From studying the multisector ripple effects of extreme events to exploring the chemical reactions that underlie life on Earth, two up-and-coming PNNL scientists were recognized for their scientific vision and prowess.

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.

A new memorandum of understanding creates a framework for expanded research, talent development, and technology transfer between Georgia Tech and PNNL.

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.

A theoretical index linking precipitation and atmospheric evaporative demand explains historical land humidity changes across observations, reanalyses, and climate models.

Observations, reanalysis, and E3SMv3 experiments linked inter-annual variability of Congo Basin surface temperature to the zonal position of precipitation over the Indo-Pacific maritime continent region.

Watershed-based representation discretized into topographic subgrid units better captured small-scale land cover, precipitation, temperature, and snow variability across multiple spatial scales.

Physics-informed and data-driven neural network models enhance Earth system models in simulating compound flooding at river-ocean interfaces.

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.