Science and Technology Highlights

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.

Joe Ralph, co-lead author and inertial confinement fusion research physicist at Lawrence Livermore National Laboratory, discusses the critical role of implosion symmetry in achieving a burning plasma state at the National Ignition Facility.
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LLNL researchers retrospectively confirm that implosion asymmetry was a major aspect for fusion experiments.

Femtosecond X-ray diffraction of laser shocked aluminum-zirconium metals.
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LLNL scientists use ultra-fast X-ray probes to track the thermal response of aluminum and zirconium on shock release from experiments. 

The image looks down the barrel of a metallic carbon nanotubes embedded in an array of closely-packed carbon nanotubes with different electronic properties.
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LLNL scientists find that pure metallic carbon nanotubes are best at transporting molecules.

A machine-learning potential derived from first-principles calculations unveils the intricate mechanisms of CO2 capture in liquid ammonia.
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LLNL scientists develop a machine-learning model to gain an atomic-level understanding of CO2 capture in amine-based sorbents.

Water gets weird under nano-confinement. This image shows an exotic phase of water trapped in tiny spaces, where it interacts surprisingly with electric fields.
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LLNL scientists and a collaborator at University of Texas at Austin turn to simulations to explain the first-order response of confined water to applied electric fields.