Diving deeper into VOCs / Jessica Lum in COATINGS TECH, Vol. 19, N° 4 (04/2022)  

[article]  inCOATINGS TECH > Vol. 19, N° 4 (04/2022) . - p. 38-46 Titre : Diving deeper into VOCs : Predicting formulation component GC-MS response factor using quantitative structure-activity relationships coupled with artificial neural networks Type de document : texte imprimé Auteurs : Jessica Lum, Auteur ; Madeline Schultz, Auteur ; Erik Sapper, Auteur Année de publication : 2022 Article en page(s) : p. 38-46 Note générale : Bibliogr. Langues : Américain (ame) Catégories : Chromatographie en phase gazeuse Réseaux neuronaux (informatique) Revêtements -- Analyse Revêtements -- Teneur en composés organiques volatils Revêtements organiques Spectrométrie de masse Structure moléculaire Index. décimale : 667.9 Revêtements et enduits Résumé : The identification, measurement, and reduction of volatile organic compounds (VOCs) has been a key motivator in recent coatings research and development efforts. Analytical methods for determining VOC levels in organic coatings continue to improve, as chromatographic and spectroscopic approaches afford a means of quantifying VOC content directly in waterborne as well as solventborne coatings. Heuristic methods for estimating the volatility of formulation components are common but are not extensively validated using quantitative structure-property relationships. Thus, a clearer link between component transport through an evolving coating matrix during curing processes, the bulk volatility of a compound, and the elution and quantification of compounds in a gas chromatograph (GC) still must be made to promote innovation in this area. To address these issues, digital tools such as molecular descriptors and machine learning models are being combined with experimental measurements to better understand the time-dependent mechanistic nature of VOCs in coatings and to enable predictive control over the volatility and in-coating behavior of newly developed formulation components. Here, we present the development and validation of a molecular structure-based neural network for the prediction of response factor for formulation components in a gas chromatography (GC) analysis. This represents an important step in creating large-scale computational design tools that enable in silico formulation, optimization, and enduse property prediction of environmentally benign coatings. Note de contenu : - MATERIALS AND METHODS : Response factor determination by GC - Quantitative structure activity relationships for identifying molecular features relevant to response factor - RESULTS - DISCUSSION - FUTURE WORK - Table 1 : Calculated molecular descriptor with the largest positive or negative correlation with compound response factor - Table 2 : Response factors and retention times for 80 compound dataset for VOC analysis, with absolute and normalized values provided - Table 3 : Predicted and experimental response factors (absolute and normalized) in the training set - Table 4 : Predicted and experimental response factors (absolute and normalized) in the validation set En ligne : https://drive.google.com/file/d/1pBE163urWUV7WBjU9qEk0WoeUe4A422L/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=37610 [article]

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