PNNL has developed a next-generation electrical resistivity tomography system for DOE that uses E4D software and AI-enhanced modeling to produce real-time subsurface images that help guide environmental remediation decisions.

RemPlex 2025 Global Summit on Environmental Remediation attendees share knowledge about cleanup and monitoring of complex sites worldwide; more than 100 presentations are posted online.

This achievement earned DIMPLES a Best Paper award at the ACM International Conference on Supercomputing 2025.

Three research teams led by PNNL organizers achieve journal publications following the Mathematics Research Communities program.

Expanding the nation’s largest wind power plant time series database; helping grid planners navigate a resilient energy future with better data.

PNNL researchers have published their paper, “Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data,” in the Journal of Proteome Research.

RemPlex and IAEA convene international leaders to tackle recruitment and retention in complex environmental cleanup efforts.

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.

The Deep Vadose Zone program leads a treatability study for subsurface remediation of the Hanford Site’s Central Plateau.

PNNL’s Center for the Remediation of Complex Sites convened attendees from around the world to discuss challenges associated with environmental contamination.

Scientists at PNNL have published a new article that focuses on understanding the composition, dynamics, and deployment of beneficial soil microbiomes to get the most out of soil.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

PNNL chemists investigate the performance of ion exchange resins for subsurface remediation of uranium, technetium, and chromium.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

A PNNL innovation uses steam to recover heat from the high-temperature reactor effluent in the HTL process, substantially reducing the propensity for fouling and potentially reducing costs.

STEM Education programs at DOE and national labs offer environmental management internships; applications are due soon for summer 2023.

To thwart pathogens, researchers in the epidemiology field of infectious disease (ID) prediction are continuously trying to forecast when, where, and how an ID event will occur.