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Finding new chemistry to capture double the carbon
DOE/Pacific Northwest National Laboratory- Journal
- Nature Chemistry
- Funder
- DOE/US Department of Energy
A decade of GoAmazon
DOE/Pacific Northwest National Laboratory- Funder
- DOE/US Department of Energy
Computational chemistry needs to be sustainable, too
DOE/Pacific Northwest National LaboratoryAs new paradigms in advanced computing take shape, computational chemistry researchers are finding new ways to solve challenging chemistry problems.
- Journal
- Journal of Chemical Theory and Computation
- Funder
- DOE/US Department of Energy
Next-generation ShAPE™ metal extrusion arrives
DOE/Pacific Northwest National LaboratoryThe next-generation ShAPE machine has arrived at PNNL, where it will help prove the mettle of the ShAPE extrusion technique. ShAPE 2 is designed to allow researchers to produce larger, more complex extrusions.
- Funder
- DOE/US Department of Energy
North to Alaska: Project tests electrification concepts
DOE/Pacific Northwest National LaboratoryResearchers from PNNL have been assessing installation and use of electric heat pumps in an Alaskan community that relies on fuel oil for heat. The resulting information could advance electrification in cold rural areas across the nation.
- Funder
- DOE/US Department of Energy
Clean energy, one community at a time
DOE/Pacific Northwest National LaboratoryPNNL researchers are working to provide the technical assistance and expertise needed for communities to shape their clean energy future.
A consumer’s guide to rooftop solar and home energy storage
DOE/Pacific Northwest National Laboratory- Journal
- Renewable Energy Focus
New all-liquid iron flow battery for grid energy storage
DOE/Pacific Northwest National LaboratoryPeer-Reviewed Publication
A new iron-based aqueous flow battery shows promise for grid energy storage applications.
- Journal
- Nature Communications
- Funder
- DOE/US Department of Energy
New AI model is a leap for autonomous materials science
DOE/Pacific Northwest National LaboratoryA new AI model developed at PNNL can identify patterns in electron microscope images of materials without requiring human intervention, allowing for more accurate and consistent materials science.
- Funder
- DOE/US Department of Energy