A new study shows how organic molecules respond to global change varies according to functional traits and ecological processes.

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.

Eight PNNL research papers were recognized by the 2022 Waste Management Symposia as “Papers of Note” and “Superior.” One paper received a Best Oral Paper/Presentation Award.

The size and concentration of ions influences the aggregation behavior of solutions of boehmite.

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.

PNNL’s Rob Howard received the 2022 Fuel Cycle and Waste Management Award from the American Nuclear Society.

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

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

The size of the alkali metal within the material influences the crystallization pathway.

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

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

The collaboration will further accelerate the science behind radioactively contaminated land management and radioactive waste disposal.

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.