STROBE Research Slices: Magnetic Materials
Imaging Magnetic Materials: Structure and Function. Practical solutions to complex imaging challenges, from table-top to facility scale experiments. Learn more about STROBE research on magnetic materials!
Congrats to Madison Jilek for Receiving a 2020 NSF Graduate Research Fellowship
Congratulations to Madison Jilek for being awarded a 2020 National Science Foundation Graduate Research Fellowship at CU Boulder in the Dukovic research group.
Congrats to Steven Zeltmann for Receiving a Microscopy & Microanalysis Student Scholar Award
Steven Zeltmann received the 2020 Microscopy & Microanalysis Conference Student Abstract Award for the August 2020 M&M Conference.
Congrats to Jorge Nicolás (Nico) Hernández Charpak for Receiving the 2020 University of Colorado President’s Diversity Award
Jorge Nicolás (Nico) Hernández Charpak received the 2020 University of Colorado President’s Diversity Award. He was nominated by the STROBE SUPER team. This annual award recognizes significant achievements of faculty*, staff, students, and academic or administrative units in developing a culturally and intellectually diverse university community reflective of inclusive excellence.
Married CU Boulder physicists claim prestigious honor
Two scientists at the University of Colorado Boulder, Professor Henry Kapteyn and Professor Margaret Murnane, a married couple and partners in physics research, have been awarded the 2020 Benjamin Franklin Medal in Physics by the Franklin Institute. lt is one of several awards given out yearly by the institute. In its 196th year, the Franklin Institute continues to pay tribute to its namesake, Benjamin Franklin, by honoring the greatest minds in science. “The Franklin Institute Awards pay tribute to America’s original scientist, Benjamin Franklin, by honoring the greatest minds in science, engineering, and industry,” said Chris Franklin, chair of the Awards Corporate Committee, in a statement. “We believe in the work the Institution does to inspire a passion for learning about science and technology.” Professor Margret Murnane believes that sharing the honor with her husband is one of the best parts about winning the award.
Capturing the First Picture of a Black Hole & Beyond
This talk will present the methods and procedures used to produce the first image of a black hole from the Event Horizon Telescope, as well as future developments. It had been theorized for decades that a black hole would leave a “shadow” on a background of hot gas. Taking a picture of this black hole shadow would help to address a number of important scientific questions, both on the nature of black holes and the validity of general relativity. Unfortunately, due to its small size, traditional imaging approaches require an Earth-sized radio telescope. In this talk, I discuss techniques the Event Horizon Telescope Collaboration has developed to photograph a black hole using the Event Horizon Telescope, a network of telescopes scattered across the globe. Imaging a black holeʼs structure with this computational telescope required us to reconstruct images from sparse measurements, heavily corrupted by atmospheric error. This talk will summarize how the data from the 2017 observations were calibrated and imaged, and explain some of the challenges that arise with a heterogeneous telescope array like the EHT. The talk will also discuss how we are developing machine learning methods to help design future telescope arrays.
Katie Bouman is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. Before joining Caltech, she was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics. She received her Ph.D. in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT in EECS. Before coming to MIT, she received her bachelor’s degree in Electrical Engineering from the University of Michigan. The focus of her research is on using emerging computational methods to push the boundaries of interdisciplinary imaging.
UCLA-led research team produces most accurate 3D images of ‘2D materials’
Scientists develop innovative technique to pinpoint coordinates of single atoms. A UCLA-led research team has produced in unprecedented detail experimental three-dimensional maps of the atoms in a so-called 2D material — matter that isn’t truly two-dimensional but is nearly flat because it’s arranged in extremely thin layers, no more than a few atoms thick. Although 2D-materials–based technologies have not yet been widely used in commercial applications, the materials have been the subject of considerable research interest. In the future, they could be the basis for semiconductors in ever smaller electronics, quantum computer components, more-efficient batteries, or filters capable of extracting freshwater from saltwater.
A 3D map of atoms in 2D materials
Scanning atomic electron tomography measurements reveal the 3D local structure around single dopant atoms in 2D transition metal dichalcogenides, providing essential information to investigate and predict their electronic properties.
Naomi Ginsberg receives ACS early-career award in experimental physical chemistry
The American Society of Chemistry (ACS) has announced Naomi S. Ginsberg is a recipient of the 2020 early-career award(link is external) in experimental physical chemistry. She is being recognized “For the development of new time- and space-resolved imaging and spectroscopy methods to study dynamical phenomena in heterogeneous materials”.
Towards Automated Information Extraction from High Resolution Transmission Electron Microscopy Images
Transmission electron microscopy (TEM) is the characterization method of choice to observe the atomic-scale and microstructural local features within materials that play a critical role in material performance. However, a bottleneck exists between image acquisition and the extraction of relevant information that can be used in a materials design feedback loop. While image analysis of individual images can easily identify regions of interest and determine whether they contain defects, it is prohibitively time-consuming to manually perform this analysis on large numbers of images. Advances in machine learning and computer vision have made high accuracy automated image interpretation possible. Here, we present application of machine learning and other high-throughput methods to TEM images for nanoparticle identification and microstructural characterization.