Existing stream monitoring efforts capture some fire-affected regions, but most fires are in places without ongoing water quality monitoring.

A new modeling approach uses burn severity maps to test how reduced infiltration shifts surface water from soil penetration into streams after wildfire.

Model simulations in humid and semi-arid basins revealed distinct thresholds where wildfires begin to significantly alter river water flows and dissolved nutrient transport.

Researchers developed a new technique that combined mass spectrometry and label-free optical imaging to map molecular and structural changes in healthy and diseased lung tissue, providing new insight into bronchopulmonary dysplasia.

Across 47 sites in the Yakima River Basin of Washington State, faster oxygen consumption occurred with warmer water, more nutrients, and more suspended solids.

Researchers reveal how uncharacterized molecules affect the diversity and composition of dissolved organic matter in rivers.

Measurements show river flow volumes and seasonal timing have shifted; models project where flows will rise, fall, or shift in the future.

Artificial intelligence helps analyze images and reduce uncertainty for a key measure of watershed function.

Dynamic organic matter traits tied to reactions can surpass static descriptors in explaining decomposition rates and responses to environmental change.

Using an AI approach to combine outputs from microbial models and apply them as inputs to reactive-transport models reduces computational time by several orders of magnitude.

Integrating experiments and modeling show how a fungal exudate-inspired substrate mixture elicits an emergent growth phenotype and metabolic responses in Pseudomonas putida.

By selectively stabilizing either a right- or left-handed helical pose of its substrate, an enzyme tunes the final product.

Uncovered how the choice of carbon source influences the stress tolerance, redox balance, and regulatory responses of yeast, which can help provide resilience against scale-up challenges.

Five PNNL innovations earned 2026 R&D 100 Awards for advances in biotechnology, manufacturing, critical minerals, and nuclear security.

New partnership agreement to focus on AI, robotics, and advanced energy systems.

Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

Study shows the advantages of top-town proteomics for characterizing histones.

At the summit, PNNL engaged with partners and investors, highlighting seven projects that accelerate the deployment of new energy technologies.

Integrating new functions into a genome-scale model enabled researchers to simulate and experimentally validate viral hijacking pathways.

A microbial community expresses a phenotype of interest ~20 percent higher than the sum of each member species.