Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

PNNL researchers design liquid-based porous electrolyte that could transport lithium ions more easily between electrodes, improving battery efficiency.

A paper by PNNL researchers has been chosen as one of 32 NIEHS Papers of the Year—a top 1% honor in the research field.

Findings show method to better prepare human lung tissue for study; it represents the latest mass spectrometry-based omics advances.

New PNNL research makes it easier to differentiate between chemical and nuclear explosions.

New research findings published in Science Advances (November 2022), help explain the progression of Alzheimer-related dementia in each patient. The findings outline a biological classification system that predicts disease severity.

Silicone wristbands could be more convenient option for collecting exposure data.

PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.

PNNL research, featured on the cover of two science journals, describes advancements in using Raman spectrometry for Hanford Site nuclear waste remediation.

We are pleased to welcome 20 new joint appointees to PNNL.

PNNL experts in nuclear forensics helped lead laboratories around the world through a rigorous test involving nuclear materials.

Yunyang Ye, a research engineer at PNNL, receives ASHRAE Homer Addams Award.

A new longer-lasting sodium-ion battery design is much more durable and reliable in lab tests. After 300 charging cycles, it retained 90 percent of its charging capacity.

Srinivas Katipamula, a staff scientist at PNNL, has been honored with an ASHRAE Exceptional Service Award.

Michael Brambley, a mechanical engineer at PNNL, receives ASHRAE Exceptional Service Award.

Jie Xiao, a world leader in electrochemical energy storage, has received the E.O. Lawrence Award from the Department of Energy.

Parallel graph neural networks identify protein-ligand 3-D structural interactions to aid in protein function prediction and drug discovery.

PNNL scientists have developed a new mass spectrometry method for in-depth analysis of proteins in individual cells.

Data from increasingly complex mass spectrometry instruments met with adaptable data analysis solution.

New facility will accelerate energy storage innovation, increase clean energy adoption and grid resilience.