University of Tartu Reports Findings in Machine Learning (Exploring the Influenc e of Ionic Liquid Anion Structure on Gas-Ionic Liquid Partition Coefficients of Organic Solutes Using Machine Learning)
University of Tartu Reports Findings in Machine Learning (Exploring the Influenc e of Ionic Liquid Anion Structure on Gas-Ionic Liquid Partition Coefficients of Organic Solutes Using Machine Learning)
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to news reportingoriginating from Tartu, Eston ia, by NewsRx correspondents, research stated, “This article presentsan in-dept h investigation into the influence of anionic structures of ionic liquids (ILs) on gas-ionic liquidpartition coefficients (log ) of organic solutes in three IL s. While the primary objective was to examinewhether there is a relationship be tween the molecular structure of the IL anion component and log ,additionally i t was looked at whether the molecular descriptors of the anion in the relationsh ips encodepossible molecular interactions during the miscibility and partitioni ng in the IL.”
Key words
Tartu/Estonia/Europe/Cyborgs/Emergin g Technologies/Ionic Liquids/Machine Learning/Solvents
NETL
NSTL