首页|New Findings on Machine Learning from Sichuan University Summarized (Decoding Pf as Contamination Via Raman Spectroscopy: a Combined Dft and Machine Learning Inv estigation)
New Findings on Machine Learning from Sichuan University Summarized (Decoding Pf as Contamination Via Raman Spectroscopy: a Combined Dft and Machine Learning Inv estigation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting out of Chengdu, People's Republic o f China, by NewsRx editors, research stated, "In this study, density function th eory (DFT) is employed to compute Raman spectra of 40 important Per-fluoroalkyl substances (PFASs) as listed in Draft Method 1633 by U.S. Environmental Protecti on Agent. A systematic comparison of their spectral features is conducted, and R aman peaks and vibrational modes are identified." Funders for this research include National Natural Science Foundation of China ( NSFC), United States Department of Agriculture (USDA). Our news journalists obtained a quote from the research from Sichuan University, "The Raman spectral regions for the main chemical bonds (such as C-C, CF2 & CF3, O-H) and main functional groups (such as-COOH,-SO3H,-C2H4SO3H, and -SO2NH2) are identified and compared. The impacts of branching location in isomer, molec ular chain length, and functional groups on the Raman spectra are analyzed. Part icularly, the isomers of PFOA alter the peak locations slightly in wavenumber re gions of 200 - 800 and 1000 - 1400 cm-1, while for PFOS, spectral features in th e 230 - 360, 470 - 680, and 1030 - 1290 cm-1 regions exhibit significant differe nce. The carbon chain length can significantly increase the number of Raman peak s, while different functional groups give significantly different peak locations . To facilitate differentiation, a spectral database is constructed by introduci ng controlled noise into the DFT-computed Raman spectra. Subsequently, two chemo metric techniques, principal component analysis (PCA) and t-distributed stochast ic neighbor embedding (t-SNE), are applied to effectively distinguish among thes e spectra, both for 40 PFAS compounds and the isomers."
ChengduPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningSichuan University
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