| 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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Rotureau, Patricia
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (20/20 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
- 2015How to use QSPR type approaches to predict the properties of green chemicals
- 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
- 2014Développement de modèles QSPR validés pour la prédiction de la stabilité thermique des peroxydes organiques
- 2013Predicting the physico-chemical properties of chemicals based on QSPR models
- 2013QSPR prediction of physico-chemical properties for REACHcitations
- 2013Prediction of thermal properties of organic peroxides using QSPR models
- 2012Global and local quantitative structure-property relationship models to predict the impact sensitivity of nitro compoundscitations
- 2012Development of validated QSPR models for impact sensitivity of nitroaliphatic compoundscitations
- 2011Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanismscitations
- 2010Excited state properties from ground state DFT descriptors : A QSPR approach for dyescitations
- 2010QSPR modeling of thermal stability of nitroaromatic compounds : DFT vs AM1 calculated descriptorscitations
- 2010Predicting explosibility properties of chemicals from quantitative structure-property relationshipscitations
- 2009On the prediction of thermal stability of nitroaromatic compounds using quantum chemical calculationscitations
- 2009Predicting explosibility properties of chemicals from quantitative structure-property relationships
- 2008Vers la prédiction des propriétés d’explosibilité des substances chimiques par les outils de la chimie quantique et les méthodes statistiques QSPR
- 2008Quantitative structure-property relationship studies for predicting explosibility of nitroaromatic compounds
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.