Fast Prediction of the Equivalent Alkane Carbon Number Using Graph Machines and Neural Networks.

International audienceThe hydrophobicity of oils is a key parameter to design surfactant/oil/water (SOW) macro-, micro-, or nano-dispersed systems with the desired features. This essential physicochemical characteristic is quantitatively expressed by the equivalent alkane carbon number (EACN) whose experimental determination is tedious since it requires knowledge of the phase behavior of the SOW systems at different temperatures and for different surfactant concentrations. In this work, two mathematical models are proposed for the rapid prediction of the EACN of oils. They have been designed using artificial intelligence (machine-learning) methods, namely, neural networks (NN) and graph machines (GM). While the GM model is implemented from the SMILES codes of a 111-molecule training set of known EACN values, the NN model is fed with some σ-moment descriptors computed with the COSMOtherm software for the 111-molecule set. In a preliminary step, the leave-one-out algorithm is used to select, given the available data, the appropriate complexity of the two models. A comparison of the EACNs of liquids of a fresh set of 10 complex cosmetic and perfumery molecules shows that the two approaches provide comparable results in terms of accuracy and reliability. Finally, the NN and GM models are applied to nine series of homologous compounds, for which the GM model results are in better agreement with the experimental EACN trends than the NN model predictions. The results obtained by the GMs and by the NN based on σ-moments can be duplicated with the demonstration tool available for download as detailed in the Supporting Information

Carnitine Alkyl Ester Bromides as Novel Biosourced Ionic Liquids, Cationic Hydrotropes and Surfactants

Hypothesis: In contrast to anionic and nonionic amphiphilic substances, bio-based cationic ones are very rare. Cationic amphiphiles are mostly based on quaternary ammonium, pyridinium or imidazolium groups that are either badly biodegradable or have toxic residues even after degradation. In the search for green alternatives to cationic hydrotropes and amphiphiles, natural L-carnitine could be a promising candidate for a cationic headgroup. Experiments: By esterification of carnitine in one step and with low cost, cationic molecules with alkyl chain length of n = 2-14 could be obtained. Their thermal properties, aggregation behaviour and cytotoxicity were determined. Hydrophobic compounds were solubilized in their aqueous solutions and the PIT slope method was applied to determine a relative hydrophilicity. Findings: It was found that some pure carnitine ester bromides were liquid at room temperature and thus can be classified as ionic liquids. They are highly water-soluble, and in aqueous solutions, they showed hydrotrope or surfactant behaviour depending on their alkyl chain length. Their high hydrotropic efficiency was demonstrated by solubilizing Disperse Red 13, while also biomolecules, like vanillin, could be dissolved in reasonable amounts. In all tests, they performed at least as good as the tested reference substances, while showing similar cytotoxicity towards human skin keratinocytes, thus demonstrating their potential as green functional amphiphilic molecules of positive charge. (C) 2017 Elsevier Inc. All rights reserved