Halilu, Ahmed and Hadj-Kali, Mohamed Kamel and Hizaddin, Hanee Farzana and Hashim, Mohd Ali and Ali, Emad M. and Bhargava, Suresh (2024) Reaching machine learning leverage to advance performance of electrocatalytic CO2 conversion in non-aqueous deep eutectic electrolytes. Scientific Reports, 14 (1). p. 24757. ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-024-74893-5.
Full text not available from this repository.Abstract
Deep eutectic electrolytes (DEEs) show promise for future electrochemical systems due to their adjustable buffer capacities. This study utilizes machine learning algorithms to analyse the carbon dioxide reduction reaction (CO2RR) in DEEs with a buffer capacity of approximately 10.21 mol/pH. The objective is to minimize undesired hydrogen evolution reactions (HER) and render CO2RR dominant in a membrane cell. The CO2RR process was found to be non-adiabatic, as the time of nuclear motion for CO32- in K2CO3 product, through CO2 center dot- trapping, is 0.368 femtoseconds shorter than the 1.856 x 10(-3)s charge transfer relaxation time. Microkinetic analysis reveals that the rate of CO2RR to CO2 center dot- is 2.14 x 10(3) mol/cm(2)/s(2) with a rate constant of 2.1 x 10(10) cm/s. Our findings demonstrate that ensemble and k-Nearest Neighbours algorithms learn the CO2RR dataset, achieving a prediction accuracy of over 99%. The models were verified visually and quantitatively by overlaying predicted and experimental dataset. Diagnostic and SHAP analyses highlighted the gradient boost ensemble algorithm, predicting asymptotic current densities of -4.114 mA/cm(2) or -13.340 mA/cm(2), with high turnover frequencies (TOF) of 3.79 x 10(10) h(-1) or 12.30 x 10(10) h(-1) for CO2 center dot- or K2CO3 generation on silver electrodes, respectively. These results consider both accuracy and robustness against overfitting, providing an opportunity to optimize future non-aqueous electrolytes for convenient TOF measurements at industrially relevant current densities.
| Item Type: | Article |
|---|---|
| Funders: | CESDE-Ng Fundamental Research (RSP2024R361), King Saud University (CESDEN-FR-2023-2024) |
| Uncontrolled Keywords: | Deep eutectic electrolytes; CO2 capture and utilization; Machine learning; Artificial Intelligence; Electrocatalysis; Reaction Engineering; Non-aqueous electrolytes |
| Subjects: | T Technology > TP Chemical technology |
| Divisions: | Faculty of Engineering > Department of Chemical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 17 Feb 2025 02:39 |
| Last Modified: | 17 Feb 2025 02:39 |
| URI: | http://eprints.um.edu.my/id/eprint/47415 |
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