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Environmental Sciences Division Director Eric Pierce presented the organization’s 2023 Distinguished Achievement awards at a December 7 all-hands meeting. From left: Megan Johnson, Michael Jones, Maria Colberg, Rachel Pilla, Eric Pierce, Rocio Uria-Martinez, Gbadebo Oladosu and Paul Leiby. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

ORNL Environmental Sciences Division Director Eric Pierce presented the division’s 2023 Distinguished Achievement Awards at the organization’s December all-hands meeting.

Conceptual art depicts machine learning finding an ideal material for capacitive energy storage. Its carbon framework (black) has functional groups with oxygen (pink) and nitrogen (turquoise). Credit: Tao Wang/ORNL, U.S. Dept. of Energy

Guided by machine learning, chemists at ORNL designed a record-setting carbonaceous supercapacitor material that stores four times more energy than the best commercial material.

An electromagnetic pulse, or EMP, can be triggered by a nuclear explosion in the atmosphere or by an electromagnetic generator in a vehicle or aircraft. Here’s the chain of reactions it could cause to harm electrical equipment on the ground. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

Researchers at ORNL have been leading a project to understand how a high-altitude electromagnetic pulse, or EMP, could threaten power plants.

The OpeN-AM experimental platform, installed at the VULCAN instrument at ORNL’s Spallation Neutron Source, features a robotic arm that prints layers of molten metal to create complex shapes. This allows scientists to study 3D printed welds microscopically. Credit: Jill Hemman, ORNL/U.S. Dept. of Energy

Using neutrons to see the additive manufacturing process at the atomic level, scientists have shown that they can measure strain in a material as it evolves and track how atoms move in response to stress.

ORNL researcher Sreenivasa Jaldanki was recently elevated to IEEE senior member. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Sreenivasa Jaldanki, a researcher in the Grid Systems Modeling and Controls group at the Department of Energy’s Oak Ridge National Laboratory, was recently elevated to senior membership in the Institute of Electrical and Electronics Engineers, or IEEE.

ORNL’s David Sholl is director of the new DOE Energy Earthshot Non-Equilibrium Energy Transfer for Efficient Reactions center to help decarbonize the industrial chemical industry. Credit: Genevieve Martin, ORNL/U.S. Dept. of Energy

ORNL has been selected to lead an Energy Earthshot Research Center, or EERC, focused on developing chemical processes that use sustainable methods instead of burning fossil fuels to radically reduce industrial greenhouse gas emissions to stem climate change and limit the crisis of a rapidly warming planet.
 

Researchers used the open-source Community Earth System Model to simulate the effects that extreme climatic conditions have on processes like land carbon storage. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Researchers from Oak Ridge National Laboratory and Northeastern University modeled how extreme conditions in a changing climate affect the land’s ability to absorb atmospheric carbon — a key process for mitigating human-caused emissions. They found that 88% of Earth’s regions could become carbon emitters by the end of the 21st century. 

Scientists conducted microbial DNA sampling at a Yellowstone National Park hot spring for a study sponsored by DOE’s Biological and Environmental Research program, the National Science Foundation and NASA. Credit: Mircea Podar/ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory scientists studied hot springs on different continents and found similarities in how some microbes adapted despite their geographic diversity.

Yaoping Wang. Credit: Yaoping Wang

Yaoping Wang, postdoctoral research associate at ORNL, has received an Early Career Award from the Asian Ecology Section, or AES, of the Ecological Society of America.

ZEISS Head of Additive Manufacturing Technology Claus Hermannstaedter, left, and ORNL Interim Associate Laboratory Director for Energy Science and Technology Rick Raines sign a licensing agreement that allows ORNL’s machine-learning algorithm, Simurgh, to be used for rapid evaluations of 3D-printed components with industrial X-ray computed tomography, or CT. Using machine learning in CT scanning is expected to reduce the time and cost of inspections of 3D-printed parts by more than ten times.

A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine