is 12 ACS Paragon Plus Environment

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expected that they will not exhibit high deviations from ideal behavior (similia similibus solventur). Moreover, it is interesting to note that the deviations of the system with C10 are negative, whereas these for the system with C12 are positive (see Tables S1 and S2 in the Supporting Information, in particular the values of activity coefficients listed therein). This may be related to prevailing role of packing effects in C12 resulting in less favorable van der Waals dispersive terpene-alkane interactions. Since the overall activity coefficients are close to unity, these interactions have to be quite strong as they compensate the positive combinatorial contribution to activity coefficient due significant differences in size and shape between solute and solvent. The solubility of (±)-camphene in alcohols increases in the following order: C10 OH ≈ C12 OH > PMA > 2PEA > 2CEA. In the case of systems with 1-alcohols, the deviations from ideal behavior are noticeably high and positive, see the values of activity coefficients listed in Tables S3 to S7 in the Supporting Information. However, the deviations are much lower when the alcohol acts as a dissolved compound, however they systematically increasing when approaching to the eutectic point. The observed change of the phase diagram compared to n-alkanes is due to the presence of OH group and a dominant effect of hydrogen bonding. Strongly positive deviations from the ideal behavior confirm a dominant role of the self-association of alcohol. In our previous paper concerned with mixtures of terpenoids with alcohols, it has been demonstrated that if the cross-association occur in the mixture, then the negative deviations from ideal SLE behavior emerge. 9 Differences in SLE curves for 2PEA and 2CEA elucidate effect of aromacity of the solvent. As seen in Figure 2, (±)-camphene is better soluble in 2CE than in 2PE in the whole range of temperature under investigation. This reveals that in the case of the aromatic solvent (2PE), the solvent-solvent interactions (possibly π–π stacking) play a key role in the macroscopic phase behavior. An impact of the length of alkyl group of the solvent (methyl in PMA vs ethyl in 2PEA) is reversed compared to n-alkanes, namely, (±)-camphene is better soluble in 2PE than in PMA. Therefore, additional methylene group enhances van der Waals dispersive forces while decreasing the overall contribution of hydrogen bonding.



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Modeling Models’ Input Summary Modified UNIFAC (Dortmund) group assignment matrix for all the chemical considered in this study is presented in Table 3. As seen, 9 distinct subgroups (including 6 main groups) were used to represent the structures of the compounds under consideration. The values of geometric and binary interaction parameters used in the presented SLE calculations can be found elsewhere. 14 A summary of the PC-SAFT pure-fluid parameters is given in Table 4. For all the chemicals, except 2PEA, the parameters were taken from literature. 9,23,25,38 Parameterization of 2PEA was performed in the same manner as it has been done for PMA and 2CEA in our previous contribution: 9 i.e. 2B (1 donor site + 1 acceptor site) association scheme was applied to mimic hydroxyl group, whereas the parameters were adjusted to saturated liquid density data and normal boiling temperature taken from DIPPR tables. 35 Additionally, in order to simplify the fitting procedure, the parameters of 2PEA related to self-association were transferred from PMA. To model SLE in binary mixtures, the PC-SAFT was applied in a purely predictive fashion and LB = 0, see so-called PC-SAFT-UNIFAC mode. In the former case, binary interaction parameter k12 LB eq (3), whereas in the latter case the value of k12 was adjusted to γ1∞ of (±)-camphene in solvent LB at its melting temperature predicted by modified UNIFAC (Dortmund). The final values of k12

obtained for each system are given in Table 5. Figure 3 shows spatial screening charge (σ) distributions calculated at BP-TZVP-COSMO level and the corresponding σ-profiles of all the compounds under consideration. Each σ-profile can be divided into three main regions: hydrogen bond donor region (σ < −1 e · nm−2 ), nonpolar region (−1 < σ/e · nm−2 < 1) and hydrogen bond acceptor region (σ > 1 e · nm−2 ). As seen from Figure 3a, (±)-camphene presents a pair of peaks within the nonpolar region only. Asymmetry of the σ-profile around +1 e · nm−2 can be noticed because of the presence of π bond allowing (±)-camphene to act as a very weak base. The σ-profiles of n-alkanes (see Figure 3b) confirm that they are strongly nonpolar compounds. σ-profiles of alcohols (see Figure 3c) are very similar to n-alkanes, but they


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disclose small peaks in the hydrogen bond donor and hydrogen bond acceptor regions. The peaks are relatively small due to a significant length of the alkyl chains. Nevertheless, as we have just discussed in previous section, these delicate differences in σ result in a completely different SLE phase diagrams. Finally, computations presented in Figure 3d explain qualitatively the difference in SLE curves observed for 2PEA and 2CEA. In fact, 2CEA displays their σ distributions mostly in the nonpolar region, thus affinity of 2CEA towards (±)-camphene is stronger compared to 2PEA. Finally, it is noteworthy that each compound has been considered as a combination of several conformers differing in the optimized molecular geometries and COSMO energies. Therefore, SLE phase diagrams were predicted based on the conformers averaging due to the Boltzmann distribution of their total free energies. 29

SLE Calculations SLE predictions with the models considered in this paper are presented in Figures 4 and 5. In Figure S1 to S7 in the Supporting Information the results of calculations are shown in terms of activity coefficients of solutes dissolved in saturated solutions. Performance of the models is expressed and discussed in terms of average absolute deviation (AAD) between calculated and experimental SLE temperature

AAD(T ) =

N 1 X calcd exptl T − Ti N i=1 i

(5)

where N stands for the number of data points measured for a given system. The values of AAD obtained for all the thermodynamic approaches under study (including ideal solubility equation) are plotted in Figure 6. Figure 4 shows that all the models predict the SLE behavior of the mixtures of (±)-camphene with n-alkanes and 1-alcohols with a reasonable accuracy. However, this is a little bit disappointing that in the case of the systems with n-alkanes, some models exhibit worse predictive capacity than the ideal solubility equation. In particular, this simple model ideal gives better results than



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PC-SAFT-UNIFAC and COSMO-RS in the case of the system {(±)-camphene + C10 }. As seen in Figure S1 in Supporting Information, only activity coefficients predicted by the modified UNIFAC (Dortmund) are in qualitative agreement with these obtained directly from SLE data. This is, however, not the case of the mixture with C12 (see Figure S2), for which experimental activity coefficients of both (±)-camphene in C12 and C12 in (±)-camphene are higher than one. For this system, PC-SAFT approach discloses the best predictive performance. On the basis of the results obtained for all the systems with alcohols, we conclude that hydrogen bonding treatment given by all the models seems to be physically correct. Indeed, an impact of alcohol structure on phase diagram is predicted properly. In the case of 1-alcohols, all the models used in this work perform very well in calculating both solubility curves and eutectic point (see Figure 4. As seen in Figure 6, all AAD values obtained for the systems with C10 OH and C12 OH are lower than 2 K. Furthermore, an effect of aromacity of the alcohol and alkyl substituent of solvent on SLE is also captured very well with the best results obtained again by the use of modified UNIFAC (Dortmund) (see Figure 5. Nevertheless, a good agreement between computation and experiment obtained by the COSMO-RS method should be particularly appreciated, taking into account that these calculations are based solely on chemical structure and some unimolecular ab initio calculations. Finally, all the tested modeling methods correctly reproduce activity coefficients of (±)-camphene dissolved in alcohol and alcohol dissolved in (±)-camphene, see Figures S3 to S7 in the Supporting Information.


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Conclusions In this paper it was showed that different types of molecular interactions between terpenes and other organic compounds like alkanes or alcohols can be disclosed by using both experimental thermodynamic solid-liquid phase equilibrium data. In particular, solubility of (±)-camphene (a representative terpene): (1) decreases with an increase of alkyl chain length of alkane used as solvent – this proves more favorable molecular packing of longer molecules; (2) drastically decreases when changing solvent from n-alkane to 1-alcohol – this is due to strong hydrogen bonds formed by alcohol’s molecules. (3) increases with an increase of alkyl chain length of solvents like 1-alkanol or phenyl-substituted alcohol, because of more significant contribution of van der Waals dispersive interactions associated with longer chains; (4) is lower in solvent having in aromatic rings (compared to saturated aliphatic cyclic solvents) due to π-π interactions. All the thermodynamic models under study displayed similar accuracy of SLE prediction in the considered systems. Taking into account all the 208 experimental data points measured in the presented study, the overall AAD in SLE temperature obtained for each model was as follows: 2.4 K LB = 0, 3.3 K for PC-SAFT-UNIFAC for modified UNIFAC (Dortmund), 3.3 K for PC-SAFT with k12

and 3.2 K for COSMO-RS. Therefore none of the approaches can be pointed out as the best one. This is actually very surprising outcome because the studied methodologies were established based on completely different molecular pictures of the real systems. The only advantage of PC-SAFT equation of state which should be highlighted is that it is a tool for consistent thermodynamic description of both mixtures and constituting components in their pure state.



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Page 18 of 34

Supporting Information Tables S1–S7 and Figures S1–S7 presenting the experimental SLE data and activity coefficients for the binary systems reported in this work.

Acknowledgement Funding for this research was provided by the National Science Centre based on decision number DEC-2013/11/N/ST5/01930.


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References (1) World Health Organization. http://www.who.int/mediacentre/factsheets/2003/fs134/en/ (accessed October 1, 2016). (2) Gershenzon, A.; Dudareva, N. The Function of Terpene Natural Products in the Natural World. Nature Chem. Biol. 2007, 3, 408–414. (3) Brahmkshatriya, P. P.; Brahmkshatriya, P. S. In Natural Products: Phytochemistry, Botany and Metabolism of Alkaloids, Phenolics and Terpenes; Ramawat, K. G., Mérillon, J.-M., Eds.; Springer-Verlag: Berlin Heidelberg, 2013; Vol. 86; pp 2665–2691. (4) Zwenger, S.; Chhandak, B. Plant Terpenoids: Applications and Future Potentials. Biotechnol. Mol. Biol. Rev. 2008, 3, 1–7. (5) Singh, B.; Sharma, R. A. Plant Terpenes: Defense Responses, Phylogenetic Analysis, Regulation and Clinical Applications. 3 Biotech 2015, 5, 129–151. (6) Kobayashi, D.; Matsuzawa, T.; Sugibayashi, K.; Morimoto, Y.; Kobayashi, M.; Kimurad, M. Feasibility of Use of Several Cardiovascular Agents in Transdermal Therapeutic Systems with l-Menthol-Ethanol System on Hairless Rat and Human Skin. Biol. Pharm, Bull. 1993, 16, 254–258. (7) Perricone, M.; Arace, E.; Corbo, M. R.; Sinigaglia, M.; Bevilacqua, A. Bioactivity of Essential Oils: A Review on their Interaction with Food Components. Front. Microbiol. 2015, 6, 76. (8) Herrera, J. M.; Zunino, M. P.; Dambolena, J. S.; Pizzolitto, R. P.; Gañan, N. A.; Lucini, E. I.; Zygadlo, J. A. Terpene Ketones as Natural Insecticides Against Sitophilus Zeamais. Ind. Crops Prod. 2015, 70, 435–442. (9) Okuniewski, M.; Paduszyński, K.; Domańska, U. (Solid + Liquid) Equilibrium Phase Diagrams in Binary Mixtures Containing Terpenes: New Experimental Data and Analysis of



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Several Modelling Strategies with Modified UNIFAC (Dortmund) and PC-SAFT Equation of State. Fluid Phase Equilib. 2015, 422, 66–77. (10) Okuniewski, M.; Paduszyński, K.; Domańska, U. Phase Diagrams of Binary Mixtures Containing Terpenoids and Their Modeling with Leading Thermodynamic Approaches. AIChE J. 2016, submitted. (11) Li, J. F.; Liang, N.; Sun, Z.; Du, R. J.; Shi, B.; Hou, X. H. Simulataneous Determination of Isoquinoline, Caryophyllene Oxide, Hexadecane in Essential Oils from Juglandis Mandshuricae Cortex by Vapour-Vapour Extaction Combined with Gas Chromatograph Analysis. Asian J. Tradit. Med. 2012, 7, 246–252. (12) Hossain, M. A.; Ismail, Z.; Rahman, A.; Kang, S. C. Chemical Composition and Anti-fungal Properties of the Essential Oils and Crude Extracts of Orthosiphon stamineus Benth. Ind. Crops Prod. 2008, 27, 328–334. (13) Štejfa, V.; Fulem, M.; Růžička, K.; Červinka, C. Thermodynamic Study of Selected Monoterpenes II. J. Chem. Thermodyn. 2014, 79, 272–279. (14) Dortmund

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(DDBST).

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(18) Al-Asheh, S.; Allawzi, M.; Al-Otoom, A.; Allaboun, H.; Al-Zoubi, A. Supercritical Fluid Extraction of Useful Compounds from Sage. Natural Science 2012, 4, 544–551. (19) Prausnitz, J. M.; Lichtenthaler, R. N.; de Azevedo, E. G. Molecular Thermodynamics of Fluid-Phase Equilibria; Prentice Hall PTR: New Jersey, 1999. (20) Fredenslund, A.; Jones, R. L.; Prausnitz, J. M. Group-Contribution Estimation of Activity Coefficients in Nonideal Liquid Mixtures. AIChE J. 1975, 21, 1086–1099. (21) Gmehling, J.; Li, J.; Schiller, M. A Modified UNIFAC Model. 2. Present Parameter Matrix and Results for Different Thermodynamic Properties. Ind. Eng. Chem. Res. 1993, 32, 178–193. (22) Constantinescu, D.; Gmehling, J. Further Development of Modified UNIFAC (Dortmund): Revision and Extension 6. J. Chem. Eng. Data 2016, 61, 2738–2748. (23) Gross, J.; Sadowski, G. Perturbed-Chain SAFT: An Equation of State Based on a Perturbation Theory for Chain Molecules. Ind. Eng. Chem. Res. 2001, 40, 1244–1260. (24) Gross, J.; Sadowski, G. Application of the Perturbed-Chain SAFT Equation of State to Associating Systems. Ind. Eng. Chem. Res. 2002, 41, 5510–5515. (25) Tihic, A. Group Contribution sPC-SAFT Equation of State. PhD Dissertation, Technical University of Denmark, Lyngby, Denmark, 2008. (26) Klamt, A. Conductor-like Screening Model for Real Solvents: A New Approach to the Quantitative Calculation of Solvation Phenomena. J. Phys. Chem. 1995, 99, 2224–2235. (27) Klamt, A.; Eckert, F. COSMO-RS: A Novel and Efficient Method for the A Priori Prediction of Thermophysical Data of Liquids. Fluid Phase Equilib. 2000, 172, 43–72. (28) Klamt, A. COSMO-RS: From Quantum Chemistry to Fluid Phase Thermodynamics and Drug Design; Elsevier: Amsterdam, 2005.



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(29) Eckert, F.; Klamt, A. COSMOtherm, Version C3.0, Release 16.01; COSMOlogic GmbH & Co. KG, Leverkusen, Germany. 2015. (30) Ahlrichs, R.; Bär, M.; Häser, M.; Horn, H.; Kölmel, C. Electronic Structure Calculations on Workstation Computers: The Program System TURBOMOLE. Chem. Phys. Lett. 1989, 162, 165–169. (31) Becke, A. D. Density-Functional Exchange-Energy Approximation with Correct Asymptotic Behavior. Phys. Rev. A 1988, 38, 3098–3100. (32) Vosko, S. H.; Wilk, L.; Nusair, M. Accurate Spin-Dependent Electron Liquid Correlation Energies for Local Spin Density Calculations: A Critical Analysis. Can. J. Phys. 1980, 58, 1200–1211. (33) Perdew, J. P. Density-Functional Approximation for the Correlation Energy of the Inhomogeneous Electron Gas. Phys. Rev. B 1986, 33, 8822–8824. (34) Schäfer, A.; Huber, C.; Ahlrichs, R. Fully Optimized Contracted Gaussian Basis Sets of Triple-ζ Valence Quality for Atoms Li to Kr. J. Chem. Phys. 1994, 100, 5829–5835. (35) Design Institute for Physical Property Research (DIPPR)/AIChE, DIPPR Project 801. https://app.knovel.com/web/toc.v/cid:kpDIPPRPF7 (accessed October 1, 2016). (36) Wei, D.; Wang, L.; Zhang, C. Solid-Liquid Equilibria in Binary Mixtures of 1,8-Cineole with p-Cymene, β-Pinene and Camphene. J. Chem. Eng. Data 2010, 55, 1456–1458. (37) Parks, G. S.; Huffman, H. M. Some Fusion and Transition Data for Hydrocarbons. Ind. Eng. Chem. 1931, 23, 1138–1139. (38) Grenner, A.; Kontogeorgis, G. M.; von Solms, N.; Michelsen, M. L. Modeling Phase Equilibria of Alkanols with the Simplified PC-SAFT Equation of State and Generalized Pure Compound Parameters. Fluid Phase Equilib. 2007, 258, 83–94.


Page 23 of 34

Table 1. Chemical Sample Information for the Compounds Investigated in This Work. Chemical name

CAS

Supplier

Initial mass

and abbreviation

23

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


Purification

fraction purity method

Final mass

Analysis

fraction purity method

Water content (ppm)

KFa

< 50

0.995

KF, GCb

< 100

none

0.998

KF, GCb

< 150

0.99

none

0.998

KF, GCb

< 150

Sigma-Aldrich

0.98

none

0.990

KF, GCb

< 100

100-51-6

Sigma-Aldrich

0.998

none

0.998

KF, GCb

< 100

2-phenylethanol (2PEA)

60-12-8

Sigma-Aldrich

0.99

none

0.995

KF, GCb

< 130

2-cyclohexylethanol (2CEA)

4442-79-9 Sigma-Aldrich

0.98

none

0.98

KF, GCb

< 100

(±)-camphene

79-92-5

Sigma-Aldrich

0.95

vacuum drying

n-decane (C10 )

124-18-5

Sigma-Aldrich

0.99

none

n-dodecane (C12 )

112-40-3

Sigma-Aldrich

0.99

1-decanol (C10 OH)

112-30-1

Sigma-Aldrich

1-dodecanol (C12 OH)

112-53-8

phenylmethanol (PMA)

a

Karl-Fischer method.

b

Gas chromatography.

ACS Paragon Plus Environment

–


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Table 2. Experimental and Literature Thermal Properties of the Investigated Compounds at 0 and Fusion Enthalpy ∆ H 0 . Normal Pressure: Fusion Temperature Tfus fus Compound

0 /K Tfus

∆fus H 0 / kJ · mol−1

This work

Literature

320.59a ; 320.3b,c

320.15; 35 317.6; 13 324.2 36

C10

245.6b,c

243.51 35

28.71 35,c

C12

263.2b,c

263.57 35

36.84 35,c

C10 OH

280.1b,c

280.05 35

33.67 35,c

C12 OH

297.2b,c

296.95 35

40.2 35,c

PMA

258.1b,c

257.85 35

8.79 35,c

2PEA

—

246.15 35,c

9.65 35,c

(±)-camphene

a

Measured by using DSC.

b

Measured by using dynamic method.

c

The value used in SLE calculations.


This work Literature 3.12c

31.8; 37 2.7 13

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Table 3. Modified UNIFAC (Dortmund) Group Assignments for the Compounds Investigated in This Work. Main Groups and Subgroups Numbers According to Numbering Scheme Defined in Reference 21 Given Below the Group Names. Compound

CH3

CH2

CH2 −C ACH

1 (1)

1 (2)

2 (7)

3 (9)

(±)-camphene

2

0

1

C10

2

8

C12

2

C10 OH

ACCH2

OH(p) CY-CH2

CY-CH

CY-C

4 (11)

5 (14)

42 (78)

42 (79)

42 (80)

0

0

0

3

2

1

0

0

0

0

0

0

0

10

0

0

0

0

0

0

0

1

9

0

0

0

1

0

0

0

C12 OH

1

11

0

0

0

1

0

0

0

PMA

0

0

0

5

1

1

0

0

0

2PEA

0

1

0

5

1

1

0

0

0

2CEA

0

2

0

0

0

1

5

1

0



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Table 4. Pure-Fluid PC-SAFT Parameters for the Compounds Investigated in This Work. ǫ AB /kB / K

κ AB

4.8225 3.8281 267.400

2926.14

0.00180

C12 OH 38,a

5.3753 3.9023 270.160

3081.87

0.00140

PMA 9,a

2.7080 3.8416 350.295

2380.72

0.00675

2PEAa,b

3.0567 3.8646 336.767

2380.72

0.00675

2CEA 9,a

3.2455 3.9684 312.475

2892.23

0.00237

Compound

m

(±)-camphene 25

3.0322 4.1470 300.830

C10 23

4.6627 3.8384 243.870

C12 23

5.3060 3.8959 249.210

C10 OH 38,a

a

2B association scheme.

b

This work.

σ/Å

u/kB / K


Page 27 of 34

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Table 5. Infinite Dilution Activity Coefficients of (±)-Camphene in Solvent (γ1∞ ) at T = 320.3 K Predicted by the Modified UNIFAC (Dortmund) and the Adjusted Binary Interaction LB ) Used in PC-SAFT Modeling. Parameters (k12 LB k12

Solvent

γ1∞

C10

1.330

0.0104

C12

1.263

0.0079

C10 OH

6.426 −0.0007

C12 OH

5.424 −0.0066

PMA

15.42

0.0060

2PEA

13.19

0.0052

2CEA

9.031 −0.0110



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Figure 1. Chemical structure and DSC thermogram of pure (±)-camphene.


Page 28 of 34

Page 29 of 34

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Figure 2. Temperature T vs mole fraction x 1 SLE phase diagrams for binary systems {(±)camphene (1) + solvent (2)}. Key: empty circles, C10 ; filled circles, C12 ; empty squares, C10 OH; filled squares, C12 OH; empty triangles, PMA; filled triangles, 2PEA; diamonds, 2CEA; solid lines, ideal solubility equation.



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Page 30 of 34

Figure 3. Screening charge density (σ) distributions (BP-TZVP-COSMO level) and corresponding σ-profiles used in the COSMO-RS calculations presented in this work: (a) (±)-camphene; (b) nalkanes; (c) 1-alcohols; (d) cyclic alcohols. Number of conformers taken into account in modeling is given in parentheses. σ-profiles corresponding to the lowest energy conformer plotted using thicker lines.


Page 31 of 34

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Figure 4. Temperature T vs mole fraction x 1 SLE phase diagrams for binary systems {(±)camphene (1) + solvent (2)}. Key: empty circles, C10 ; filled circles, C10 OH; empty squares, C12 ; filled squares, C12 OH; solid lines, modified UNIFAC (Dortmund); dashed lines, PC-SAFTUNIFAC; dot-dashed lines, COSMOR-RS.



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Page 32 of 34

Figure 5. Temperature T vs mole fraction x 1 SLE phase diagrams for binary systems {(±)camphene (1) + solvent (2)}. Key: circles, PMA; squares, 2PEA; triangles, 2CEA; solid lines, modified UNIFAC (Dortmund); dashed lines, PC-SAFT-UNIFAC; dot-dashed lines, COSMOR-RS.


Page 33 of 34

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Figure 6. Average absolute deviation (AAD) between experimental and calculated SLE temperature for each binary system and modeling approach considered in this work. AAD defined in eq (5).



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TOC Graphic


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Thermodynamic Study of Molecular Interactions in Eutectic Mixtures

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