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Nanomaterial datasets to advance tomography in scanning transmission electron microscopy

Authors :
Héctor Abruña
David A Muller
Rui Xu
Colin Ophus
Jianwei Miao
Robert Hovden
Peter Ercius
Chien-Chun Chen
Barnaby David Aldington Levin
Elliot Padgett
Mary Cooper Scott
Wolfgang Theis
Yi Jiang
Yongsoo Yang
Haitao Zhang
Don-Hyung Ha
Deli Wang
Yingchao Yu
Richard Robinson
Lena Fitting Kourkoutis
Héctor Abruña
David A Muller
Rui Xu
Colin Ophus
Jianwei Miao
Robert Hovden
Peter Ercius
Chien-Chun Chen
Barnaby David Aldington Levin
Elliot Padgett
Mary Cooper Scott
Wolfgang Theis
Yi Jiang
Yongsoo Yang
Haitao Zhang
Don-Hyung Ha
Deli Wang
Yingchao Yu
Richard Robinson
Lena Fitting Kourkoutis
Publication Year :
2016

Abstract

Electron tomography in materials science has flourished with the demand to characterize nanoscale materials in three dimensions (3D). Access to experimental data is vital for developing and validating reconstruction methods that improve resolution and reduce radiation dose requirements. This work presents five high-quality scanning transmission electron microscope (STEM) tomography datasets in order to address the critical need for open access data in this field. The datasets represent the current limits of experimental technique, are of high quality, and contain materials with structural complexity. Included are tomographic series of a hyperbranched Co2P nanocrystal, platinum nanoparticles on a carbon nanofibre imaged over the complete 180o tilt range, a platinum nanoparticle and a tungsten needle both imaged at atomic resolution by equal slope tomography, and a through-focal tilt series of PtCu nanoparticles. A volumetric reconstruction from every dataset is provided for comparison and development of post-processing and visualization techniques. Researchers interested in creating novel data processing and reconstruction algorithms will now have access to state of the art experimental test data.

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.ocn953902080
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.6084.M9.FIGSHARE.C.2185342