首页|Faculty of Technology Reports Findings in Machine Learning (QSPR for the predict ion of critical micelle concentration of different classes of surfactants using machine learning algorithms)
Faculty of Technology Reports Findings in Machine Learning (QSPR for the predict ion of critical micelle concentration of different classes of surfactants using machine learning algorithms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news originating from Medea, Algeria, by NewsRx corresp ondents, research stated, “The determination of the critical micelle concentrati on (CMC) is a crucial factor when evaluating surfactants, making it an essential tool in studying the properties of surfactants in various industrial fields. In this present research, we assembled a comprehensive set of 593 different classe s of surfactants including, anionic, cationic, nonionic, zwitterionic, and Gemin i surfactants to establish a link between their molecular structure and the nega tive logarithmic value of critical micelle concentration (pCMC) utilizing quanti tative structure-property relationship (QSPR) methodologies.”
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