Shear Assisted Processing and Extrusion (ShAPE) imparts significantly more deformation compared to conventional extrusion. The latest ShAPE system at PNNL, ShAPEshifter, is a purpose-built machine designed for maximum configurability.

This study used historical data, remote sensing, and aquatic sensors to measure how far wildfire impacts propagated through the watershed after the 2022 Hermit’s Peak/Calf Canyon fire, New Mexico’s largest wildfire in history.

The Coastal Observations, Mechanisms, and Predictions Across Systems and Scales: Field, Measurements, and Experiments project established a network of observational field sites across Chesapeake Bay and western Lake Erie.

A new database allows researchers to connect lattice parameters and composition in olivine.

New methodology can evaluate the potential for carbon storage and critical mineral recovery in undercharacterized geological settings.

New research investigating water-lean solvents for carbon dioxide capture identifies the unique chemistry possible with their use, may lead to new design principles that move beyond single carbon capture.

Department of Energy’s Advanced Research Projects Agency-Energy selects PNNL project to help accelerate the development of marine carbon dioxide removal technologies.

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.

Metabolomics analysis revealed differences in metabolome profiles for two distinct modes of fungal wood decay.

Under Secretary for Science and Innovation at the Department of Energy Geri Richmond visited PNNL.

First-of-its-kind genomic catalog lends insight into previously unknown processes at the river’s edge.

Department of Energy, Office of Science Director Asmeret Asefaw Berhe visited PNNL to learn about the Lab’s drive to conduct discovery science, commitment to science for an equitable future, and development of a diversified STEM workforce.

Newly developed models can help rapidly optimize systems to meet the need for rapidly deployable commercial carbon capture.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

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

Updated flexible software generates and optimizes monitoring programs for detecting potential leaks from geological carbon storage with an enhanced user experience.

A new perspective article discusses how integrating carbon dioxide capture and conversion in solvents can lead to cheaper and more efficient carbon management systems.

Providing a perspective on the importance of integrating experimental work with theory and simulations when designing new carbon capture solvents.

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.