Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

Abstract Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too va...

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Autores principales: Taher Hajilounezhad, Rina Bao, Kannappan Palaniappan, Filiz Bunyak, Prasad Calyam, Matthew R. Maschmann
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/7cbc035ed72342a18a4f677e7c46a9ae
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