| 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
Data analysis of sugar-based surfactant properties : towards quantitative structure property relationships
Abstract
Since the beginning of 20th century, numerous experimental studies have been conducted on surfactants. Tabulated properties, such as Critical Micelle Concentration (CMC), Krafft Temperature (Tk), equilibrium surface tension (Yeq), Efficiency (pC20) or Cloud Temperature (Tc) can be found in reference textbooks [1]. Progress of experimental investigations and modern instantaneous internet access to a large part of relevant scientific literature makes now possible to gather a widespread amount of data. Because of such large availability of data, it is now relevant to focus on particular families, in order to obtain a reliable overview of such properties and their evolution with gradual variation of surfactant molecular structure. To exemplify this, an extensive database has been constituted for amphiphilic physicochemical properties of sugar-based surfactants. Four important properties characterizing this family of surfactants were selected: CMC, Tk, Yeq, and pC20. For the three first properties, data concerning approximately 300 molecules were found. This data collection can be used to : Quickly find useful data characterizing aqueous solutions of sugar-based surfactants ; Quickly evaluate if experimental data are missing ; Establish predictive models, including Quantitative Structure-Property Relationship (QSPR) models. A literature review on existing QSPR models for surfactants properties showed that only 10 models, concerning CMC, are relevant to sugar-based surfactants. The most statistically significant model [2] was tested for an extensive set of 271 sugar based surfactants. The results suggest that there is still room for improvement by focusing on particular families and using more extensive databases. Work is currently in progress in our laboratories to develop such models. This work was performed, in partnership with the SAS PIVERT, within the frame of the French Institute for the Energy Transition (Institut pour la Transition Energétique - ITE) P.I.V.E.R.T. (www.institut-pivert.com) selected as an Investment for the Future (“Investissements d’Avenir”). This work was supported, as part of the Investments for the Future, by the French Government under the reference ANR-001-01.