On February 2, 2022, Song, Xue-Chao; Dreolin, Nicola; Damiani, Tito; Canellas, Elena; Nerin, Cristina published an article.Application In Synthesis of 4,4′-Sulfonylbis(chlorobenzene) The title of the article was Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials. And the article contained the following:
The synthetic chems. in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chems. in food contact materials were exptl. derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chems. and a series of mol. descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated mols. were 1.50 and 1.82%, resp. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confidence of 11 oligomers was improved by the direct comparison of the exptl. data with the predicted CCS values. Finally, the challenges and opportunities of current machine-learning models on CCS prediction were also discussed. The experimental process involved the reaction of 4,4′-Sulfonylbis(chlorobenzene)(cas: 80-07-9).Application In Synthesis of 4,4′-Sulfonylbis(chlorobenzene)
The Article related to nonintentional substance food contact collision crossection svm, nias, collision cross section, food contact materials, ion mobility, machine learning, Placeholder for records without volume info and other aspects.Application In Synthesis of 4,4′-Sulfonylbis(chlorobenzene)
Referemce:
Chloride – Wikipedia,
Chlorides – an overview | ScienceDirect Topics