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Oak Ridge National Laboratory’s Ramesh Bhave partnered with Momentum Technologies to develop a modular, scalable system for recycling scrap permanent magnets in e-waste. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Researchers at Oak Ridge National Laboratory and Momentum Technologies have piloted an industrial-scale process for recycling valuable materials in the millions of tons of e-waste generated annually in the United States.

Samarthya Bhagia examines a sample of a thermoplastic composite material additively manufactured using poplar wood and polylactic acid. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Chemical and environmental engineer Samarthya Bhagia is focused on achieving carbon neutrality and a circular economy by designing new plant-based materials for a range of applications from energy storage devices and sensors to environmentally friendly bioplastics.

Scattering-type scanning near-field optical microscopy, a nondestructive technique in which the tip of the probe of a microscope scatters pulses of light to generate a picture of a sample, allowed the team to obtain insights into the composition of plant cell walls. Credit: Ali Passian/ORNL, U.S. Dept. of Energy

To optimize biomaterials for reliable, cost-effective paper production, building construction, and biofuel development, researchers often study the structure of plant cells using techniques such as freezing plant samples or placing them in a vacuum.

Oak Ridge National Laboratory scientists are enhancing the performance of polymer materials for next-generation lithium batteries. Credit: Adam Malin/ORNL, U.S. Dept. of Energy

Researchers at Oak Ridge National Laboratory are using state-of-the-art methods to shed light on chemical separations needed to recover rare-earth elements and secure critical materials for clean energy technologies.

Oak Ridge National Laboratory researchers developed an invertible neural network, a type of artificial intelligence that mimics the human brain, to improve accuracy in climate-change models and predictions. Credit: Getty Images

Oak Ridge National Laboratory researchers developed an invertible neural network, a type of artificial intelligence that mimics the human brain, to improve accuracy in climate-change models and predictions.

Jennifer Morrell-Falvey leads the Molecular and Cellular Imaging group at ORNL, advancing new insights in several scientific areas, including the interactions between plants and microbes that influence ecosystem health and carbon cycling. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Jennifer Morrell-Falvey’s interest in visualizing the science behind natural processes was what drew her to ORNL in what she expected to be a short stint some 18 years ago. 

LandScan Global depicts population distribution estimates across the planet. The darker orange and red colors above indicate higher population density. Credit: ORNL, U.S. Dept. of Energy

It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.

Physicist Charles Havener uses the NASA end station at ORNL’s Multicharged Ion Research Facility to simulate the origin of X-ray emissions from space. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Scientists are using Oak Ridge National Laboratory’s Multicharged Ion Research Facility to simulate the cosmic origin of X-ray emissions resulting when highly charged ions collide with neutral atoms and molecules, such as helium and gaseous hydrogen.

Oak Ridge National Laboratory researchers built an Earth-to-space communications system to work with private and government partners with the goal of directly connecting data downlinks to high performance computing. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory is debuting a small satellite ground station that uses high-performance computing to support automated detection of changes to Earth’s landscape.

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.