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John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.
ORNL scientists develop a sample holder that tumbles powdered photochemical materials within a neutron beamline — exposing more of the material to light for increased photo-activation and better photochemistry data capture.
ORNL researchers used electron-beam additive manufacturing to 3D-print the first complex, defect-free tungsten parts with complex geometries.
A technology developed by Oak Ridge National Laboratory works to keep food refrigerated with phase change materials, or PCMs, while reducing carbon emissions by 30%.
Groundwater withdrawals are expected to peak in about one-third of the world’s basins by 2050, potentially triggering significant trade and agriculture shifts, a new analysis finds.
Cheekatamarla is a researcher in the Multifunctional Equipment Integration group with previous experience in product deployment. He is researching alternative energy sources such as hydrogen for cookstoves and his research supports the decarbonization of building technologies.
An international team using neutrons set the first benchmark (one nanosecond) for a polymer-electrolyte and lithium-salt mixture. Findings could produce safer, more powerful lithium batteries.
Alyssa Carrell started her science career studying the tallest inhabitants in the forest, but today is focused on some of its smallest — the microbial organisms that play an outsized role in plant health.
Although he built his career around buildings, Fengqi “Frank” Li likes to break down walls. Li was trained as an architect, but he doesn’t box himself in. Currently he is working as a computational developer at ORNL. But Li considers himself a designer. To him, that’s less a box than a plane – a landscape scattered with ideas, like destinations on a map that can be connected in different ways.
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.