Machine learning boosted Design of Ionic Liquid-based Deep Eutectic Solvents for CO2 Capture

Ruina Zhang; Guokai Cui; Guoxiong Zhan; Mengyi Ma; Xiaochun Zhang; Xiangping Zhang; Hanfeng Lu

doi:10.1039/d6gc00083e

January 1, 2026Green Chemistry

Machine learning boosted Design of Ionic Liquid-based Deep Eutectic Solvents for CO2 Capture

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Key Points

To explore the role of machine learning in designing ionic liquid-based deep eutectic solvents for effective CO2 capture.
Application of machine learning techniques to optimize solvent design
Evaluation of deep eutectic solvents based on ionic liquids
Analysis of CO2 capture efficiency in laboratory conditions.
Machine learning identified improved formulations for deep eutectic solvents
Enhanced CO2 capture performance compared to traditional methods
Demonstrated scalability and sustainability benefits of these solvents.

Implication

Machine learning improves CO2 capture in ionic liquid-based deep eutectic solvents, indicating a greener alternative for carbon capture technologies.

Abstract

The escalating atmospheric CO2 concentration demands sustainable and scalable carbon capture technologies. Deep eutectic solvents (DESs), especially those based on ionic liquids (ILs), offer a green and tunable alternative to...

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Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69faa22704f884e66b532cca https://doi.org/10.1039/d6gc00083e

View Full Paper

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Machine learning boosted Design of Ionic Liquid-based Deep Eutectic Solvents for CO2 Capture

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine learning boosted Design of Ionic Liquid-based Deep Eutectic Solvents for CO2 Capture

Key Points

Implication

Abstract

Discussion

Cite This Study

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