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| http://dx.doi.org/10.1073/pnas.97.10.5028 | DOI Listing |
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A prediction model for electrical strength of gaseous medium based on molecular reactivity descriptors and machine learning method.
J Mol Model
January 2025
Hubei Key Laboratory·for High-Efficiency-Utilization of Solar Energy and Operation, Control of Energy-Storage System, Hubei-University of Technology, Wuhan, 430068, China.
Context: Ionization and adsorption in gas discharge are similar to electrophilic and nucleophilic reactions. The molecular descriptors characterizing reactions such as electrostatic potential descriptors are useful in predicting the electrical strength of environmentally friendly gases. In this study, descriptors of 73 molecules are employed for correlation analysis with electrical strength.
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Acta Neuropathol
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Polar branch of the Russian Federal Research Institute of Fisheries and Oceanography ("PINRO" named after N.M. Knipovich), Murmansk, Russia.
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JNCASR: Jawaharlal Nehru Centre for Advanced Scientific Research, New Chemistry Unit, Jakkur, Bangalore, INDIA.
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Université Grenoble Alpes, INSERM, U1216, Grenoble Institute Neurosciences, GIN, Grenoble, France.
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