首页|Findings from University of Southern California (USC) Provides New Data on Nanop articles (Solvent Dependence of Ionic Liquid-based Pt Nanoparticle Synthesis: Ma chine Learning-aided In-line Monitoring In a Flow Reactor)
Findings from University of Southern California (USC) Provides New Data on Nanop articles (Solvent Dependence of Ionic Liquid-based Pt Nanoparticle Synthesis: Ma chine Learning-aided In-line Monitoring In a Flow Reactor)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in Nanotechnology - N anoparticles. According to news reporting originating in Los Angeles, California , by NewsRx journalists, research stated, "Colloidal platinum nanoparticles (Pt NPs) possess a myriad of technologically relevant applications. A potentially su stainable route to synthesize Pt NPs is via polyol reduction in ionic liquid (IL ) solvents; however, the development of this synthetic method is limited by the fact that reaction kinetics have not been investigated." Financial supporters for this research include Alliance for Sustainable Energy, LLC, USC Office of Research and Innovation President's Sustainability Initiative Large Program Award, King Abdulaziz University. The news reporters obtained a quote from the research from the University of Sou thern California (USC), "In-line analysis in a flow reactor is an appealing appr oach to obtain such kinetic data; unfortunately, the optical featurelessness of Pt NPs in the visible spectrum complicates the direct analysis of flow chemistry products via ultraviolet-visible (UV-vis) spectrophotometry. Here, we report a machine learning (ML)-based approach to analyze in-line UV-vis spectrophotometri c data to determine Pt NP product concentrations. Using a benchtop flow reactor with ML-interpreted in-line analysis, we were able to investigate NP yield as a function of residence time for two IL solvents: 1-butyl-1-methylpyrrolidinium tr iflate (BMPYRR-OTf) and 1-butyl-2-methylpyridinium triflate (BMPY-OTf). While th ese solvents are structurally similar, the polyol reduction shows radically diff erent yields of Pt NPs depending on which solvent is used. The approach presente d here will help develop an understanding of how the subtle differences in the m olecular structures of these solvents lead to distinct reaction behavior. The ac curacy of the ML prediction was validated by particle size analysis and the erro r was found to be as low as 4%."
Los AngelesCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesIonic LiquidsMachin e LearningNanoparticlesNanotechnologySolventsUniversity of Southern Cali fornia (USC)
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