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PNNL and Argonne researchers developed and tested a chemical process that successfully captures radioactive byproducts from used nuclear fuel so they could be sent to advanced reactors for destruction while also producing electrical power.
In the third year of the DISCOVR Consortium project, the consortium team has identified an algal strain that progressed successfully through multiple evaluation phases.
A PNNL technology enables automated Economic Dispatch, which coordinates the use of energy in a manner that enhances distributed generation, efficiency, renewables, and grid reliability.
Nitrogen oxides, also known as NOx, form when fossil fuels burn at high temperatures. When emitted from industrial sources such as coal power plants, these pollutants react with other compounds to produce harmful smog.
Eric Hoppe, senior scientist, was selected a 2019 American Chemical Society (ACS) fellow. Eric is being recognized for his contributions to analytical chemistry measurements and three decades of volunteer service to the ACS community.
Researchers at PNNL have developed a model that predicts outcomes from the algae hydrothermal liquefaction process in a way that mirrors commercial reality much more closely than previous analyses.
When two powerful earthquakes rocked southern California earlier this month, officials’ attention focused, understandably, on safety. How many people were injured? Were buildings up to code? How good are we at predicting earthquakes?
Researchers at PNNL have introduced an alternative method using a molecular-based pump that could potentially use a quarter less energy than the age-old mechanical pump.
Researchers have come up with a new method for creating synthetic “colored” nanodiamonds, a step on the path to realization of quantum computing, which promises to solve problems far beyond the abilities of current supercomputers.
Researchers at PNNL and their collaborators have made a significant improvement to a catalyst that is more rugged and can reduce tailpipe pollution at lower temperatures than existing methods.
Scientists created a fast-track tutorial that equips a neural network to tackle drug discovery and other applications where there's a shortage of precisely labeled chemical data.