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.

Separating quantum circuit wiring from molecular data allows researchers to more efficiently perform quantum simulations

Short-term flooding of an experimental coastal forest impacted soils but not trees, with implications for understanding ecosystem disturbance responses.

Lab experiments show that just a few saltwater pulses can rapidly alter soil pore structure and reduce oxygen flow, stressing tree roots in coastal forest soils.

Differences in dissolved organic matter between rivers and estuaries follow similar patterns across freshwater and saltwater coasts.

Newly designed sensor network comprehensively monitors a range of coastal landscapes to better understand nutrient and elemental dynamics that drive ecosystem function.

Exploring how shifting boundaries between upland and wetland areas influence soil processes and signal early ecosystem transformations.

A high-performance, scalable pipeline transforms raw high frequency coastal sensor data into research-ready datasets.

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.

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.

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

A suite of new modeling methods gives researchers better tools to predict molecular behavior inside cavities.

A recent article highlighted a PNNL report focused on post-quantum cryptography and electric vehicle charging infrastructure.

Darío Gil, DOE's Undersecretary for Science, makes his inaugural visit to PNNL.

An approach combining multiple theories increases the accuracy of quantum-based simulations without increasing computational demands.

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.

Researchers have developed the first coastal transport model for microplastics to understand how particles move in waterways and coastal currents.

This research helps predict how coastal forests will respond to future sea level changes which is crucial for protecting these ecosystems.

Due to their inherent variability and complexity over space and time, scientists are challenged to understand the complex interactions among soil, vegetation, and water along coastal terrestrial-aquatic interfaces.