Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents

Mohan, Mood; Demerdash, Omar N.; Simmons, Blake A.; Singh, Seema; Kidder, Michelle K.; Smith, Jeremy C.

doi:10.1021/acsomega.4c01175

Title: Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents

Journal Article · 19 April 2024 · ACS Omega

DOI: https://doi.org/10.1021/acsomega.4c01175 · OSTI ID:2339676

^[1]; Demerdash, Omar N. ^[1];

^[2]; Singh, Seema ^[3];

^[4];

^[5]

Biosciences Division and Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
Deconstruction Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, United States
Deconstruction Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
Manufacturing Science Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6201, United States
Biosciences Division and Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States, Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996, United States

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Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-05CH11231; SC0022273

OSTI ID:: 2339676

Journal Information:: ACS Omega, Journal Name: ACS Omega Journal Issue: 17 Vol. 9; ISSN 2470-1343

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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