LLNL’s novel approach utilizes a number of techniques to improve reconstruction accuracy:
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![A cold-spray chamber is shown during deposition, with the nozzle at the top of the image and a near-full density sample being fabricated in the center. Particles of the brittle thermoelectric bismuth telluride are accelerated to more than 900 meters per second, or almost Mach 3, in inert gas and directed onto a copper surface, laying down the strips that form the basis of a functioning thermoelectric generator to harvest waste heat. Graphic by Jacob Long/LLNL](/sites/default/files/styles/scale_exact_400x400_/public/2021-02/Cold%20Spray_875x500px.jpg?itok=hjM9UrWO)
![graphic_of_simulation.png graphic_of_simulation](/sites/default/files/styles/scale_exact_400x400_/public/2019-08/graphic_of_simulation.png?itok=eyhMWp8B)
![Intensification of laser in simulations and electrons being accelerated](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/intensification%20of%20laser%20in%20simulations%20and%20electrons%20being%20accelerated_875x500px.jpg?itok=bdZS_mHA)
LLNL pioneered the use of tomographic reconstruction to determine the power density of electron beams using profiles of the beam taken at a number of angles. LLNL’s earlier diagnostic consisted of a fixed number of radially oriented sensor slits and required the beam to be circled over them at a fixed known diameter to collect data. The new sensor design incorporates annular slits instead,…
![CAL Computed Axial Lithography](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/CAL%20computed-axial-lithography-780x470.jpg?itok=UHejtTl0)
LLNL has developed a system and method that accomplishes volumetric fabrication by applying computed tomography (CT) techniques in reverse, fabricating structures by exposing a photopolymer resin volume from multiple angles, updating the light field at each angle. The necessary light fields are spatially and/or temporally multiplexed, such that their summed energy dose in a target resin volume…
![Machine Learning for Monitoring microfluidic microcapsules](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/Machine%20Learning%20for%20Monitoring%20microfluidic%20microcapsules%20875_0.jpg?itok=cLdsZh03)