A Functional Approach to Solubility Parameter Computations

Abstract

The determination of solubility parameters for solutes represents a challenging mathematical problem of locating the central tendency of solvent affinity based on a limited set of data taken from experimental observations. At present, the most commonly used methods for computing solubility parameters of a solute require a binary classification of solvent affinity for the solute and employ a spherical/ellipsoidal compatibility region in the three-dimensional Hansen solubility parameter space. Utilizing a binary classification requires an arbitrary solubility threshold, and an ellipsoidal fitting model imposes a symmetry on the intermolecular forces that is rarely reflected by the experimental data. To overcome these issues, an approach that makes use of accurate solubility data to describe a three-dimensional solubility function, f, is introduced. The principles of the approach are discussed in detail and the procedures for constructing the solubility function and computing solubility parameters are described. An example using PCBM solubility data available in the literature demonstrates the new method. Lastly, a method that employs f as a predictor of solubility in arbitrary solvents with a proposed measure of reliability is presented.

Effect of Solvent, Role of Formic Acid and Rh/C Catalyst for the Efficient Liquefaction of Lignin

“…Moreover, the R a /R o values of the other three solvents are higher than 1 (water, 2.08; methanol, 1.08; isopropanol, 1.07). Based on the HSP theory, this indicates the poor affinity to lignin . This trend of R a / R o value correlates with the weight percentage of bio‐oil produced in various solvents (Figure S4; ESI) used for liquefaction.…”

“…Based on the HSP theory, this indicates the poor affinity to lignin. [53] This trend of R a /R o value correlates with the weight percentage of bio-oil produced in various solvents ( Figure S4; ESI) used for liquefaction. As a result, ethanol was chosen for further solvent optimization.…”

Impact of Varying Binary Solvent Gradients on the Solubility Parameters of Poly(3‐hexylthiophene)

“…The FSPs of P3HT were calculated using the procedure described in the literature, and the commercially available HSPiP program was used to calculate the HSPs . For both sets of calculations the dispersion (δ D ), polar (δ P ), and hydrogen‐bonding (δ H ) solubility parameters of the solvent mixtures were computed using the single‐liquid approximation, wherein the solubility parameter of a binary mixture is a volume fraction (φ) weighted sum of the solubility parameters of the pure solvents 1 and 2 in the mixture, where i = D , P , or H .…”

“…These endeavors have fostered a renewed interest in solubility parameters and their application to polymeric materials. However, current methods for estimating the solubility parameters of conjugated polymers and organic semiconductor moieties, for example, fullerene and fullerene derivatives, suffer from theoretical and computational deficiencies that have led to many questions and investigations that seek improvement in the solubility parameter theory of these compounds …”

“…However, current methods for estimating the solubility parameters of conjugated polymers and organic semiconductor moieties, for example, fullerene and fullerene derivatives, suffer from theoretical and computational deficiencies that have led to many questions and investigations that seek improvement in the solubility parameter theory of these compounds. [26][27][28][29][30][31][32][33] Table 1 shows the solubility parameters of a prototype conjugated polymer, poly(3-hexylthiophene) (P3HT), previously reported in the literature. [28,31,32,34,35] The functional solubility parameters (FSPs) and Hansen solubility parameters (HSPs) were calculated using different computational methods and experimental constraints, for example, different solvents, concentration thresholds, etc.…”