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Jack Cahill of ORNL’s Biosciences Division is developing new techniques to view and measure the previously unseen to better understand important chemical processes at play in plant-microbe interactions and in human health. In this photo, Cahill is positioning a rhizosphere-on-a-chip platform for imaging by mass spectrometry. Credit: Carlos Jones/ORNL, U.S. Dept of Energy

John “Jack” Cahill is out to illuminate previously unseen processes with new technology, advancing our understanding of how chemicals interact to influence complex systems whether it’s in the human body or in the world beneath our feet.

ORNL will use its land surface modeling tools to determine Baltimore’s climate risk and analyze green infrastructure improvements that can help mitigate impacts on underserved communities as part of a DOE Urban Integrated Field Laboratory project. Source: Google Earth, accessed Sept. 12, 2022

ORNL researchers are deploying their broad expertise in climate data and modeling to create science-based mitigation strategies for cities stressed by climate change as part of two U.S. Department of Energy Urban Integrated Field Laboratory projects.

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.

Giri Prakash, director of the ARM Data Center, works with the latest ARM computing cluster at ORNL. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

The Atmospheric Radiation Measurement Data Center is shepherding changes to its operations to make the treasure trove of data more easily available accessible and useful to scientists studying Earth’s climate.

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.