Wickramaarachchi K, Minakshi M, Aravindh SA, Dabare R, Gao X, Jiang Z-T, Wong KW. Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models. Nanomaterials. 2022; 12(11):1847. https://doi.org/10.3390/nano12111847
Repurposing N-doped grape marc for the fabrication of supercapacitors with theoretical and machine learning models
|Author:||Wickramaarachchi, Kethaki1; Minakshi, Manickam1; Aravindh, S. Assa2;|
1College of Science, Health, Engineering & Education, Murdoch University, Perth, WA 6150, Australia
2Nano and Molecular Systems Research Unit, University of Oulu, Pentti Kaiteran Katu 1, 90570 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 6.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022071351652
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2022-07-13
Porous carbon derived from grape marc (GM) was synthesized via carbonization and chemical activation processes. Extrinsic nitrogen (N)-dopant in GM, activated by KOH, could render its potential use in supercapacitors effective. The effects of chemical activators such as potassium hydroxide (KOH) and zinc chloride (ZnCl₂) were studied to compare their activating power toward the development of pore-forming mechanisms in a carbon electrode, making them beneficial for energy storage. GM carbon impregnated with KOH for activation (KAC), along with urea as the N-dopant (KACurea), exhibited better morphology, hierarchical pore structure, and larger surface area (1356 m² g⁻¹) than the GM carbon activated by ZnCl₂ (ZnAC). Moreover, density functional theory (DFT) investigations showed that the presence of N-dopant on a graphite surface enhances the chemisorption of O adsorbates due to the enhanced charge-transfer mechanism. KACurea was tested in three aqueous electrolytes with different ions (LiOH, NaOH, and NaClO₄), which delivered higher specific capacitance, with the NaOH electrolyte exhibiting 139 F g⁻¹ at a 2 mA current rate. The NaOH with the alkaline cation Na⁺ offered the best capacitance among the electrolytes studied. A multilayer perceptron (MLP) model was employed to describe the effects of synthesis conditions and physicochemical and electrochemical parameters to predict the capacitance and power outputs. The proposed MLP showed higher accuracy, with an R² of 0.98 for capacitance prediction.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
116 Chemical sciences
216 Materials engineering
114 Physical sciences
This research was funded by Academy of Finland 311934.
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).