Cargando…
Machine Learning to Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos
[Image: see text] Liquid-phase transmission electron microscopy (TEM) has been recently applied to materials chemistry to gain fundamental understanding of various reaction and phase transition dynamics at nanometer resolution. However, quantitative extraction of physical and chemical parameters fro...
Autores principales: | Yao, Lehan, Ou, Zihao, Luo, Binbin, Xu, Cong, Chen, Qian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2020
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453571/ https://www.ncbi.nlm.nih.gov/pubmed/32875083 http://dx.doi.org/10.1021/acscentsci.0c00430 |
Ejemplares similares
-
Real-space imaging of nanoparticle transport and interaction dynamics by graphene liquid cell TEM
por: Kang, Sungsu, et al.
Publicado: (2021) -
Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images
por: Wen, Haotian, et al.
Publicado: (2021) -
Metrology of convex-shaped nanoparticles via soft classification machine learning of TEM images
por: Wen, Haotian, et al.
Publicado: (2021) -
Imaging how thermal capillary waves and anisotropic interfacial stiffness shape nanoparticle supracrystals
por: Ou, Zihao, et al.
Publicado: (2020) -
Complex
Dispersion of Detonation Nanodiamond Revealed
by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular
Dynamics Simulations
por: Kuschnerus, Inga C., et al.
Publicado: (2023)