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Using as much as 50 percent lignin by weight, a new composite material created at ORNL is well suited for use in 3D printing.

Scientists at the Department of Energy’s Oak Ridge National Laboratory have created a recipe for a renewable 3D printing feedstock that could spur a profitable new use for an intractable biorefinery byproduct: lignin.

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An Oak Ridge National Laboratory-led team used a scanning transmission electron microscope to selectively position single atoms below a crystal’s surface for the first time.

Sergei Kalinin, director of the Institute for Functional Imaging of Materials at Oak Ridge National Laboratory, convenes experts in microscopy and computing to gain scientific insights that will inform design of advanced materials for energy and informati

Sergei Kalinin of the Department of Energy’s Oak Ridge National Laboratory knows that seeing something is not the same as understanding it. As director of ORNL’s Institute for Functional Imaging of Materials, he convenes experts in microscopy and computing to gain scientific insigh...

Schematic drawing of the boron nitride cell. Credit: University of Illinois at Chicago.

A new microscopy technique developed at the University of Illinois at Chicago allows researchers to visualize liquids at the nanoscale level — about 10 times more resolution than with traditional transmission electron microscopy — for the first time. By trapping minute amounts of...

3D printed permanent magnets with increased density were made from an improved mixture of materials, which could lead to longer lasting, better performing magnets for electric motors, sensors and vehicle applications. Credit: Jason Richards/Oak Ridge Nati

Oak Ridge National Laboratory scientists have improved a mixture of materials used to 3D print permanent magnets with increased density, which could yield longer lasting, better performing magnets for electric motors, sensors and vehicle applications. Building on previous research, ...

Rose Ruther and Jagjit Nanda have been collaborating to develop a membrane for a low-cost redox flow battery for grid-scale energy storage.

Oak Ridge National Laboratory scientists have developed a crucial component for a new kind of low-cost stationary battery system utilizing common materials and designed for grid-scale electricity storage. Large, economical electricity storage systems can benefit the nation’s grid ...

The electromagnetic isotope separator system operates by vaporizing an element such as ruthenium into the gas phase, converting the molecules into an ion beam, and then channeling the beam through magnets to separate out the different isotopes.

A tiny vial of gray powder produced at the Department of Energy’s Oak Ridge National Laboratory is the backbone of a new experiment to study the intense magnetic fields created in nuclear collisions.

Illustration of satellite in front of glowing orange celestial body

A shield assembly that protects an instrument measuring ion and electron fluxes for a NASA mission to touch the Sun was tested in extreme experimental environments at Oak Ridge National Laboratory—and passed with flying colors. Components aboard Parker Solar Probe, which will endure th...

From left, Andrew Lupini and Juan Carlos Idrobo use ORNL’s new monochromated, aberration-corrected scanning transmission electron microscope, a Nion HERMES to take the temperatures of materials at the nanoscale. Image credit: Oak Ridge National Laboratory

A scientific team led by the Department of Energy’s Oak Ridge National Laboratory has found a new way to take the local temperature of a material from an area about a billionth of a meter wide, or approximately 100,000 times thinner than a human hair. This discove...

ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.

A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the