Transmission electron microscope images dataset for AutoDetect-mNP (triangular prisms)

Wang, Xingzhi, University of California, Berkeley, https://orcid.org/0000-0003-1107-8084

Li, Jie, University of California, Berkeley, https://orcid.org/0000-0002-4727-1786

Ha, Hyun Dong, University of California, Berkeley, https://orcid.org/0000-0001-7531-0757

Dahl, Jakob, University of California, Berkeley

Head-Gordon, Teresa, University of California, Berkeley, https://orcid.org/0000-0003-0025-8987

Alivisatos, Paul, University of California, Berkeley

xingzhi_wang@berkeley.edu, jerry-li1996@berkeley.edu, hyundong.ha@berkeley.edu, jakobd@berkeley.edu, thg@berkeley.edu, paul.alivisatos@berkeley.edu

Published Apr 07, 2021 on Dryad. https://doi.org/10.6078/D1S12H

Cite this dataset

Wang, Xingzhi et al. (2021). Transmission electron microscope images dataset for AutoDetect-mNP (triangular prisms) [Dataset]. Dryad. https://doi.org/10.6078/D1S12H

Abstract

AutoDetect-mNP is an unsupervised algorithm capable of detecting and segmenting particles from bright-field TEM images and classifying the particles based on their shapes, requiring minimum human intervention in the process. The algorithm was validated on TEM images of 4 different Au nanoparticle systems (nanorods with ~5 aspect ratio, nanorods with ~2 aspect ratio, mixture of the two nanorods, triangular nanoprisms). This dataset contains the unprocessed TEM images of the triangular prisms. For details regarding the data collection process, please refer to https://doi.org/10.1021/jacsau.0c00030.

Funding

United States Department of Energy, Award: DE-AC02-05-CH11231

National Institute of General Medical Sciences, Award: 5U01GM121667