| 研究課題/領域番号 |
23K26489
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| 補助金の研究課題番号 |
23H01796 (2023)
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| 研究種目 |
基盤研究(B)
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| 配分区分 | 基金 (2024)
補助金 (2023) |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分28020:ナノ構造物理関連
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| 研究機関 | 国立研究開発法人物質・材料研究機構 |
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研究代表者 |
湯 代明 国立研究開発法人物質・材料研究機構, ナノアーキテクトニクス材料研究センター, 主幹研究員 (50646271)
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| 研究分担者 |
石原 伸輔 国立研究開発法人物質・材料研究機構, ナノアーキテクトニクス材料研究センター, 主幹研究員 (30644067)
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| 研究期間 (年度) |
2023-04-01 – 2027-03-31
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| 研究課題ステータス |
交付 (2024年度)
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| 配分額 *注記 |
18,980千円 (直接経費: 14,600千円、間接経費: 4,380千円)
2026年度: 3,380千円 (直接経費: 2,600千円、間接経費: 780千円)
2025年度: 2,470千円 (直接経費: 1,900千円、間接経費: 570千円)
2024年度: 2,210千円 (直接経費: 1,700千円、間接経費: 510千円)
2023年度: 10,920千円 (直接経費: 8,400千円、間接経費: 2,520千円)
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| キーワード | Carbon nanotubes / Molecular junction / Electron microscopy / Chirality transformation / Gas sensor / Carbon nanotube / Machine learning / carbon nanotube / chirality / molecular junction / transport properties / quantum sensors |
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| 研究開始時の研究の概要 |
We aim to develop gas sensors for individual molecules, based on CNT molecular junction transistors, by elucidating the chirality transformation mechanism, investigating the transport properties, and studying the electrochemical properties interacting with molecular adsorbents.
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| 研究実績の概要 |
Carbon nanotubes (CNTs) are promising for gas sensors at molecular level, because of the extraordinary electrical properties associated with the unique one-dimensional geometry. Depending on the helical structure, so called “chirality”, CNTs are either metallic or semiconducting. Such chirality-dependence has been recognized as an inherent and permanent property, until recently, we realized chirality transformation of a metallic CNT, resulting in an ultrashort semiconducting nanotube channel within a metallic nanotube to form a molecular junction.
In this fiscal year, we have made progresses in (1) controlled growth of CNTs assisted by machine learning; (2) development of advanced 4D-STEM method and revealed the stability map and transformation routes during the manipulation of the chirality structures.
In the next steps, the chirality transformation mechanisms will be quantitatively investigated by using MEMS chips. Machine learning will be adopted for predictive fabrication of CNT molecular junctions. And ultimately, CNT junction based sensors will be developed for the detection of individual molecules in real-time. This research will not only provide fundamental insights into molecular dynamics and electronics, but also pave the way to the development of quantum sensors with real-time responses to individual molecules.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
The purposes of the project include (1) elucidate the CNT chirality transformation mechanism at atomic level, (2) to precisely control the local CNT chirality, and (3) to understand the transport properties of the CNT junction transistors. The plan for FY2023 is mainly to investigate the chirality transformation mechanism. We have made progresses in (1) machine learning-assisted growth of single walled CNTs, (2) quantitative investigation of the chirality transformation process by advanced in situ and 4D-STEM.
CNTs with controlled structures are the basis for fabricating CNT molecule junction devices. We developed a high-throughput strategy to investigate the statistical patterns in catalyst activity and selective growth of SWCNTs. Statistical patterns of the yield, quality and selectivity are associated with the alloy composition, revealing a negative correlation between the yield and enrichment of s-SWCNTs. Semiconducting SWCNTs with a purity higher than 90% were obtained (Carbon 2023, 118073).
Our original concept of “chirality engineering” has been widely accepted by the community. In an invited review, we summarized approaches to control global and local CNT chiralities by growth, separation, and transformation. We discussed opportunities and challenges for chirality engineering towards surpassing the performance of conventional electronic devices, and development of unconventional CNT quantum electronics (Nature Reviews Electrical Engineering 2024, 1 (3), 149-162).
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| 今後の研究の推進方策 |
To fulfill the goals of the project to understand the CNT chirality transformation mechanism, and to establish the dependence of transport properties on the atomic structure, and finally to develop quantum sensors based on the electrochemical interactions of CNT junctions and gas molecules, future plan includes (1) quantitative investigation of chirality transformation mechanism by MEMS devices and simulation at multiple length scales, (2) predictable chirality transformation and fabrication of CNT junction devices by applying machine learning, (3) fabrication of CNT junction devices on chips to study the transport properties and electrochemical properties with the presence of gas molecules.
Specifically, the deformation of individual CNTs under mechanical stress, temperature and electron irradiation will be realized by developing MEMS chips and the process will be monitored by in situ TEM observations. The atomic structure and chirality of CNTs will be characterized by Cs-corrected TEM imaging and electron diffraction. The temperature of the Joule heated CNTs will be measured by STEM-EELS using the shift of plasmon peaks. For machine learning modelling, input parameters will include the structure of the original and the resulting nanotube, the electrical properties of the original nanotube, and processing parameters. The resulted nanotube chirality will be the label of the target. After training, the model will be used to predict the processing parameters for the desired chirality transformations.
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