{"title":"Machine Learning as a \"Catalyst\" for Advancements in Carbon Nanotube Research.","authors":"Guohai Chen, Dai-Ming Tang","doi":"10.3390/nano14211688","DOIUrl":null,"url":null,"abstract":"<p><p>The synthesis, characterization, and application of carbon nanotubes (CNTs) have long posed significant challenges due to the inherent multiple complexity nature involved in their production, processing, and analysis. Recent advancements in machine learning (ML) have provided researchers with novel and powerful tools to address these challenges. This review explores the role of ML in the field of CNT research, focusing on how ML has enhanced CNT research by (1) revolutionizing CNT synthesis through the optimization of complex multivariable systems, enabling autonomous synthesis systems, and reducing reliance on conventional trial-and-error approaches; (2) improving the accuracy and efficiency of CNT characterizations; and (3) accelerating the development of CNT applications across several fields such as electronics, composites, and biomedical fields. This review concludes by offering perspectives on the future potential of integrating ML further into CNT research, highlighting its role in driving the field forward.</p>","PeriodicalId":18966,"journal":{"name":"Nanomaterials","volume":"14 21","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11547478/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nanomaterials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.3390/nano14211688","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The synthesis, characterization, and application of carbon nanotubes (CNTs) have long posed significant challenges due to the inherent multiple complexity nature involved in their production, processing, and analysis. Recent advancements in machine learning (ML) have provided researchers with novel and powerful tools to address these challenges. This review explores the role of ML in the field of CNT research, focusing on how ML has enhanced CNT research by (1) revolutionizing CNT synthesis through the optimization of complex multivariable systems, enabling autonomous synthesis systems, and reducing reliance on conventional trial-and-error approaches; (2) improving the accuracy and efficiency of CNT characterizations; and (3) accelerating the development of CNT applications across several fields such as electronics, composites, and biomedical fields. This review concludes by offering perspectives on the future potential of integrating ML further into CNT research, highlighting its role in driving the field forward.
由于碳纳米管（CNTs）的生产、加工和分析本身具有多重复杂性，因此其合成、表征和应用长期以来一直面临着巨大挑战。机器学习（ML）的最新进展为研究人员应对这些挑战提供了新颖而强大的工具。本综述探讨了机器学习在碳纳米管研究领域的作用，重点关注机器学习如何通过以下方式促进碳纳米管研究：(1) 通过优化复杂的多变量系统彻底改变碳纳米管合成，实现自主合成系统，减少对传统试错方法的依赖；(2) 提高碳纳米管表征的准确性和效率；(3) 加快碳纳米管在电子、复合材料和生物医学等多个领域的应用开发。本综述最后展望了将 ML 进一步融入 CNT 研究的未来潜力，强调了 ML 在推动该领域发展方面的作用。
期刊介绍：
Nanomaterials (ISSN 2076-4991) is an international and interdisciplinary scholarly open access journal. It publishes reviews, regular research papers, communications, and short notes that are relevant to any field of study that involves nanomaterials, with respect to their science and application. Thus, theoretical and experimental articles will be accepted, along with articles that deal with the synthesis and use of nanomaterials. Articles that synthesize information from multiple fields, and which place discoveries within a broader context, will be preferred. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental or methodical details, or both, must be provided for research articles. Computed data or files regarding the full details of the experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material. Nanomaterials is dedicated to a high scientific standard. All manuscripts undergo a rigorous reviewing process and decisions are based on the recommendations of independent reviewers.
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