A structural approach to calculate physical properties of pure organic substances: The critical temperature, critical volume and related properties

Abstract

A new approach based on computation of the molecular surface interactions (MSI) to estimate several physical properties of pure organic substances is described. MSI are derived from molecular structural data and consist of total molecular surface area, electrostatic molecular surface interactions, and a hydrogen bonding term. This new approach estimates the critical temperature and the molar critical volume of pure organic substances with molecular weights in the range of 40-500 a.u.. In addition, the following properties can be calculated: the critical pressure, the boiling temperature, the molar volume in liquid state at normal pressure and temperature. The method can be used to predict physical properties of compounds having flexible or rigid, symmetric or asymmetric, polar or nonpolar molecular structures, and compounds with or without hydrogen bonding groups.

Eco-Relevant Properties of Selected Organosilicon Materials

“…However, such an approach is time consuming and, as noted, assumes that the compound does not decompose/react below its critical point_ In this section, a set of empirical relationships (Equations 4 -6) is provided that can be used to estimate Tc, Pc, and Vc from the normal boiling point (T b ) and molar volume (V m). These relationships, derived using more than 160 compounds [9], are assumed to be general, and thus valid for both organics and organosilicon compounds. Regression statistics are presented for each equation; where available, measured values of Tc, Pc, and V c are also listed in Tables 4 a, …”

Improved structure–property relationship models for prediction of critical properties

“…Also, two new non-linear descriptors were identified using genetic algorithms. Several of the descriptors involved in the non-linear constructed descriptor have been previously identified to be significant in modeling critical properties (see, e.g., Egolf et al [118], Espinosa et al [119], Grigoras [42], Katritzky [41], Luke [112], Stanton and Jurs [90]). where Hy = number of hydrophylic groups; HDSA = hydrogen donor surface area; GI = gravitational index; 3 χ = third order Randic index; q d = Zefirov charges on donor; S d = surface area of donors; S tot = total surface area WNSA-2 weighted PNSA [QC] Min e-e repulsion for a F atom Gravitation index (all pairs) YZ shadow where ω = acentric factor; C = number of carbon atoms; A = number of non-carbon atoms; Hetero = number of hetero atoms; Hy = number of hydrophylic groups; θ = T b …”

“…The QSPR approach has been applied in many different areas, including (a) properties of single molecules such as boiling point [40,41], critical temperature [42], vapor pressure [12,43], flash point [44], auto ignition temperature [45], density [46][47][48], refractive index [49] and melting point [11,50,51]; (b) interactions among different molecular species such as octanol/water partition coefficient [13,52], aqueous solubility of solids, liquids and vapors [47,48,53], solvent polarity scales [50,51], and GC retention time and response factor [129]; (c) surfactant properties such as critical micelle concentration [54], cloud point [55]; and (d) polymer properties such as the polymer glass transition temperature [56], polymer refractive index, and rubber vulcanization acceleration [57].…”

The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule Descriptors

“…Grigoras [132] furans, tetrahydrofurans the authors concluded that due to structural differences between nitrogen heterocycles and sulfur and oxygen heterocycles, various connectivity, electronic, constitutional and CPSA descriptors cannot adequately encode enough information for a combined set of heterocycles Stanton et al [134] furans, tetrahydrofurans, thiophenes, pyrans 299 MLR, NN both methods had the same quality of prediction for the training set Egolf and Jurs [135], Egolf et al [136] pyridines 572 for pyridines, in the case of the cross-validation set, the NNs outperformed conventional QSPR; descriptors that reflect hydrogen bonding and dipoledipole interactions improved the predictive models for the pyridines data set diverse organic compounds 298 for this set the back-propagation NN combination resulted in 1K improvement over the MLR alkanes 150 NN 10:7:1 architecture; the performance was slightly better in comparison with the MLR methods…”