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

New apps help researchers with statistical analyses of data.

A team of researchers from PNNL provided technical knowledge and support to test a suite of techniques that detect genetically modified bacteria, viruses, and cells.

Across the United States, organic carbon concentration imposes a primary control on river sediment respiration, with additional influences from organic matter chemistry.

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

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

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