Interpreting nanovoids in atom probe tomography data for accurate local compositional measurements

Xing Wang (), Constantinos Hatzoglou, Brian Sneed, Zhe Fan, Wei Guo, Ke Jin, Di Chen, Hongbin Bei, Yongqiang Wang, William J. Weber, Yanwen Zhang, Baptiste Gault, Karren L. More, Francois Vurpillot and Jonathan D. Poplawsky ()
Additional contact information
Xing Wang: Oak Ridge National Laboratory
Constantinos Hatzoglou: Groupe de Physique des Matériaux
Brian Sneed: Oak Ridge National Laboratory
Zhe Fan: Oak Ridge National Laboratory
Wei Guo: Oak Ridge National Laboratory
Ke Jin: Oak Ridge National Laboratory
Di Chen: Los Alamos National Laboratory
Hongbin Bei: Oak Ridge National Laboratory
Yongqiang Wang: Los Alamos National Laboratory
William J. Weber: Oak Ridge National Laboratory
Yanwen Zhang: Oak Ridge National Laboratory
Baptiste Gault: Max-Planck-Institut für Eisenforschung
Karren L. More: Oak Ridge National Laboratory
Francois Vurpillot: Groupe de Physique des Matériaux
Jonathan D. Poplawsky: Oak Ridge National Laboratory

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract Quantifying chemical compositions around nanovoids is a fundamental task for research and development of various materials. Atom probe tomography (APT) and scanning transmission electron microscopy (STEM) are currently the most suitable tools because of their ability to probe materials at the nanoscale. Both techniques have limitations, particularly APT, because of insufficient understanding of void imaging. Here, we employ a correlative APT and STEM approach to investigate the APT imaging process and reveal that voids can lead to either an increase or a decrease in local atomic densities in the APT reconstruction. Simulated APT experiments demonstrate the local density variations near voids are controlled by the unique ring structures as voids open and the different evaporation fields of the surrounding atoms. We provide a general approach for quantifying chemical segregations near voids within an APT dataset, in which the composition can be directly determined with a higher accuracy than STEM-based techniques.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-020-14832-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14832-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-14832-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().