Researchers at PNNL are sharing work grounded in One Health, which connects human, animal, plant, and environmental data to improve situational awareness, early warning, and decision-making.

Assistant Secretary Katie Jereza toured PNNL capabilities that translate laboratory innovation into operational and industry impact.

Global exercise advances nuclear forensic science and investigative capabilities.

Five PNNL innovations earned 2026 R&D 100 Awards for advances in biotechnology, manufacturing, critical minerals, and nuclear security.

PNNL has formed a partnership with the Orlando-based firm Refinity, which has licensed a key process from PNNL that will help the company convert mixed plastic waste into valuable industrial products like chemicals, plastics, and fuels.

A new report highlights a public workshop hosted by the National Academies Forum on Microbial Threats and features PNNL's Lauren Charles and the AI-Driven One Health Security program.

Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

Integrating new functions into a genome-scale model enabled researchers to simulate and experimentally validate viral hijacking pathways.

Matteo Muratori, director of transportation and industry programs at PNNL, has been named to the 2026–2028 cohort of the National Academies of Sciences, Engineering, and Medicine’s New Voices Program.

Secretary Wright visited PNNL's campus in Richland, Washington, late last year to meet with innovators in chemistry, biology, computing, and more.

A study by researchers at PNNL assessed the feasibility of using strontium isotope ratios and an existing machine learning–based model to predict and verify a product’s source—in this case, honey.

This summer, PNNL hosted the inaugural “As Conductive As Copper” (AC2.0) workshop, fostering a collaborative conversation on the future of the U.S. copper supply chain.

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.

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

In the latest issue of the Domestic Preparedness Journal, Ashley Bradley and Kristin Omberg share how new research is shedding light on the scientific and technological challenges with detecting fentanyl.

Recycling polyolefin materials is challenging. One waste management strategy is plastic upcycling. New work demonstrates a single-step upcycling route coupling cracking and alkylation, recycling carbon and keeping valuable resources active.

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

PNNL is supporting the Department of Homeland Security Science and Technology Directorate's Chemical Security Analysis Center in improving capabilities to enhance detection and analysis of chemical threats.

A new report highlights the results of an assessment PNNL conducted of field-portable detection products used by first responders to detect illicit substances like fentanyl in the field.