Science and Technology Highlights

Vice President Sang Yup Lee from the Korea Advanced Institute of Science and Technology (KAIST) and Glenn Fox, principal associate director at LLNL, signed a memorandum of understanding in June 2024, to collaborate on basic science research regarding hydrogen and other carbon-neutral technologies.
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LLNL leaders and the Korea Advanced Institute of Science and Technology (KAIST) sign a memorandum of understanding to expand collaborations related to hydrogen and other low-carbon energy technology.

From left, Daniela Cusak, LLNL’s Karis McFarlane and Andy Nottingham take soil samples from a rainforest.
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LLNL scientists and colleagues find that warming and drying of tropical forest soils may increase soil carbon vulnerability, by increasing degradation of older carbon.

Shown is the SpaceX Transporter-11 stack with the Deep Purple payload (circled in red) attached to the Pathfinder Technology Demonstrator-R satellite.
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The Deep Purple telescope developed by LLNL researchers is now operational in space.

Artistic rendition of X-ray diffraction from a sample in the toroidal diamond anvil cell at conditions relevant to the deep interior of Neptune
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An international team of LLNL scientists and collaborators develop a new sample configuration that improves the reliability of equation of state measurements in a pressure regime.

From left to right: Teal Pershing, Jimmy Kingston, Rachel Mannino, Ethan Bernard and Jingke Xu stand with the “XeNu” (Xe-Neutron) setup that calibrates LZ-style dark matter detectors.
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LLNL scientists contribute to figuring out the nature of dark matter using the world’s most sensitive dark matter detector, LUX-ZEPLIN (LZ).  

 

Under one of LLNL’s 2024 DOE Technology Commercialization Fund grants, Simon Pang (left) and Wenquin Li (right) will lead a team of researchers to collaborate in an effort to optimize site locations of carbon dioxide removal facilities.
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LLNL researchers continue to capture key DOE grants focused on funding projects aimed at delivering clean energy solutions to the market. 

Lawrence Livermore National Laboratory scientists and engineers, including Aldair Gorgora (right) and Timothy Yee are addressing longstanding challenges in 3D-printed lattice structures by using machine learning and artificial intelligence to accelerate lattice designs optimized with unprecedented speed and efficiency.
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LLNL scientists and engineers look to incorporating machine learning (ML) and artificial intelligence to accelerate design of lattice structures.

LLNL scientist Alan Hidy used the Center for Accelerator Mass Spectrometry to study fossils from Greenland.
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LLNL researchers and collaborators examine Iceland's core to discover clear evidence of ice-free times.

LLNL researchers combined phase-field simulations (background), topological feature extraction (inside the magnifying glass, showing a pore-size analysis), property calculations and machine learning analysis to uncover the microstructure-property relationship in polymeric porous materials.
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LLNL scientists develop an efficient and comprehensive computational framework to decipher implications of porous microstructures and their properties.

Wenyu Sun, Aditya Prajapati and Jeremy Feaster in the lab where their research takes place.
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Using thin film nickel anodes, a team of LLNL scientists and collaborators figure out how to clean up chemical production.