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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.

Researchers at Oak Ridge National Laboratory designed an adsorbent material to rapidly remove toxic chromium and arsenic simultaneously from water resources. Credit: Adam Malin/ORNL, U.S. Dept. of Energy

Researchers at ORNL are tackling a global water challenge with a unique material designed to target not one, but two toxic, heavy metal pollutants for simultaneous removal.

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.

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.

Earth Day

Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time. 

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.