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.

CACTUS provides AI-powered cheminformatics for autonomous science.

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.

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.

New apps help researchers with statistical analyses of data.

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.

A team of researchers from PNNL provided technical knowledge and support to test a suite of techniques that detect genetically modified bacteria, viruses, and cells.

PNNL researchers are key contributors to GAO report

Summer internship programs with partnering colleges provide pathways for students to explore careers in science, technology, engineering, and math.

The ChemSpace Tool, when fully developed, is intended to divide chemical space into three subsets: the detectable space, the identifiable space, and the region that includes compounds that are not detectable or identifiable.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

Microbes that were previously frozen in soils are becoming more active. This study demonstrates the diverse RNA viral communities found in thawed permafrost.