2024
DOI: 10.1021/acsomega.4c01175
|View full text |Cite
|
Sign up to set email alerts
|

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

Mood Mohan,
Omar N. Demerdash,
Blake A. Simmons
et al.

Abstract: Carbon dioxide (CO 2 ) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO 2 capture. Chemically reactive DESs, which form chemical bonds with the CO 2 , are superior to nonreactive, physically based DESs for CO 2 absorption. However, there are no accurate computational models that provide accurate … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(1 citation statement)
references
References 60 publications
0
1
0
Order By: Relevance
“…It is a powerful tool to accelerate molecule design and functional materials discovery and processing for applications such as catalysts, 20 pharmaceutical synthesis, 21 and pretreatment of Li-ion batteries. 22,23 ML approaches also have great performance in DESs, such as property prediction 24 and gas absorption, 25 which could unlock new opportunities for the recovery of spent LIBs through DESs. Great attention has been paid to leaching cathodes through DESs; 12 currently, the mainstream view assumes that low viscosity, high acidity, strong coordination, and reducibility of DESs might be beneficial for efficient leaching, 26 while little work has validated these hypotheses, and we are still lacking quantification of the importance of each property.…”
Section: Introductionmentioning
confidence: 99%
“…It is a powerful tool to accelerate molecule design and functional materials discovery and processing for applications such as catalysts, 20 pharmaceutical synthesis, 21 and pretreatment of Li-ion batteries. 22,23 ML approaches also have great performance in DESs, such as property prediction 24 and gas absorption, 25 which could unlock new opportunities for the recovery of spent LIBs through DESs. Great attention has been paid to leaching cathodes through DESs; 12 currently, the mainstream view assumes that low viscosity, high acidity, strong coordination, and reducibility of DESs might be beneficial for efficient leaching, 26 while little work has validated these hypotheses, and we are still lacking quantification of the importance of each property.…”
Section: Introductionmentioning
confidence: 99%