Developed a method for selectively anchoring diverse divalent metal cations in zeolites, important for precise control over catalytic materials.

Developed a simple kinetic framework for evaluating catalyst hydride transfer ability.

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.

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

A low-temperature, single-stage catalytic process converts PVC and polyolefin into valuable alkanes.

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.

A comprehensive investigation provides quantitative data on the interaction between zeolite pores and linear alcohols, with hydroxyl group interactions playing the largest role.

The ability of a storm-resolving weather model to predict the growth of storms over central Argentina was evaluated with data from the Clouds, Aerosols, and Complex Terrain Interactions (CACTI) field campaign in central Argentina.

After isolating copper hydride monomers, researchers identified the mechanistic details of catalyst aggregation and cluster formation.

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

Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.

Lakes are important sentinels of climate change and contribute significantly to the emissions of the second most important greenhouse gas—methane.

The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.

To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.

PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.

This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

The low-temperature breakdown of polyolefins is driven by carbenium ions generated from chloroaluminate ionic liquids.

PNNL chemical engineer Mariefel Olarte shares passion for mentoring students and the next-generation workforce.

Tuning the environment of supported palladium atoms with hydrogen treatment enhances their catalytic activity.