Don’t miss out on the discounted registration fee for M&M2021! The Early Bird registration rate deadline was extended to June 15, 2021. Register by this Tuesday and save money at all registration levels. Register here for M&M 2021!
The Major Awards of the Society honor distinguished scientific contributions to the field of microscopy and microanalysis by technologists and by scientists at various career stages, as well as distinguished service to the Society. The honor will be conferred at the upcoming Microscopy & Microanalysis 2021 (M&M 2021) meeting. Please congratulate this year’s awards […]
X-12 Short Course: Guidelines for Performing 4D-STEM Characterization from the Atomic to >Micrometer Scales: Experimental Considerations, Data Analysis and Simulation
August 1, 20218:30 am - 5:30 pm
Sunday Short Course at M&M2021 (Virtual)
LEAD INSTRUCTORS: David Muller, Cornell University Colin Ophus, Lawrence Berkeley National Laboratory
With modern electron detector technology, it is now possible to record full images of a converged STEM probe while scanning it over the sample surface, resulting in a 4D-STEM dataset. Because the atomic-scale scattering information contained in an atomic-scale STEM probe is decoupled from the step size between STEM probe positions, 4D-STEM can be used for experiments ranging from sub-Angstrom resolution phase contrast imaging to statistical characterization of functional materials over large length scales. In this course, we will give tutorials on how to perform 4D-STEM experiments, analyze the (potentially very large!) resulting datasets, and perform 4D-STEM simulations.
X-15 Short course: Data Analysis in Materials Science
August 1, 20218:30 am - 5:30 pm
Sunday Short Course at M&M 2021 (Virtual)
LEAD INSTRUCTORS: Eric Prestat, University of Manchester and SuperSTEM Laboratory, United Kingdom Joshua Taillon, National Institute of Standards and Technology
This short course will introduce the use of HyperSpy and related Python libraries (atomap, pixStem, pyXem) for analysis of microscopy datasets. No prior Python knowledge is required. Attendees will learn how to perform basic machine learning, multi-dimensional curve fitting for EELS and EDS quantification, atomic resolution image analysis and big data processing (such as 4D STEM) on desktop computers.
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