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Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182344/ https://www.ncbi.nlm.nih.gov/pubmed/35683703 http://dx.doi.org/10.3390/nano12111847 |
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author | Wickramaarachchi, Kethaki Minakshi, Manickam Aravindh, S. Assa Dabare, Rukshima Gao, Xiangpeng Jiang, Zhong-Tao Wong, Kok Wai |
author_facet | Wickramaarachchi, Kethaki Minakshi, Manickam Aravindh, S. Assa Dabare, Rukshima Gao, Xiangpeng Jiang, Zhong-Tao Wong, Kok Wai |
author_sort | Wickramaarachchi, Kethaki |
collection | PubMed |
description | 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 (ZnCl(2)) 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 m(2) g(−1)) than the GM carbon activated by ZnCl(2) (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 NaClO(4)), 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 R(2) of 0.98 for capacitance prediction. |
format | Online Article Text |
id | pubmed-9182344 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91823442022-06-10 Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models Wickramaarachchi, Kethaki Minakshi, Manickam Aravindh, S. Assa Dabare, Rukshima Gao, Xiangpeng Jiang, Zhong-Tao Wong, Kok Wai Nanomaterials (Basel) Article 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 (ZnCl(2)) 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 m(2) g(−1)) than the GM carbon activated by ZnCl(2) (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 NaClO(4)), 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 R(2) of 0.98 for capacitance prediction. MDPI 2022-05-27 /pmc/articles/PMC9182344/ /pubmed/35683703 http://dx.doi.org/10.3390/nano12111847 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/). |
spellingShingle | Article Wickramaarachchi, Kethaki Minakshi, Manickam Aravindh, S. Assa Dabare, Rukshima Gao, Xiangpeng Jiang, Zhong-Tao Wong, Kok Wai Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models |
title | Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models |
title_full | Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models |
title_fullStr | Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models |
title_full_unstemmed | Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models |
title_short | Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models |
title_sort | repurposing n-doped grape marc for the fabrication of supercapacitors with theoretical and machine learning models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182344/ https://www.ncbi.nlm.nih.gov/pubmed/35683703 http://dx.doi.org/10.3390/nano12111847 |
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