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A study led by ORNL researchers examines the causes behind ordering of cations, the positive ions that help make double perovskite oxides look promising as an energy source. Credit: Getty Images

A study led by researchers at ORNL could uncover new ways to produce more powerful, longer-lasting batteries and memory devices.

Consumers have a new resource for finding plug-in electric and fuel cell vehicle tax credits. Current owners and those considering an electric vehicle purchase can access a free tool developed by ORNL researchers for fueleconomy.gov. Credit: ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory researchers have developed an online resource to help consumers understand the electric vehicle tax credits available through the Inflation Reduction Act.

ORNL researchers have developed a way to manage car batteries of different types and sizes as energy storage for the power grid. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

When aging vehicle batteries lack the juice to power your car anymore, they may still hold energy. Yet it’s tough to find new uses for lithium-ion batteries with different makers, ages and sizes. A solution is urgently needed because battery recycling options are scarce.

A multiport design allows a utility to easily interface with an EV truck stop to provide fast-charging at megawatt-scale. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

Researchers at Oak Ridge National Laboratory have designed architecture, software and control strategies for a futuristic EV truck stop that can draw megawatts of power and reduce carbon emissions.

The ORNL and Cummins CRADA led to the development of the R&D 100 Award-winning SpaciMS diagnostic tool that enabled researchers to gain a better understanding of reactor and catalytic chemistry. Credit: ORNL, U.S. Dept. of Energy

When Bill Partridge started working with industry partner Cummins in 1997, he was a postdoctoral researcher specializing in applied optical diagnostics and new to Oak Ridge National Laboratory.

Jim Szybist, Propulsion Science section head at ORNL, is applying his years of alternative fuel combustion and thermodynamics research to the challenge of cleaning up the hard-to-decarbonize, heavy-duty mobility sector. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy.

What’s getting Jim Szybist fired up these days? It’s the opportunity to apply his years of alternative fuel combustion and thermodynamics research to the challenge of cleaning up the hard-to-decarbonize, heavy-duty mobility sector — from airplanes to locomotives to ships and massive farm combines.

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It’s been referenced in Popular Science and Newsweek, cited in the Economic Report of the President, and used by agencies to create countless federal regulations.

The online Fuel Economy Guide, compiled by ORNL researchers, provides simple tips to save at the pump including the Trip Calculator tool to better navigate vehicle choice and estimate mileage. Credit: Storyblocks

Oak Ridge National Laboratory researchers determined that for every 5 miles per hour that drivers travel over a 50-mph speed limit, fuel economy decreases by 7% and equates to paying an extra 28 cents per gallon at current.

ORNL, VA and Harvard researchers developed a sparse matrix full of anonymized information on what is thought to be the largest cohort of healthcare data used for this type of research in the U.S. The matrix can be probed with different methods, such as KESER, to gain new insights into human health. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

A team of researchers has developed a novel, machine learning–based  technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.

This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography.  Credit: Ada Sedova/ORNL, U.S. Dept. of Energy

A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.