CO2 reduction routes to value-added oxygenates: a review

Bhatia, Parth; Dharaskar, Swapnil; Unnarkat, Ashish

doi:10.1007/s11356-021-16003-w

Cited by 11 publications

(1 citation statement)

References 124 publications

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“…The climate warming caused by the greenhouse effect is the focus of social concern. – Excessive emission of CO 2 is the main culprit of the greenhouse effect. An international consensus has been reached to control the level of CO 2 emissions.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Assisted Screening of Cu-Based Trimetallic Catalysts for Electrochemical Conversion of CO₂ to CO

Xiong,

Liu,

Yang

et al. 2023

Energy Fuels

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The electrochemical reduction of carbon dioxide to useful chemicals and fuels is a new strategy to utilize large amounts of carbon dioxide. However, the lack of efficient catalysts has hindered the development of this technology. Herein, a machine learning (ML)-assisted screening model is developed to explore efficient trimetallic electrocatalysts for the CO 2 reduction reaction by combining with density functional theory (DFT) and electrochemical experiments. The group of doped elements in the periodic table is the most important descriptor of Cu-based trimetallic electrocatalysts and the support vector regression algorithm has the best predictive performance. Based on ML predictions, the overpotential of PdPt@Cu is successfully predicted to be 0.11 V, and it shows the best electrocatalytic performance for the CO 2 reduction reaction (CO 2 RR). DFT calculation results show that CO 2 → COOH* is the potential-limiting step of CO 2 RR-to-CO for PdPt@Cu and its overpotential is 0.09 V, which is consistent with the ML-predicted results. The electrochemical experiments show that the Faraday efficiency of CO is 82.12% at −0.8 V vs RHE for PdPt@Cu. After 12 h of electrolysis in the H-cell, the catalyst still maintains good catalytic performance. This work provides an efficient method for screening catalysts.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Assisted Screening of Cu-Based Trimetallic Catalysts for Electrochemical Conversion of CO₂ to CO

Xiong,

Liu,

Yang

et al. 2023

Energy Fuels

View full text Add to dashboard Cite

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Microbial electrosynthesis of valuable chemicals from the reduction of CO2: a review

Suri,

Aeshala,

Palai

2024

Environ Sci Pollut Res

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Sustainable methanol production from carbon dioxide: advances, challenges, and future prospects

Patil,

Naji,

Mondal

et al. 2024

Environ Sci Pollut Res

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CO2 reduction routes to value-added oxygenates: a review

Cited by 11 publications

References 124 publications

Machine Learning-Assisted Screening of Cu-Based Trimetallic Catalysts for Electrochemical Conversion of CO₂ to CO

Machine Learning-Assisted Screening of Cu-Based Trimetallic Catalysts for Electrochemical Conversion of CO₂ to CO

Microbial electrosynthesis of valuable chemicals from the reduction of CO2: a review

Sustainable methanol production from carbon dioxide: advances, challenges, and future prospects

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CO2 reduction routes to value-added oxygenates: a review

Cited by 11 publications

References 124 publications

Machine Learning-Assisted Screening of Cu-Based Trimetallic Catalysts for Electrochemical Conversion of CO2 to CO

Machine Learning-Assisted Screening of Cu-Based Trimetallic Catalysts for Electrochemical Conversion of CO2 to CO

Microbial electrosynthesis of valuable chemicals from the reduction of CO2: a review

Sustainable methanol production from carbon dioxide: advances, challenges, and future prospects

Contact Info

Product

Resources

About

Machine Learning-Assisted Screening of Cu-Based Trimetallic Catalysts for Electrochemical Conversion of CO₂ to CO

Machine Learning-Assisted Screening of Cu-Based Trimetallic Catalysts for Electrochemical Conversion of CO₂ to CO