JOURNAL ARTICLE

Improving Data Quality in Traditional Low‐Dose Scanning Transmission Electron Microscopy Imaging.

  • Published In: Particle & Particle Systems Characterization, 2023, v. 40, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Lingling; Jiang, Yilan; Zhou Yi; Shi, Ruikai; Hosokawa, Fumio; Terasaki, Osamu; Zhang, Qing 3 of 3

Abstract

It has become very important to study and find optimal conditions for imaging electron‐beam (e‐beam) sensitive materials in scanning transmission electron microscopy under low electron‐dose with high signal‐to‐noise ratio (SNR). Convergence and collection angles and electron‐probe current are essential parameters. However, these parameters have rarely been discussed in a systematic way. In this paper, the illumination and collection conditions are optimized according to the resolution requirement of different materials by adjusting the condenser and intermediate lenses in a commercial transmission electron microscope. To demonstrate the significance of optimizing these parameters, two examples, zeolite MFI and metal–organic framework (MOF) MIL‐101, are taken among the sensitive materials, with the most important electron incidences along the [010] and <110> directions, respectively. High SNR atomic resolution images of MFI are obtained with e‐beam current as low as 0.50 pA, reaching information transfer for reflection up to 18 0 2 corresponding to d‐spacing of 0.11 nm, close to the resolution limit of 0.098 nm from resolvable diffraction limit. MOF MIL‐101 is characterized under an even lower e‐beam 0.2 pA to avoid severe beam damage. High‐quality annular dark and bright field images are obtained, which proves the wide applicability of this method on more e‐beam sensitive materials. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Particle & Particle Systems Characterization. 2023/01, Vol. 40, Issue 1, p1
  • Document Type:Article
  • Subject Area:Biography
  • Publication Date:2023
  • ISSN:0934-0866
  • DOI:10.1002/ppsc.202200122
  • Accession Number:161471835
  • Copyright Statement:Copyright of Particle & Particle Systems Characterization is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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