MRS Meetings and Events

 

CH01.08.01 2022 MRS Fall Meeting

Optimal Sampling Strategies for Controlled Dose In Situ and High Resolution Scanning Transmission Electron Microscopy (STEM)

When and Where

Dec 1, 2022
8:00am - 8:30am

Hynes, Level 1, Room 102

Presenter

Co-Author(s)

Beata Layla Mehdi1,Nigel Browning1,2,Alex Robinson1,Daniel Nicholls1,Jack Wells1,Amirafshar Moshtaghpour1,3,Angus Kirkland3,4

University of Liverpool1,Sivananthan Laboratories2,Rosalind Franklin Institute3,University of Oxford4

Abstract

Beata Layla Mehdi1,Nigel Browning1,2,Alex Robinson1,Daniel Nicholls1,Jack Wells1,Amirafshar Moshtaghpour1,3,Angus Kirkland3,4

University of Liverpool1,Sivananthan Laboratories2,Rosalind Franklin Institute3,University of Oxford4
For many imaging and microanalysis experiments using state-of-the-art aberration corrected scanning transmission electron microscopy (STEM), the resolution and precision of the final result is primarily determined by the tolerance of the sample to the applied electron beam dose. In the case of in-situ experiments, where the goal is to image a chemical or structural process as it evolves, the effect of the beam dose can be harder to unravel, as a change in structure/chemistry is the expectation of the experiment – when you are looking for a change in a complex experiment and do actually see one, it is natural to expect that the change you see is exactly the one you were looking for. However, if the dose is not controlled, the kinetics of the observations can be dramatically changed by the beam, leading to a different structure/chemistry than would be expected from an ex-situ experiment under similar reaction conditions. Recent results at the University of Liverpool have shown that the optimal solution for dose control in any form of transmission electron microscopy (TEM) is to form the image from discrete locations of a small electron beam separated by as far as possible in space and time. Instead of forming the image with an extended beam (as with TEM) or from a regular raster pattern (as in conventional STEM) this condition is satisfied ideally by moving the STEM probe over the area of the image using large jumps between the acquisition pixels. This form of STEM imaging presents numerous challenges to the stability of the microscope, but these stability issues can be routinely overcome using either a form of random walk scanning, a calibrated random scanning or a mixture of conventional scanning and rapid beam blanking. The larger than standard jumps between pixel acquisition locations in this methodology creates problems with image interpretation, as the gaps between locations of acquisition are missing information. Fortunately, we can use Inpainting (a form of compressive sensing) to retrieve the missing information and retrieve the full image of the sample. In this presentation, we will discuss the methodology behind the use of sub-sampling and Inpainting, with particular reference to the speed and efficiency of the reconstruction method and the potential for future real-time imaging. In addition, the use of simulations to provide a starting point for image interpretation and the use of deep learning approaches to allow the microscope to adapt its own imaging conditions to the sample being studied, will also be discussed. Finally, this complete integrated artificial intelligence work flow for image acquisition and processing ideally lends itself to integrated image analytics that can be used, for example, to quantify feature sizes and their rate of change directly from the sub-sampled datasets.

Keywords

chemical composition | crystallographic structure | morphology

Symposium Organizers

Dongsheng Li, Pacific Northwest National Laboratory
Qian Chen, University of Illinois at Urbana-Champaign
Yu Han, King Abdullah University of Science and Technology
Barnaby Levin, Direct Electron LP

Symposium Support

Bronze
King Abdullah University of Science and Technology
MilliporeSigma

Publishing Alliance

MRS publishes with Springer Nature