A process developed at PNNL that converts biomass and waste into a chemical intermediate or into gasoline, diesel, and jet fuel is available for commercial licensing.

TransMED analyzes patient data from similar diseases across multiple sources to understand COVID-19 patient outcome risk factors.

PNNL Laboratory fellow Dave Heldebrant has been selected as a 2022 fellow by the American Chemical Society.

A novel combination of temperature and four-dimensional geophysical sensors revealed otherwise unseeable hydrologic dynamics below riverbed sediments.

Forms of nitrogen released by nitrogen-fixing bacteria differ across sampling scales and in structured environments.

Dominant and functionally important soil microbes show strong, predictable, and distinctly different associations with continental-scale gradients in climate, vegetation, and soil moisture.

The Washington State Academy of Sciences added six people from PNNL to its 2022 class of inductees.

Soil and its microbial inhabitants from Washington state are heading for the International Space Station today.

The virus that causes COVID-19 takes over the body’s fat-processing system and boosts cellular triglycerides as it causes disease.

A new perspective article discusses how integrating carbon dioxide capture and conversion in solvents can lead to cheaper and more efficient carbon management systems.

A PNNL scientist and an Oregon State University faculty member with a PNNL joint appointment were named recipients.

Providing a perspective on the importance of integrating experimental work with theory and simulations when designing new carbon capture solvents.

Soil transplant experiment gives new insights into coastal forest resilience, rising seas, and increasing storms.

The Department of Homeland Security Science and Technology Virtual First Responder Capitol Hill Showcase highlighted PNNL work in fentanyl detection standards and database libraries.

PNNL will demonstrate how new technologies, innovative approaches and partnering with others can lead to net-zero emissions and decarbonization of operations.

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

Researchers use machine learning to speed up the analysis of aerosol datasets from mass spectrometry.

This PNNL-developed separation system quickly and successfully separates larger particles from smaller ones at various scales, in different solid-liquid mixtures and at different flow rates.

A novel ecological measurement uncovered interactions between river corridor organic matter assemblages and microbial communities, highlighting potentially important microbial taxa and molecular formula types.

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