A team from PNNL joined the Department of Homeland Security Science and Technology Directorate and first responders from across the country to evaluate commercially available unmanned ground vehicles for emergency response.
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.
Researchers at PNNL share a research- and practitioner-informed approach to assess the threat landscape, elicit and integrate feedback into solutions, and ultimately share outcomes with the emergency response and public safety community.
Shear Assisted Processing and Extrusion (ShAPE) imparts significantly more deformation compared to conventional extrusion. The latest ShAPE system at PNNL, ShAPEshifter, is a purpose-built machine designed for maximum configurability.
Two new publications provide emergency response agencies with critical insights into commercially available unmanned ground vehicles used for hazardous materials response.
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.
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.
PNNL advisors joined a panel of Washington State emergency management personnel to discuss how partnerships with national laboratories are enabling science and technology solutions.
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.
The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.
