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ORNL's Communications team works with news media seeking information about the laboratory. Media may use the resources listed below or send questions to news@ornl.gov.

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Technical illustration of an EV battery collector with vehicle placement shown in background.

Strengthening the competitiveness of the U.S. transportation industry depends on developing domestic EV batteries that combine rapid charging with long-range performance — two goals that often conflict. Researchers at ORNL have addressed this challenge by redesigning a key battery component, enabling fast, 10-minute charging while improving energy density and reducing reliance on copper.

Illustration of melting point of lithium chloride, which is shown with green and blue structures in two rows.

Scientists have developed a new machine learning approach that accurately predicted critical and difficult-to-compute properties of molten salts, materials with diverse nuclear energy applications. 

Illustration of an amino acid molecule that is going through a process from air to water with three phases, split into bubbles

Using the now-decommissioned Summit supercomputer, researchers at ORNL ran the largest and most accurate molecular dynamics simulations yet of the interface between water and air during a chemical reaction. The simulations have uncovered how water controls such chemical reactions by dynamically coupling with the molecules involved in the process. 

A color-enhanced 3D laser scan of a large concrete slab in a housing development, showing surface variations in shades of blue, green, yellow, and purple. Surrounding structures and terrain are rendered in black and white. The image was captured using the FLAT tool’s 360-degree scanning technology.

Researchers at ORNL have developed a tool that gives builders a quick way to measure, correct and certify level foundations. FLAT, or the Flat and Level Analysis Tool, examines a 360-degree laser scan of a construction site using ORNL-developed segmentation algorithms and machine learning to locate uneven areas on a concrete slab. 

the foreground shows new macromolecules that could be made using a process invented by Oak Ridge National Laboratory chemists to upcycle the polymers from discarded plastics.

By editing the polymers of discarded plastics, ORNL chemists have found a way to generate new macromolecules with more valuable properties than those of the starting material.

Procter & Gamble scientists used ORNL’s Summit supercomputer to create a digital model of the corneal epithelium, the primary outer layer of cells covering the human eye, and test that model against a series of cleaning compounds in search of a gentler, more environmentally sustainable formula.

P&G is using simulations on the ORNL Summit supercomputer to study how surfactants in cleaners cause eye irritation. By modeling the corneal epithelium, P&G aims to develop safer, concentrated cleaning products that meet performance and safety standards while supporting sustainability goals.

Emrullah Aydin, an ORNL research is posing for a headshot in a black suit with black tie
Emrullah Aydin, an R&D staff associate in the Vehicle Power Electronics Research group at the Department of Energy’s Oak Ridge National Laboratory, has been elevated to senior member status in the Institute of Electrical and Electronics Engineers.
Vivek Sujan is posing for a photo in a suit jacket with brown stairs and grey and brown background

Vivek Sujan, a distinguished R&D scientist in the Applied Research for Mobility Systems group at ORNL, has been named a 2024 National Association of Inventors Fellow for his numerous transportation-related patents. 

ORNL chemist Benjamin Manard is posing for a photo with a light blue and navy background

Benjamin Manard, a nuclear analytical chemist at ORNL, has been named the 2025 winner of the Emerging Leader in Atomic Spectroscopy Award from Spectroscopy magazine. 

Researchers are looking at computers, working with a bright blue box in the middle of the table

Researchers at ORNL are using microwave radar reflection to nondestructively detect and measure the moisture content of materials within walls without removing drywall or cladding. This also expedites the moisture identification process and enables mold growth to be treated in the early stages.