Mixture Descriptors toward the Development of Quantitative Structure–Property Relationship Models for the Flash Points of Organic Mixtures

Abstract

Quantitative structure−property relationships (QSPRs) are increasingly used for the prediction of physicochemical properties of pure compounds, but only a few have been developed to predict the properties of mixtures. In this work, a series of existing and new formulas were proposed to derive mixture descriptors for the development of QSPR models for mixtures. These mixture descriptors were used to model the flash points of a series of 435 organic mixture compositions. Multilinear models were obtained using 12 different mathematical formulas, taking into account the linear or nonlinear dependence of the flash point on the concentration of each compound. The best model, issued from the newly proposed (x 1 d 1 + x 2 d 2 ) 2 formula, was a four-parameter model presenting good prediction capabilities (with a mean absolute error in prediction of 10.3°C) compared with existing predictive methods for both mixtures and pure compounds. ■ INTRODUCTIONQuantitative structure−property relationships (QSPRs) are predictive models that allow the prediction of macroscopic properties by correlation with descriptors of the molecular structures of chemicals. 1 These molecular descriptors belong to various categories: 1,2 constitutional, topological, geometrical, or quantum-chemical. Such methods have been largely used for biological activities in the fields of toxicology, 3 ecotoxicology, 4 and pharmaceutics 5,6 and are increasingly used for physicochemical properties. 7−9Various models have been developed for hazardous physicochemical properties 10−19 such as flammability, 10,11 thermal stability, 12−14 and explosibility. 15−19 To date, the QSPR approach has mostly been dedicated to pure compounds, and only a few recent works have been dedicated to mixtures. 20 Ajmani and co-workers proposed various models to predict the densities 21,22 and infinite-dilution activity coefficients 23 of binary mixtures. In these studies, the molecular descriptors for each pure compound were combined, e.g., by mole-weighted averaging, 24 to derive mixture descriptors. These mixture descriptors were then correlated to the property of the studied mixtures. Several studies have also been dedicated to azeotropic mixtures. 25−29 In particular, Oprisiu et al. 29 developed several QSPR models to predict the boiling points 25,28 of azeotropic binary mixtures on the basis of fragment descriptors.The flash point (FP) is the temperature at which the vapor above a flammable liquid ignites under the effect of a spark. 30 This property characterizes flammability hazards of liquids and is a key safety issue in the risk assessment of industrial processes and in various regulatory frameworks dedicated to chemicals (for use, storage, and transport). 31,32 The flash points of pure compounds have been studied in several works with the aim of developing predictive methods taking advantage of the large availability of data. 33,34 Among them, many are based on knowledge of other properties such as the boiling point. 24,35,36 The highest performance was obtained by Carroll ...

New QSPR Models to Predict the Flammability of Binary Liquid Mixtures

“…Predictive capabilities were evaluated on an external validation set, constituted of 30 % of their dataset of 284 datapoints, and an error of 10.1 °C was obtained. In a previous work, similar performances were achieved by using a larger database of mixtures (including 435 datapoints) and different mixture formula to define the mixture descriptors from the molecular descriptors of the single compounds and their respective molar fractions in the mixture. The best model was obtained from a non‐linear mixture formula (the square of the mole‐weighted averaged values of molecular descriptors) and highlighted an error in prediction of 10.3 °C on an external validation set (respecting a “mixture‐out” partition, as defined by Muratov et al …”

“…The experimental dataset consists in 650 flash points extracted from literature and detailed in Supporting Information (Table S1). It represents an update of the database used in a previous work by taking into account new data published after its constitution . The database concerns 60 binary mixtures constituted of 47 different pure compounds (hydrocarbons, alcohols, ketones, esters, acids) in various concentrations (to be compared to 435 data for 34 compounds in 43 mixtures, in the previous work).…”

“…In previous work, a series of mixture formula f mixt were proposed to calculate mixture descriptors D based on the molecular descriptors d i and the molar fraction x i of each component in the mixture, as summarized on eq. …”

“…So, not only linear but also non‐linear formula may be considered. Among the 12 mixture formula proposed in previous study, work has been focused on the nine formulas taking into account the influence of the molar fraction of each constituent, presented in Table . This set of formula include some equations that consider the linear and non‐linear influences of the molar fraction of each compounds on the flash point and others that describe how the property deviates from an linear trend.…”

How to use QSPR‐type approaches to predict properties in the context of Green Chemistry

“…So, these two examples encourage the use of such predictive approaches to anticipate the possible increase of hazards when mixing chemicals. Another strategy was also recently developed by Gaudin et al 53 consisting of defi ning mixture descriptors by applying a mixing formula to molecular descriptors to achieve mixture QSPR models. Such an approach can be particularly useful when no mixing rule is (easily) available.…”