A New Scaling Principles–Quantitative Structure Property Relationship Model (SP-QSPR) for Predicting the Physicochemical Properties of Substances at the Saturation Line

Abstract

In recent years, there has been rapid development in new methods for calculating the physicochemical properties of substances based on scaling principles (SP) and quantitative structure–property relationships (QSPR). This paper presents an extended SP-QSPR model that can be used to predict the refractive index, surface tension, density, and viscosity of liquids on the saturation line. The temperature dependence of the molar refractivity is also considered. In this paper, we propose a new additive increment scheme for alkanes and its halogenated derivatives which can be used for calculations of orthochor, scaling values of parachor, molar refractivity, critical molar volume and the molar volume of a liquid subcooled to the absolute zero (T = 0 K). The correlation between structural-additive properties and critical molar volume is also discussed.

Prediction of nanofluids properties: the density and the heat capacity

“…where С P0 -coefficient depending on the thermodynamic properties of a substance; γ -critical parameter, which has a universal value for different substances -γ =1.24 [19]; ψ(t) -crossover function of the reduced temperature - In [18] the authors show that the crossover function for non-associated substances has a universal character. For nanofluids, in which the base fluid belongs to the class of associated substances, the function ψ (t) must be determined individually using reference information on the heat capacity on the boiling line.…”

“…Figure 9 shows that the values of the isobaric mole heat capacity, which is calculated using the characteristic sizes of nanoparticles in the base fluid [14] are in a good agreement with experimental data on the heat capacity of isopropyl alcohol/Al 2 O 3 nanoparticles at a temperature of 293 K. Since optical studies are generally performed at environment temperature, the proposed method cannot predict the isobaric heat capacity of nanofluids in a wide range of the state parameters. To solve this problem, we propose to use the method of predicting the thermophysical properties of substances on the boiling line, which is based on the application of the basic principles of extended scaling [18]. According to this method, the heat capacity of various substances on the boiling line can be described by the equation:…”

The relationship between the surface tension and the saturated vapor pressure of model nanofluids

“…A number of papers dedicated to development the predicting methods for the surface tension of pure liquids and their mixtures [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] have been published from the middle of the last century to the presents. These methods can be divided into two types: empirical correlations and methods based on thermodynamic approaches.…”

“…Li et al [18] presented a new method based on a combination of corresponding-states and group contribution models. In addition, Zhelezny et al proposed a new scaling principlequantitative structure-property relationship method for predicting the physicochemical (including surface tension) properties [19].…”

Combined Experimental and Predictive Uncertainty of Quantitative Structure Property Relationship Models

“…with n being the number of data points, X i exp is the experimental value for the respective property for compound i , and X i calc means the corresponding value derived using the model. Other statistical parameters that are related to the predictive power of a model are, e.g., the bias of the method which should be close to zero 10 or the slope of the regression line which should be close to unity 4. However, none of these metrics describes the actual deviation of the modeled results from reality.…”