Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network-Reference-Cited by-同舟云学术

Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network

Published:2023 Issue:9 Volume:25 Page:3475-3492
ISSN:1463-9262
Container-title:Green Chemistry
language:en
Short-container-title:Green Chem.

Author:

Mohan Mood¹²^ORCID,Demerdash Omar²,Simmons Blake A.¹³^ORCID,Smith Jeremy C.²⁴,K. Kidder Michelle⁵^ORCID,Singh Seema¹^ORCID

Affiliation:

1. Deconstruction Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, USA

2. Biosciences Division and Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA

3. Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, USA

4. Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996, USA

5. Manufacturing Science Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6201, USA

Abstract

We report the development of machine learning model for the calculation of carbon dioxide solubilities in deep solvent solvents. This model helps to predict and accelerate the development of carbon capture solvents with ideal experimental conditions.

Funder

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Environmental Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/GC/D2GC04425K

Reference68 articles.

1. Investigation of CO2 solubility in monoethanolamine hydrochloride based deep eutectic solvents and physical properties measurements

2. The Impact of Climate Change on Mental Health: A Systematic Descriptive Review

3. Research on the peak of CO2 emissions in the developing world: Current progress and future prospect