0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessSignificance Liquid cell transmission electron microscopy (LCTEM) is an emerging technique, which enables nanoscale visualization and tracking of single nanoparticles near interfaces with unprecedented spatial resolution. Here, we studied the diffusion of nanoparticles in LCTEM experiments using techniques powered by deep neural networks and statistical tests. We observed two underlying regimes of diffusive behavior which are governed by the interaction of the electron beam, the nanoparticle, the nearby substrate, and the liquid environment. This understanding forms the foundation to use LCTEM for single-nanoparticle tracking for a broad range of nanoparticles, interfaces, and liquids.
Vida Jamali, Cory Hargus, Assaf Ben‐Moshe, Amirali Aghazadeh, Hyun-Dong Ha, Kranthi K. Mandadapu, Paul Alivisatos, Vida Jamali, Cory Hargus, Assaf Ben‐Moshe, Amirali Aghazadeh, Hyun-Dong Ha, Kranthi K. Mandadapu, Paul Alivisatos (2021). Anomalous nanoparticle surface diffusion in LCTEM is revealed by deep learning-assisted analysis. , 118(10), DOI: https://doi.org/10.1073/pnas.2017616118.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2021
Authors
14
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1073/pnas.2017616118
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access