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.

A multi-omics analysis provides the framework for gaining insights into the structure and function of microbial communities across multiple habitats on a planetary scale

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

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

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

Across the United States, water moving between the river and riverbed sediments does not overcome localized processes that govern organic matter chemistry.

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.