Multicomponent solubility parameters

Specific separation effects can be understood from the multicomponent solubility parameter theory. Specific effects for nonpolar compounds are predictable with perfluorinated and graphitized carbon black stationary phases. Specific selectivity for polar compounds in reversed-phase HPLC can be realized with polar additives to the mobile phase. 

Y. Hernandez, M. hotya, D. Rickard, S.D. Bergin, J.N. Coleman, Measurement of multicomponent solubility parameters for graphene facilitates solvent discovery, Langmuir,... 

The Hildebrand one-component solubility parameter, 8, is appropriate for solutions lacking in polarity and in specific interactions. This parameter, to some extent, is now replaced by the multicomponent solubility parameters which give values for each of the different interaction forces. The total solubility parameter may be expressed as... 

There are several excellent reviews of multicomponent solubility parameters, including those by Hansen (1967), Karger et al. (1973), Barton (1975), and Snyder (1978). Barton s review provides all the information necessary to familiarize the reader with the subject, and it contains a comprehensive reference list to the original work that developed the theory and techniques on which the multicomponent approach is founded. 

Mixtures of nonpolar solvents are normally characterized by the term solubility parameter (5). The difference in solubility parameters of mixture components provides a measure of solution nonideality.Mixtures of aliphatic hydrocarbons are nearly ideal, whereas mixtures of aliphatic hydrocarbon with aromatics show appreciable nonideality. Sometimes, it is difficult to predict the behavior of highly nonideal mixtures. Thermodynamic properties of binary and multicomponent mixtures have been dealt with extensively in the literature. " ... 

Application of solubility parameter data is hindered by the multicomponent nature of humic substances, and homogeneous fractions are needed to obtain the necessary data for the macromolecules. Nevertheless, the available information shows that the best solvents for humic acids have polar,... 

Table 3 lists the Hildebrand solubility parameter 8, the total solubility parameter do, and the multicomponent parameters for dispersion dj, polar dp, and hydrogen bonding d/, forces for a number of solvents. These data are taken from the compilation by Barton (1975). He has pointed out that the data become empirical when multicomponent parameters are used, and thus it is important to use a set of data that are self-consistent. Keller et al. (1971) and Karger et al. (1976) have further subdivided the hydrogen bonding parameter into the acid or proton donor ( ) parameter, and the base or proton acceptor (6/,) parameter. Values for these are listed for some of the compounds in Table 3 from data provided by Snyder (1978). These data are not from the same source as those compiled by Barton (1975) and included in Table 3, and hence the values given for d/, should not be compared directly with those for 8 and 8. ... 

This chapter summarizes the thermodynamics of multicomponent polymer systems, with special emphasis on polymer blends and mixtures. After a brief introduction of the relevant thermodynamic principles - laws of thermodynamics, definitions, and interrelations of thermodynamic variables and potentials - selected theories of liquid and polymer mixtures are provided Specifically, both lattice theories (such as the Hory-Huggins model. Equation of State theories, and the gas-lattice models) and ojf-lattice theories (such as the strong interaction model, heat of mixing approaches, and solubility parameter models) are discussed and compared. Model parameters are also tabulated for the each theory for common or representative polymer blends. In the second half of this chapter, the thermodynamics of phase separation are discussed, and experimental methods - for determining phase diagrams or for quantifying the theoretical model parameters - are mentioned. 

This is the so-called one-component solubility parameter or also Hildebrand solubility parameter [29,30]. Other definitions of solubility parameters extend this concept and include the various contributions to intermolecular interactions in multicomponent polar and nonpolar liquids. Expressed in the experimentally... 

ITAAE is necessary to understand situations where the contribution of collective IMP in the bulk phase is comparable with the influence of the interface. This situation occurs in porous media. Petroleum disperse systems (PDS) are multicomponent systems with a hierarchy of intermolecular interactions (IMP). Various selective contributions to IMP determine appropriate terms to establish solubility parameters. This theory is a key to understand structuring of PDS on nano- and microscales. Special attention should be drawn to the new and still not fully understood effect of superficial aggregation when surface forces manage formation of superficial units during and after Langmuire and Brunauer-Emmett-Teller (BET) adsorption. This kinetic effect is closely related to phase stabiUty in open systems. 

Applications of solubility parameters include selecting compatible solvents for coating resins, predicting the swelling of cured elastomers by solvents, estimating solvent vapor pressure in polymer solutions for devolatilization and reaction systems (16), and predicting phase equihhria for polymer-polymer (107), polymer-binary (93), random copolymer (102), and multicomponent solvents (38, 98,108,109). 

If the mutual solubilities of the solvents A and B are small, and the systems are dilute in C, the ratio ni can be estimated from the activity coefficients at infinite dilution. The infinite dilution activity coefficients of many organic systems have been correlated in terms of stmctural contributions (24), a method recommended by others (5). In the more general case of nondilute systems where there is significant mutual solubiUty between the two solvents, regular solution theory must be appHed. Several methods of correlation and prediction have been reviewed (23). The universal quasichemical (UNIQUAC) equation has been recommended (25), which uses binary parameters to predict multicomponent equihbria (see Eengineering, chemical DATA correlation). 

Whilst it is obviously valuable to measure the solubility of reagents in the SCF, it is important to be aware that the solubility in a multicomponent system can be very different from that in the fluid alone. It is also important to note that the addition of reagents and catalysts can have a profound effect on the critical parameters of the mixture. Indeed, at high concentrations of reactants, the mole fraction of C02 is necessarily lower and it may not be possible to achieve a supercritical phase at the temperature of interest. Increases in pressure (i.e. further additions of C02) could yield a single liquid phase (which would have a much lower compressibility than scC02). For example, the Diels-Alder reaction (see Chapter 7) between 2-methyl-1,3-butadiene and maleic anydride has been carried out a pressure of 74.5 bar and a temperature of 50 °C, assuming that this would be under supercritical conditions as it would if it were pure C02. However, the critical parameters calculated for this system are a pressure of 77.4bar and a temperature of 123.2 °C, far in excess of those used [41]. 

Chapter 2) C) methods for correlating the solubility of drugs and environmentally important substances in multicomponent solutions (Chapter 4) and D) protein solubility and its correlation with the preferential binding parameter (Chapter 5) can have practical applications. 

The Wilson parameters Ay and NRTL parameters Qy inherit a Boltzmann-type T dependence from the origins of the expressions for G, but it is only approximate. Computations of properties sensitive to this dependence (e.g., heats of mixing and liquid/liquid solubility) are in general only qualitatively correct. However, all parameters are found from data for binary (in contrast to multicomponent) systems, and this makes parameter determination for the local composition models a task of manageable proportions. 

These three approaches have found widespread application to a large variety of systems and equilibria types ranging from vapor-liquid equilibria for binary and multicomponent polymer solutions, blends, and copolymers, liquid-liquid equilibria for polymer solutions and blends, solid-liquid-liquid equilibria, and solubility of gases in polymers, to mention only a few. In some cases, the results are purely predictive in others interaction parameters are required and the models are capable of correlating (describing) the experimental information. In Section 16.7, we attempt to summarize and comparatively discuss the performance of these three approaches. We attempt there, for reasons of completion, to discuss the performance of a few other (mostly) predictive models such as the group-contribution lattice fluid and the group-contribution Flory equations of state, which are not extensively discussed separately. 

With multicomponent amorphous polymeric materials, prediction of the occurrence of a surface enrichment effect is very difficult because of many uncertain parameters. With worked multicomponent silicate glasses, the wet grinding and polishing process, and the cleaning with liquids, leaches out the soluble constituents,... 

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