Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes
Liu, Xiaoyu (University of New South Wales)
Ji, Dali (University of New South Wales)
Jin, Xiaoheng (University of New South Wales)
Quintano, Vanesa (Institut Català de Nanociència i Nanotecnologia)
Joshi, Rakesh (University of New South Wales)

Data:	2023
Resum:	Graphene oxide (GO) intercalated with transition metal oxides (TMOs) has been investigated for optimal supercapacitance performance. However, attaining the best performance requires conducting numerous experiments to find an optimal material composition. This raises an important question; can resource consumption associated with extensive experiments be minimized? Here, we combine the machine learning (ML)-based random forest (RF) model with experimentally observed X-ray photoelectron spectroscopy (XPS) data to construct the complete chemical analysis dataset of Co(Ⅲ)/Co(Ⅱ) ratio for thermally synthesized Co-rGO supercapacitor electrodes. The ML predicted dataset could be further coupled with other experiment results, such as cyclic voltammetry (CV), to establish a precise model for predicting capacitance, with ML coefficient of determination (R) value of 0. 9655 and mean square error value of 6. 77. Furthermore, the error between predicted capacitance and experimental validation is found to be less than 8%. Our work indicates that RF can be used to predict XPS data for the TMO-GO system, thereby reducing experimental resource consumption for materials analysis. Moreover, the RF-predicted result can be further utilized in experimental and computational analysis.
Ajuts:	European Commission 101066462
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Publicat a:	Carbon, Vol. 214 (October 2023) , art. 118342, ISSN 0008-6223

DOI: 10.1016/j.carbon.2023.118342

7 p, 3.2 MB

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