Scholarlink

<p>The convergence of artificial intelligence (AI) and carbon nanotube (CNT) chemistry is accelerating innovations in the synthesis, functionalization, and advanced applications of carbon-based nanomaterials. This review highlights recent AI-driven methodologies, including neural networks, ensemble learning, metaheuristics, and hybrid frameworks that are redefining the design, surface engineering, and structure–property relationships of CNTs. Special attention is given to their roles in clean energy technologies, polymer nanocomposites, environmental systems, and nanoelectronics. Advances such as autonomous synthesis guided by deep learning, high-throughput experimentation, and AI-enabled property prediction are critically reviewed. Challenges including data fragmentation, class imbalance, and lack of benchmarking are addressed, alongside future directions such as physics-informed machine learning, robotics integration, and multi-objective optimization. This review positions AI as a disruptive catalyst in advanced CNT research, offering intelligent automation and predictive insights across diverse carbon-material applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-driven strategies for the design and functionalization of carbon nanotubes: a critical review

  • 期刊论文

  • 发表日期：

    2026年3月12日

查看全文
  • Mohammad Mohammadi,
  • Yousef Bazargan Lari,
  • Farhang Daneshmand,
  • Seyed Mohammad Reza Nazemosadat

摘要

The convergence of artificial intelligence (AI) and carbon nanotube (CNT) chemistry is accelerating innovations in the synthesis, functionalization, and advanced applications of carbon-based nanomaterials. This review highlights recent AI-driven methodologies, including neural networks, ensemble learning, metaheuristics, and hybrid frameworks that are redefining the design, surface engineering, and structure–property relationships of CNTs. Special attention is given to their roles in clean energy technologies, polymer nanocomposites, environmental systems, and nanoelectronics. Advances such as autonomous synthesis guided by deep learning, high-throughput experimentation, and AI-enabled property prediction are critically reviewed. Challenges including data fragmentation, class imbalance, and lack of benchmarking are addressed, alongside future directions such as physics-informed machine learning, robotics integration, and multi-objective optimization. This review positions AI as a disruptive catalyst in advanced CNT research, offering intelligent automation and predictive insights across diverse carbon-material applications.