X-ray CT Scans - Bats - Set 4
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
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A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of specimens from the Denver Museum of Nature & Science.
A classic scan solenoid can establish that the injector meets emittance requirements. Incorporating an aperture plate can validate predictions of much lower emittance. Measurements of beam parameters on the injector and ITS can inform predictions for the full LIA. Injector beam data is needed to tune the full LIA for initial turn-on and operation. Emittance measurements on the Scorpius injector are difficult because the beam is space-charge dominated. Emittance measurements on Scorpius injector and ITS will need large beam size to prevent overheating.
Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray Computed Tomography (CT) machines enable volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) are shown to accurately segment full resolution CT scans of thermal sprayed materials and provide maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of Myotis keaysi pilosotibialis specimens from Texas A&Ms Biodiveristy Research and Teaching Collections.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
A collection of x-ray computed tomography scans of specimens from the Museum of Southwestern Biology.
The initial goal of our 4-dimensional scanning transmission electron microscopy (4D-STEM)-based project was to develop strain resolution two orders of magnitude better than what is now currently possible with electron-based scattering techniques, all while collecting scattering information from 7 different tilt axes at one time [multi-beam electron diffraction (MBED)1 ] through the development of a new electron probe-forming aperture with non-circular features (patterned probes 2 ). We set out to accomplish this through a collaboration with Drs. Colin Ophus and Ben Savitsky at Lawrence Berkeley Laboratory (they are the world-leading experts in developing the complex computational codes required to perform orientation analysis and quantitative strain mapping on our 4D-STEM data sets. We are motivated to invest in this area as it will be the only technique sensitive enough to perform three- dimensional automated crystallographic orientation mapping (ACOM) and strain mapping for materials exposed to external stimulus (a focus of our larger efforts).