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Accurate determination of the chiral indices of carbon nanotubes (CNTs) is crucial for their controllable synthesis and practical applications. Transmission electron microscopy (TEM) combined with electron diffraction has been one of the most reliable methods for characterizing the chiral indices of CNTs. However, it is still a challenge to analyze TEM images to extract chirality with high efficiency and accuracy, especially for nanotubes with a diameter larger than 2 nanometers. In this work, a Python code is developed with assistance from an artificial intelligence model (Claude Code) for the auto-extraction of CNTs’ chirality from TEM images. Precise and reliable measurement of the diameter was realized by using a new method to determine the average distance between the bright and dark fringes. Symmetry and geometry-based rules enabled measurements of layer lines in the Fourier transformation of TEM images with a sub-pixel resolution. As a result, an accuracy higher than 90% for CNTs with diameters ranging from 0.5 nm to 3.0 nm was achieved. High robustness has been validated by experimentally analyzing the chiralities of CNTs grown by a floating catalyst chemical vapor deposition method. The open-source codes and tools will be valuable for the research community to quantitatively identify the chirality distribution of CNTs with high efficiency.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: carbon nanotubes, chirality, AI for science, TEM, vibe coding

刊行年月日: 2026-07-20

出版者: Informa UK Limited

掲載誌:

• Science and Technology of Advanced Materials (ISSN: 14686996) 2705816

研究助成金:

• JSPS Kakenhi JP25820336
• JST-FOREST Program JPMJFR223T
• WPI-MANA Challenging Research Program (CRP)
• NIMS Support system for curiosity-driven research program
• Ministry of Education, Culture, Sports, Science and Technology JPMXP1225NM5167

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.6427

公開URL: https://doi.org/10.1080/14686996.2026.2705816

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更新時刻: 2026-07-27 09:50:32 +0900

MDRでの公開時刻: 2026-07-27 12:27:57 +0900