| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Gaudin, Théophile
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (5/5 displayed)
- 2019Estimating the adsorption efficiency of sugar-based surfactants from QSPR modelscitations
- 2017Conformations of n-alkyl-α/β-D-glucopyranoside surfactants : Impact on molecular propertiescitations
- 2016Predictive models for amphiphilic properties of sugar-based surfactants
- 2015Data analysis of sugar-based surfactant properties : towards quantitative structure property relationships
- 2015Mixture descriptors toward the development of Quantitative Structure-Property Relationship models for the flash points of organic mixturescitations
Places of action
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document
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.