Researchers developed a robust, cost-effective, and easy-to-use cap-based technique for spatial proteome mapping, addressing the lack of accessible proteomics technologies for studying tissue heterogeneity and microenvironments.

Klickitat Valley Health's fuel cell and microgrid project boosts rural healthcare, enhancing energy resilience and sustainability for social equity.

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

PNNL researchers will pursue both fundamental and applied projects with this support from the Department of Energy

Data from SOSAT web tool informs site selection and management for the subsurface injection of captured carbon dioxide.

To improve our ability to “see” into the subsurface, scientists need to understand how different mineral surfaces respond to electrical signals at the molecular scale.

Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.

PNNL researchers led a collaborative effort to showcase the most up-to-date, innovative, commercial cement and concrete technologies.

A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.

Advanced proteomic analysis tools dig deep into human blood plasma to understand the molecular nature of advanced alcoholic disease.

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.

Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.