Findings show method to better prepare human lung tissue for study; it represents the latest mass spectrometry-based omics advances.

A collaborative effort led by PNNL researchers analyzed decarbonization pathways for Argentina, Chile, Egypt, Indonesia, and Nigeria.

The Joint Appointment program at PNNL is one of the most diverse among other U.S. national laboratories, involving nearly 60 universities and research institutions in the United States and abroad.

A review article led by researcher Jade Holliman explores the different classes of metamaterials, from the underlying fundamental science to potential applications.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

A multi-institutional team of wind energy experts led by PNNL assessed the scientific grand challenges for offshore wind and provided recommendations for closing gaps in models.

Researchers identified a new intermediate phase that forms during the crystallization of hydrated aluminum-containing salts.

PNNL researchers are moving energy research forward through collaborative projects with universities across the United States.

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

To thwart pathogens, researchers in the epidemiology field of infectious disease (ID) prediction are continuously trying to forecast when, where, and how an ID event will occur.

A PNNL team developed and used a model framework to understand the performance and structural reliability of a state-of-the-art solid oxide electrolysis cell design.

Multidisciplinary teams from research and industry are brought together using management systems developed by a team of software engineers at PNNL.

One year into an Early Career Research Program award, Marcel Baer is developing new workflows to model biologically inspired systems more efficiently.

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.

Silicone wristbands could be more convenient option for collecting exposure data.

Advancing the science of radiation, especially among students at minority-serving institutions, is the goal of one of the Department of Energy’s newest consortia.

Tailoring hydrogen-based energy storage solutions to specific real-world applications.

Organismal contribution and community interaction drive soil organic matter breakdown by microbes.

PNNL gathered researchers from eight national laboratories plus the U.S. Department of Energy (DOE) to share ideas and build synergy at the Energy Equity and Environmental Justice Summit.