Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models

Nanomaterials

Zhong-Tao Jiang¹  Xiangpeng Gao¹  Kethaki Wickramaarachchi¹  Rukshima Dabare¹  Kok Wai Wong¹  Manickam Minakshi¹  S. Assa Aravindh² 

[1] College of Science, Health, Engineering & Education, Murdoch University, Perth, WA 6150, Australia;Nano and Molecular Systems Research Unit, University of Oulu, Pentti Kaiteran Katu 1, 90570 Oulu, Finland;

关键词: biomass carbon;    winery waste;    density functional theory;    nitrogen doping;    activating agent;    energy storage;   

DOI  :  10.3390/nano12111847

来源: DOAJ

【 摘 要 】

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 (KAC_urea), 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. KAC_urea 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.

【 授权许可】

Unknown