LLNL researchers and collaborators lay the foundation for understanding how POMs interact with some of the most chemically challenging actinide elements.
Science and Technology Highlights
LLNL researchers aim to use a machine-learning model that can distinguish opioids from other chemicals with an accuracy over 95% in a laboratory setting.
A multidisciplinary team of LLNL researchers has successfully demonstrated a potentially simpler, more accurate way to measure plasma conditions with two laser beams that cross paths.
In an open-access database and with publicly available code, LLNL researchers have simulated and published one million orbits in cislunar space.
In a recent study, LLNL researchers and collaborators engineered carbon nanotubes with openings that can reversibly open and close depending on pH.
LLNL researchers have co-developed a new way to precisely control the internal structure of common plastics during 3D printing.
LLNL researchers identify a first-of-its-kind carbon dioxide-equivalent polymer that can be recovered from high-pressure conditions.
In a recent study, LLNL researchers and collaborators created a new framework that couples tiny, atom-scale simulations to code that describes the macroscopic world, all within the same simulation.
LLNL scientists and their collaborators demonstrate a method to overcome the challenges of the traditional additive manufacturing process.
In a new study, LLNL researchers and collaborators examine multi-ignition fires, calculating their impact and modeling the mechanisms behind them.
