A team from PNNL contributed several articles to the Domestic Preparedness Journal showcasing recent efforts to explore the emergency management and artificial intelligence research and development landscape.
Researchers developed a robust, cost-effective, and easy-to-use cap-based technique for spatial proteome mapping, addressing the lack of accessible proteomics technologies for studying tissue heterogeneity and microenvironments.
A team of researchers recently coordinated a series of international workshops aimed at enhancing chemical research security and fostering collaboration among scientists and academic researchers from both countries.
Over the past three years, PNNL’s Know Your Collaborator (KYC) workshop series has engaged hundreds of academic partners and institutional researchers internationally on the topic of research security.
Erich Hsieh, Deputy Assistant Secretary for OE’s Energy Storage Division, shared insights about the Grid Storage Launchpad and energy storage innovations .
PNNL advisors joined a panel of Washington State emergency management personnel to discuss how partnerships with national laboratories are enabling science and technology solutions.
The nation is closer to its offshore wind energy goals than ever before, but better wind forecasting is still needed. To address this challenge, PNNL and collaborators are charting a new course with help from novel technology.
PNNL researchers helped design and conduct an international exercise hosted by the Ministry of Finance of Finland to help improve financial sector resilience.
The Forefront23 workshop convened researchers, scientists, and engineers who are just that: at the forefront of cybersecurity and nuclear nonproliferation.
A research buoy managed by PNNL has been deployed in Hawai’ian waters, collecting oceanographic and meteorological measurements off the coast of O’ahu.
A multi-institutional team of wind energy experts led by PNNL assessed the scientific grand challenges for offshore wind and provided recommendations for closing gaps in models.
