PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

The diversity and function of organic matter in rivers at a large scale are influenced by factors, such as the types of vegetation covering the land, the energy characteristics, and the breakdown potential of the molecules.

ICON science is a Department of Energy-developed framework to enhance scientific outcomes via more intentional design of research efforts across all domains of science.

A new microbial community model reveals previous exposure to hydrologic variation influences a species’ specialized biogeochemical functions.

Detailed characterization of molecules in, under, and along the Columbia River reveal potential mechanisms for biogeochemical cycling.

Integrating hydrogeology and biogeochemistry are required to model the dynamics of geochemical processes occurring in river corridor zones where groundwater and surface water mix.

Theoretical analysis reveals flow variations that happen over longer time periods affect a plume’s spread and center of mass.

Principles derived from coastal wetlands to describe wetland channel cross-sections were applicable to the Columbia River estuary, but not the tidal river.

An updated version of a widely used database will provide powerful insights into the global carbon cycle.

Variability in field measurement conditions likely does not impact estimates of an important flux in the global carbon cycle.

Pacific Northwest marsh carbon stocks are twice the global average. Long-term sea-level rise impacts will likely decrease ecosystem carbon stocks.