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First models to predict thermal decomposition properties of possible self-reactive substances based on industrial datasets
- Self-reactive substances are unstable chemical substances which can easily decompose and may lead to explosion. For this reason, their thermal stability properties are required within regulatory frameworks related to chemicals in order to assess their hazardous properties. Due to the fast development and availability of computers, predictive approaches like QSPR models are increasingly used in the evaluation process of hazardous substances complementary to experiments. In that context, the HAZPRED project (2015-2018) aimed to develop QSPR models to predict physical hazards of substances to fill the lack of knowledge on these hazardous substances quickly. An experimental campaign, based on 50 samples provided by Industrial producers, was carried out on potential self-reactive substances, for which no QSPR model already existed. Their heats of decomposition were characterized using differential scanning calorimetry in homogeneous experimental conditions. QSPR models were derivedSelf-reactive substances are unstable chemical substances which can easily decompose and may lead to explosion. For this reason, their thermal stability properties are required within regulatory frameworks related to chemicals in order to assess their hazardous properties. Due to the fast development and availability of computers, predictive approaches like QSPR models are increasingly used in the evaluation process of hazardous substances complementary to experiments. In that context, the HAZPRED project (2015-2018) aimed to develop QSPR models to predict physical hazards of substances to fill the lack of knowledge on these hazardous substances quickly. An experimental campaign, based on 50 samples provided by Industrial producers, was carried out on potential self-reactive substances, for which no QSPR model already existed. Their heats of decomposition were characterized using differential scanning calorimetry in homogeneous experimental conditions. QSPR models were derived using the GA-MLR method (using a genetic algorithm and multi-linear regressions) using molecular descriptors calculated by Dragon software based on both 3D molecular structures from density functional theory (DFT) optimizations, to access three-dimensional descriptors, and SMILES codes, favoring the access to simpler models, requiring no preliminary quantum chemical calculations. All models respected the OECD validation guidelines for regulatory acceptability of QSPR models. They were tested by internal and external validation tests and their applicability domains were defined and analyzed. If improved models should be expected with larger database (and a better ratio between size and chemical diversity), these first models already represent a screening tool capable to access early reactive hazards.…
Autor*innen: | G. Fayet |
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Koautor*innen: | Klaus-Dieter Wehrstedt, Annett Knorr, P. Rotureau |
Dokumenttyp: | Posterpräsentation |
Veröffentlichungsform: | Präsentation |
Sprache: | Englisch |
Jahr der Erstveröffentlichung: | 2021 |
Organisationseinheit der BAM: | 2 Prozess- und Anlagensicherheit |
2 Prozess- und Anlagensicherheit / 2.2 Prozesssimulation | |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
Freie Schlagwörter: | QSPR; Self-reactive substances; Thermal decomposition |
Themenfelder/Aktivitätsfelder der BAM: | Material |
Veranstaltung: | 19th International Workshop on Quantitative Structure-Activity Relationships in Environmental and Health Sciences |
Veranstaltungsort: | Online meeting |
Beginndatum der Veranstaltung: | 07.06.2021 |
Enddatum der Veranstaltung: | 09.06.2021 |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 03.09.2021 |
Referierte Publikation: | Nein |