PNNL researchers are moving energy research forward through collaborative projects with universities across the United States.

Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.

To thwart pathogens, researchers in the epidemiology field of infectious disease (ID) prediction are continuously trying to forecast when, where, and how an ID event will occur.

One year into an Early Career Research Program award, Marcel Baer is developing new workflows to model biologically inspired systems more efficiently.

A new project will produce a database of optimized conditions for efficient, carbon negative recovery of energy-relevant minerals in red mud waste.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

When wildfires burn in the west, the heat and airborne particles they release inflict stronger rain and larger hail upon central states.

Evaluating the relative roles of soil water stress and vapor pressure deficit at limiting canopy conductance in coupled land-atmosphere simulations.

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

Tailoring hydrogen-based energy storage solutions to specific real-world applications.

Organismal contribution and community interaction drive soil organic matter breakdown by microbes.

PNNL gathered researchers from eight national laboratories plus the U.S. Department of Energy (DOE) to share ideas and build synergy at the Energy Equity and Environmental Justice Summit.

Understanding why the westerly jets shift poleward in certain seasons and regions but equatorward in others under global warming.

A review article examines a decade of work on the atom-scale process of turning carbon dioxide into stable minerals.

HiSVSIM enables large-scale quantum simulations through graph partitioning.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

A new deep learning approach allows researchers to rapidly analyze three-dimensional representations of multi-component catalysts.

Where molybdenum dopants sit within catalytic material affects catalysis at a mechanistic level.

The amount of radiation in a system influences how fast aggregated boehmite particles dissolve.

Using electrocatalysis offers a different path for reactions involving polar groups.