On December 31, 2020, Xu, Mengyu; Wang, Chun-Hung; Terracciano, Anthony C.; Masunov, Artem E.; Vasu, Subith S. published an article.SDS of cas: 39512-49-7 The title of the article was High accuracy machine learning identification of fentanyl-relevant molecular compound classification via constituent functional group analysis. And the article contained the following:
Fentanyl is an anesthetic with a high bioavailability and is the leading cause of drug overdose death in the U. S. Fentanyl and its derivatives have a low LD and street drugs which contain such compounds may lead to death of the user and simultaneously pose hazards for first responders. Rapid identification methods of both known and emerging opioid fentanyl substances is crucial. In this effort, machine learning (ML) is applied in a systematic manner to identify fentanyl-related functional groups in such compounds based on their observed spectral properties. In our study, accurate IR (IR) spectra of common organic mols. which contain functional groups that are constituents of fentanyl is determined by investigating the structure-property relationship. The average accuracy rate of correctly identifying the functional groups of interest is 92.5% on our testing data. All the IR spectra of 632 organic mols. are from National Institute of Standards and Technol. (NIST) database as the training set and are assessed. Results from this work will provide Artificial Intelligence (AI) based tools and algorithms increased confidence, which serves as a basis to detect fentanyl and its derivatives The experimental process involved the reaction of 4-(4-Chlorophenyl)piperidin-4-ol(cas: 39512-49-7).SDS of cas: 39512-49-7
The Article related to fentanyl mol compound classification machine learning functional group analysis, Toxicology: Methods (Including Analysis) and other aspects.SDS of cas: 39512-49-7
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Piperidine – Wikipedia,
Piperidine | C5H11N – PubChem