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Machine Learning-Based Detection of Graphene Defects with Atomic Precision
Defects in graphene can profoundly impact its extraordinary properties, ultimately influencing the performances of graphene-based nanodevices. Methods to detect defects with atomic resolution in graphene can be technically demanding and involve complex sample preparations. An alternative approach is...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer Singapore
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7770819/ https://www.ncbi.nlm.nih.gov/pubmed/34138207 http://dx.doi.org/10.1007/s40820-020-00519-w |