2018 | U.S. DOE Office of Science (SC)

U.S. Energy Department Funds Geography Project at UGA

The United States Department of Energy is funding $10 million toward 13 separate projects working to further improve the Energy Exascale Earth System Model. Gabriel J. Kooperman, assistant professor of geography at the University of Georgia, is leading a collaboration with other universities on one of those projects.

Reverse Engineering Reveals Pine Tree's Chemical Production — Worth Billions

Washington State University researchers have reverse engineered the way a pine tree produces a resin, which could serve as an environmentally friendly alternative to a range of fossil‑fuel based products worth billions of dollars.

ASU Professor Wins Grants to Elucidate the Magic of Proteins

For Arizona State University’s Dmitry Matyushov, professor in the School of Molecular Sciences and the Department of Physics, years of studying how electrons make their way through some important protein molecules has been recently rewarded with two major grants from the National Science Foundation (NSF) and the Department of Energy (DOE).

Nuclear Fusion: Wrestling with Burning Questions on the Control of 'Burning Plasmas'

Recent work by members of Lehigh’s Plasma Control Laboratory will be presented at the 27th International Atomic Energy Agency (IAEA) Fusions Conference in Gandhinagar, India. Their first piece of work, entitled “Physics-model-based Real-time Optimization for the Development of Steady-state Scenarios at DIII-D,” discusses recent experimental results that demonstrate the effectiveness of a model-based real-time optimization scheme to consistently achieve desired advanced scenarios at predefined times.

National Clean Energy Week: New Technology Could Wean the Battery World Off Cobalt

A research team led by scientists at the University of California, Berkeley, has opened the door to using other metals in lithium-based batteries, and have built cathodes with 50 percent more lithium-storage capacity than conventional materials.

UNH Researchers Discover New Materials to Generate Solar Fuel Production

Researchers at the University of New Hampshire have identified new, readily available materials that convert sunlight and carbon dioxide (CO2) into building blocks for liquid fuels that could one day heat homes and power cars.

CCNY Study Breaks Förster Resonant Energy Transfer (FRET) Distance Limit

Using engineered nanocomposite structures called metamaterials, a City College of New York-led research team reports the ability to measure a significant increase in the energy transfer between molecules.

Purdue Team Receives $2.5 Million to Develop Quantum Computing Technologies

A team of Purdue researchers led by Sabre Kais, a professor of chemistry, received a grant from the U.S. Department of Energy to advance technologies in quantum information science.

Hampton University Physics Graduate Student is the first Office of Science Graduate Student Research Awardee

Hampton University physics graduate student, Sahara Jesmin Mohammad Prem Nazeer, is officially the first Hampton University Office of Science Graduate Student Research Awardee.

Amanda Morris’ Chemistry Lab Mimicking How Plants Energize in Bid to Create Clean, Sustainable Energy

Morris, an associate professor in the Department of Chemistry, part of the Virginia Tech College of Science, and her student researchers are trying to mimic the ways plants use sunlight to convert carbon dioxide to storable food.

Finding Antineutrino Oscillation

For more than three years, scientists on the NOvA collaboration, which includes MSU physicists, have been observing particles called neutrinos as they oscillate from one type to another over a distance of 500 miles. Now, in a new result unveiled at the Neutrino 2018 conference, the collaboration has announced its first results using antineutrinos.

Machine Learning May Be a Game-Changer for Climate Prediction

Researchers led by Pierre Gentine, associate professor of earth and environmental engineering at Columbia Engineering, demonstrate that machine learning techniques can be used to better represent clouds in coarse resolution climate models, with the potential to narrowing the range of prediction.