Addressing the Trade-Off between Crystallinity and Yield in Single-Walled Carbon Nanotube Forest Synthesis Using Machine Learning.
- DOI
- 10.1021/acsnano.3c07587
- Published
- 2023 Nov 28
- Container
- ACS nano
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acsnano.3c07587,
title = {Addressing the Trade-Off between Crystallinity and Yield in Single-Walled Carbon Nanotube Forest Synthesis Using Machine Learning.},
author = {Lin D and Muroga S and Kimura H and Jintoku H and Tsuji T and Hata K and Chen G and Futaba DN},
year = {2023},
journal = {ACS nano},
doi = {10.1021/acsnano.3c07587},
url = {https://doi.org/10.1021/acsnano.3c07587}
}RIS
TY - JOUR TI - Addressing the Trade-Off between Crystallinity and Yield in Single-Walled Carbon Nanotube Forest Synthesis Using Machine Learning. AU - Lin D AU - Muroga S AU - Kimura H AU - Jintoku H AU - Tsuji T AU - Hata K AU - Chen G AU - Futaba DN PY - 2023 JO - ACS nano DO - 10.1021/acsnano.3c07587 UR - https://doi.org/10.1021/acsnano.3c07587 ER -
APA
D, L., S, M., H, K., H, J., T, T., K, H., G, C., & DN, F. (2023). Addressing the Trade-Off between Crystallinity and Yield in Single-Walled Carbon Nanotube Forest Synthesis Using Machine Learning.. ACS nano. https://doi.org/10.1021/acsnano.3c07587
Source records
- pubmed · retrieved 2026-09-26T22:42:45.530Z