| 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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article
Développement de modèles QSPR validés pour la prédiction de la stabilité thermique des peroxydes organiques
Abstract
Organic peroxides are unstable chemicals which can easily decompose and may lead to explosion. Such a process can be characterized by physico-chemical parameters such as heat and temperature of decomposition, whose determination is crucial to manage related hazards. These thermal stability properties are also required within many regulatory frameworks related to chemicals in order to assess their hazardous properties. In this work, performed in the PREDIMOL project, new quantitative structure-property relationship (QSPR) models were developed to predict accurately the thermal stability of organic peroxides from their molecular structure only in compliance with the OECD guidelines for regulatory acceptability of QSPRs. Based on the acquisition of 38 reference experimental data using differential scanning calorimetry apparatus in homogenous experimental conditions, multi-linear models were derived for the prediction of the decomposition heat and of the onset temperature using different types of molecular descriptors. Being rigorously validated, models presented the best performances in terms of fitting, robustness and predictive power and the descriptors used in these models were linked to the peroxide bond whose breaking represents the main decomposition mechanism of organic peroxides. These models will be available soon in a QSAR toolbox for REACH application files to supplement physical trials.