Science and Technology Highlights

Scientists looking at computer screens
// S&T Highlights
LLNL Forensic Science Center scientists earned an “A” grade in the Organisation for the Prohibition of Chemical Weapons’ (OPCW) recent biomedical proficiency test.
Artist's rendering of molecules flowing through three electrodes
// S&T Highlights
To take advantage of the growing abundance and cheaper costs of renewable energy, Livermore scientists and engineers are 3D printing flow-through electrodes.
Radiating lines of white
// S&T Highlights
The next generation of high-repetition-rate, short-pulse lasers promises to accelerate and advance high-energy-density and photon science.
Precision Diagnostic System layout in graphical form
// S&T Highlights
The new Precision Diagnostic System has an advanced array of diagnostic tools that help researchers experiment with potential methods to increase laser performance
Artist's conception of molecules in a layer
// S&T Highlights
New materials made with carbon nanotube composites and a special thin polymer layer protect first responders from chemical and biological threats without sacrificing breathability and comfort.
Nozzle emitting bluish gas
// S&T Highlights
A new take on an additive manufacturing tool may be the key to capturing waste heat from manufacturing processes and converting it to electricity.
Two masked people flank metal box
// S&T Highlights
Livermore technology transfer and private-sector partnerships played an important role in fighting the COVID-19 pandemic.
Artist's rendering of 3D print machine creating electrochemical reactor
// S&T Highlights
A research team leverages the power of 3D printing to improve the performance of electrochemical reactors used to convert carbon dioxide (CO2) to useful energy sources, chemicals and material feedstocks.
Artist's rendering of bacteria in soil
// S&T Highlights
Just a few bacterial groups found in ecosystems across the planet are responsible for more than half of carbon cycling in soils.
Kelli Humbird
// S&T Highlights
Researchers have developed a new machine learning-based approach for modeling inertial confinement fusion experiments that results in more accurate predictions of National Ignition Facility shots.