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Predictive models for amphiphilic properties of sugar-based surfactants

Abstract : This project aims to develop new predictive models of the amphiphilic properties of biomass-based surfactants, in order to better anticipate their performance as ingredients of formulated products, such as detergents, or cosmetics. This contribution will present the different steps towards the development of predictive models for amphiphilic properties of sugar-based surfactants. A large collection of available experimental data was conducted for four important [1] amphiphilic properties for sugar-based surfactants: critical micelle concentration (CMC), surface tension at CMC (γCMC), efficiency (pC20), and Krafft temperature (TK). From a qualitative analysis of this database, important structural features for the targeted properties were identified. Several molecular descriptors for sugar-based surfactants were computed and analyzed, and it appeared that fragment descriptors (computed from the polar head or the apolar chain) have a better ability to discriminate sugar-based surfactants with respect to these features. In order to further understand how subtle structural changes can influence CMC, γCMC and pC20, a molecular thermodynamic [2] approach was also used. Based on all these studies, predictive Quantitative Structure-Property Relationship (QSPR) models [3] were developed for CMC, γCMC and pC20 that demonstrated reasonable predictive capabilities and potential for industrial applications. Predictive modelling for TK is currently under investigation.
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Conference papers
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https://hal-ineris.archives-ouvertes.fr/ineris-01854177
Contributor : Gestionnaire Civs <>
Submitted on : Monday, August 6, 2018 - 3:16:22 PM
Last modification on : Tuesday, July 23, 2019 - 8:22:02 PM

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  • HAL Id : ineris-01854177, version 1

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Théophile Gaudin, Guillaume Fayet, Patricia Rotureau, Isabelle Pezron. Predictive models for amphiphilic properties of sugar-based surfactants. 14. International Conference on Properties and Phase Equilibria for Product and Process Design (PPEPPD 2016), May 2016, Porto, Portugal. ⟨ineris-01854177⟩

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