首页|Findings from School of Energy Science and Engineering Update Understanding of M achine Learning (Rapid and Accurate Identification of Effective Metal Organic Fr ameworks for Tetrafluoromethane/ nitrogen Separation By Machine Learning)
Findings from School of Energy Science and Engineering Update Understanding of M achine Learning (Rapid and Accurate Identification of Effective Metal Organic Fr ameworks for Tetrafluoromethane/ nitrogen Separation By Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. Accordingto news reporting originating in Changs ha, People’s Republic of China, by NewsRx journalists, researchstated, “Effecti vely capturing tetrafluoromethane (CF4), a notorious greenhouse gas having a gre enhousewarming potential 6630 times higher than carbon dioxide, is important to mitigate climate change. Metalorganic frameworks (MOFs) are promising adsorben ts to entrap CF4 with extreme high selectivity becausethey contain versatile fu nctionalized ligands and tunable pores.”
ChangshaPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningSchool of Energy Science a nd Engineering
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