{"title":"掺杂 N 的多孔生物炭电极电容性能的机器学习建模及实验验证","authors":"","doi":"10.1016/j.renene.2024.120969","DOIUrl":null,"url":null,"abstract":"<div><p>N-doped porous biochar is considered as a promising carbon material for supercapacitor electrodes application. However, the intrinsic relations and effect mechanisms of the pore structure and N-doping to the capacitive performance are still inscrutable, giving rise to the challenges for enhancing the capacitive performance by regulating the physicochemical properties of N-doped biochar. In this study, various machine learning models were established to predict the specific capacitance of N-doped biochar electrodes based on the pore structure and N-doping properties. The effect mechanisms of pore structure and N-doping to the specific capacitance were also explored. Results showed that Random Forest model predicted the specific capacitance most accurately. The generalization performance of the model was verified to be quite well with our experiments. It is suggested that developing pore structure with abundant micropores plays more important role than N-doping in enhancing the specific capacitance. The optimal interval of each physiochemical property of N-doped biochar were also determined to maximize the specific capacitance. Furthermore, synergistic effects of pore structure and N-doping to the specific capacitance were revealed. This study provides a useful guideline for N-doped porous biochar production with the aim of capacitive performance enhancement.</p></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":null,"pages":null},"PeriodicalIF":9.0000,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning modeling of the capacitive performance of N-doped porous biochar electrodes with experimental verification\",\"authors\":\"\",\"doi\":\"10.1016/j.renene.2024.120969\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>N-doped porous biochar is considered as a promising carbon material for supercapacitor electrodes application. However, the intrinsic relations and effect mechanisms of the pore structure and N-doping to the capacitive performance are still inscrutable, giving rise to the challenges for enhancing the capacitive performance by regulating the physicochemical properties of N-doped biochar. In this study, various machine learning models were established to predict the specific capacitance of N-doped biochar electrodes based on the pore structure and N-doping properties. The effect mechanisms of pore structure and N-doping to the specific capacitance were also explored. Results showed that Random Forest model predicted the specific capacitance most accurately. The generalization performance of the model was verified to be quite well with our experiments. It is suggested that developing pore structure with abundant micropores plays more important role than N-doping in enhancing the specific capacitance. The optimal interval of each physiochemical property of N-doped biochar were also determined to maximize the specific capacitance. Furthermore, synergistic effects of pore structure and N-doping to the specific capacitance were revealed. This study provides a useful guideline for N-doped porous biochar production with the aim of capacitive performance enhancement.</p></div>\",\"PeriodicalId\":419,\"journal\":{\"name\":\"Renewable Energy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.0000,\"publicationDate\":\"2024-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Renewable Energy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0960148124010371\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Renewable Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0960148124010371","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
摘要
掺杂 N 的多孔生物炭被认为是超级电容器电极应用中一种前景广阔的碳材料。然而,孔隙结构和掺杂 N 对电容性能的内在关系和影响机制仍不明确,这给通过调节掺杂 N 生物炭的理化性质来提高电容性能带来了挑战。本研究建立了多种机器学习模型,根据孔隙结构和掺氮特性预测掺氮生物炭电极的比电容。研究还探讨了孔隙结构和 N 掺杂对比值电容的影响机制。结果表明,随机森林模型能最准确地预测比电容。实验也验证了该模型的泛化性能相当不错。结果表明,在提高比电容方面,发展具有丰富微孔的孔隙结构比掺 N 更重要。此外,还确定了掺 N 生物炭各理化性质的最佳区间,以最大限度地提高比电容。此外,研究还揭示了孔隙结构和掺 N 对比值电容的协同效应。这项研究为掺 N 多孔生物炭的生产提供了有用的指导,目的是提高电容性能。
Machine learning modeling of the capacitive performance of N-doped porous biochar electrodes with experimental verification
N-doped porous biochar is considered as a promising carbon material for supercapacitor electrodes application. However, the intrinsic relations and effect mechanisms of the pore structure and N-doping to the capacitive performance are still inscrutable, giving rise to the challenges for enhancing the capacitive performance by regulating the physicochemical properties of N-doped biochar. In this study, various machine learning models were established to predict the specific capacitance of N-doped biochar electrodes based on the pore structure and N-doping properties. The effect mechanisms of pore structure and N-doping to the specific capacitance were also explored. Results showed that Random Forest model predicted the specific capacitance most accurately. The generalization performance of the model was verified to be quite well with our experiments. It is suggested that developing pore structure with abundant micropores plays more important role than N-doping in enhancing the specific capacitance. The optimal interval of each physiochemical property of N-doped biochar were also determined to maximize the specific capacitance. Furthermore, synergistic effects of pore structure and N-doping to the specific capacitance were revealed. This study provides a useful guideline for N-doped porous biochar production with the aim of capacitive performance enhancement.
期刊介绍:
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