LLNL’s novel approach utilizes a number of techniques to improve reconstruction accuracy:
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- Additive Manufacturing (37)
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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,…
![Nanoporus gold](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/nanoporus%20gold%20875x500.jpg?itok=A0gFmVPT)
By combining 3D printing and dealloying., researchers at LLNL have developed a method for fabricating metal foams with engineered hierarchical architectures consisting of pores at least 3 distinct length scales. LLNL’s method uses direct ink writing (DIW), a 3D printing technique for additive manufacturing to fabricate hierarchical nanoporous metal foams with deterministically controlled 3D…
![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)