Innovative cement and concrete technologies at PNNL enhance durability and performance through self-healing formulations and cost-effective, high-performance materials derived from waste, enabling more resilient, reliable infrastructure.

PNNL evaluates bacterial, fungi, and algae strains as part of a four-step process to streamline and standardize biomanufacturing processing.

The AI-Driven One Health Security activities at PNNL are focused on disrupting health threats and their impacts.

The Community Earth atmospheric Data System (CEDS) provides historical surface flux data that are used for general analysis and also for model validation through comparisons with observations.

The Center for AI @PNNL is driving a research agenda that explores the foundations and emerging frontiers of AI, combining capability development and application to mission areas in science, security and energy resilience.

RemPlex provides a global forum committed to fostering technical leadership, collaborative research, and professional development that facilitates the cost-effective remediation of complex sites.

The Center for Understanding Subsurface Signals and Permeability (CUSSP) Energy Earthshot Research Center (EERC) is working to develop the ability to predict and control fluid flow through fracture networks in enhanced geothermal systems.

PNNL provides tools and expertise to bring faster, cost-effective catalysts to industry that can be produced at a large scale.

PNNL is contributing analytics expertise to a first-of-its-kind electric vehicle carsharing program in rural Hood River, Oregon.

PNNL and their collaborators are investigating combinations of fuels to boost vehicle efficiency, performance, and affordability.

The COMPASS project aims to increase fundamental understanding, models, and the ability to predict change in coastal systems.

The Computational and Theoretical Chemistry Institute (CTCI) aspires to establish a premier international center for chemistry and materials science software at extreme scales.

DREAM generates and optimizes monitoring programs for detecting potential leaks from geological carbon storage.