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.
The Iowa Nuclear Energy Task Force, chaired by PNNL’s Mark Nutt, evaluated the state’s potential nuclear future including emerging technologies, workforce needs, waste management, and economic growth opportunities.
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.
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.
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.
The Department of Energy Office of Nuclear Energy acting assistant secretary makes his first visit to a national laboratory in his new role, touring PNNL's Radiochemical Processing Laboratory.
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.
