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Researchers found that moderate levels of ash — sometimes found as spheres in biomass — do not significantly affect the mechanical properties of biocomposites made up of corn stover, switchgrass and PLA thermoplastic. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

The presence of minerals called ash in plants makes little difference to the fitness of new naturally derived compound materials designed for additive manufacturing, an Oak Ridge National Laboratory-led team found.

ORNL researchers led by Michael Garvin, left, and David Kainer discovered genetic mutations called structural variants and linked them to autism spectrum disorders, demonstrating an approach that could be used to develop better diagnostics and drug therapies. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

ORNL researchers discovered genetic mutations that underlie autism using a new approach that could lead to better diagnostics and drug therapies.

Researchers used quantum Monte Carlo calculations to accurately render the structure and electronic properties of germanium selenide, a semiconducting nanomaterial. Credit: Paul Kent/ORNL, U.S. Dept. of Energy

A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.

These images show increasing levels of magnification of phytoliths in the leaves of poplar trees, a key biofuel crop, imaged using ORNL’s specialized microscopy-spectroscopy. Credit: Elizabeth Herndon/ORNL, U.S. Dept. of Energy

Scientists at Oak Ridge National Laboratory are closer to unlocking the secrets to better soil carbon sequestration by studying the tiny, sand-like silicon deposits called phytoliths in plants.

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.

The ORNL-developed AquaBOT measures a range of water quality indicators, providing data for studies focused on clean water and sustainable energy. Credit: Natalie Griffiths/ORNL, U.S. Dept. of Energy

Measuring water quality throughout river networks with precision, speed and at lower cost than traditional methods is now possible with AquaBOT, an aquatic drone developed by Oak Ridge National Laboratory.

ORNL researchers worked with partners at the Colorado School of Mines and Baylor University to develop a new process optimization and control method for a closed-circuit reverse osmosis desalination system. The work is intended to support fully automated, decentralized water treatment plants. Credit: Andrew Sproles/ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory scientists worked with the Colorado School of Mines and Baylor University to develop and test control methods for autonomous water treatment plants that use less energy and generate less waste.

ORNL is making underused or inaccessible bioenergy data available to accelerate innovation for the bioeconomy. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

A research team from Oak Ridge National Laboratory has identified and improved the usability of data that can help accelerate innovation for the growing bioeconomy.

Project bridges compute staff, resources at ORNL and VA health data to speed suicide risk screening for US veterans. Image Credit: Carlos Jones, ORNL

In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly. 

Motion sensing technology

Oak Ridge National Laboratory is training next-generation cameras called dynamic vision sensors, or DVS, to interpret live information—a capability that has applications in robotics and could improve autonomous vehicle sensing.