Literature DB >> 28689081

High-throughput, semi-automated quantitative STEM mass measurement of supported metal nanoparticles using a conventional TEM/STEM.

Stephen D House1, Yuxiang Chen2, Rongchao Jin2, Judith C Yang3.   

Abstract

The adaptation of quantitative STEM techniques to enable atom-counting in supported metal nanoparticles with a modern, conventional (non-aberration-corrected) TEM/STEM (a JEOL JEM2100F) without the need for any modifications or special hardware is presented. No image simulation is required, either. This technique enables the practical analysis of the size, mass, and basic shape information of statistically robust populations of hundreds to thousands of nanoparticles. The methods for performing the necessary calibrations of the microscope and images are detailed. A user-friendly semi-automated analysis program was also written to facilitate high throughput. The program optimizes the analysis parameters, applying the procedure consistently across the entire dataset, enhancing the meaningfulness of the statistics as well as the reproducibility and transferability of the results. A series of atomically precise Au nanoparticles were used to validate the technique, which was determined to be accurate within a (nearly uniform) scaling factor of around two for the given instrument, and could be brought into better agreement with a calibration standard. The magnitude of the disparity was found to significantly and unexpectedly rely on the chosen magnification and spot size, the underlying reasons for which are unclear and likely instrument-dependent. The possible sources of error from the calibration and acquisition were examined and their impact on the accuracy and precision of quantification were estimated. The scattering cross-sections measured using this technique are relatively insensitive to moderate errors in the various detector calibrations but particularly sensitive to pixel size error.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Atom counting; HAADF; Mass measurement; Quantitative STEM; STEM; nanoparticles

Year:  2017        PMID: 28689081     DOI: 10.1016/j.ultramic.2017.07.004

Source DB:  PubMed          Journal:  Ultramicroscopy        ISSN: 0304-3991            Impact factor:   2.689


  2 in total

1.  AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles.

Authors:  Xingzhi Wang; Jie Li; Hyun Dong Ha; Jakob C Dahl; Justin C Ondry; Ivan Moreno-Hernandez; Teresa Head-Gordon; A Paul Alivisatos
Journal:  JACS Au       Date:  2021-02-25

2.  Deep learning detection of nanoparticles and multiple object tracking of their dynamic evolution during in situ ETEM studies.

Authors:  Khuram Faraz; Thomas Grenier; Christophe Ducottet; Thierry Epicier
Journal:  Sci Rep       Date:  2022-02-15       Impact factor: 4.379

  2 in total

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