Story Tips

From the Department of Energy’s Oak Ridge National Laboratory

January 2018

 

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Biology—Telltale microbes

 

A new process to identify certain microbes in women could be used to diagnose endometriosis without invasive surgery, even before symptoms start. A collaborative research team analyzed bacteria from a small sample of premenopausal women undergoing laparoscopic surgery for suspected endometriosis. Endometriosis occurs when the uterus’ lining grows outside the uterus, resulting in painful lesions and possible infertility. Researchers from Southern Illinois University School of Medicine, Michigan State University and Oak Ridge National Laboratory studied microbes from women with and without endometriosis and compared bacteria from the uterus with vaginal microbes. “We determined that the uterine microbiome is not simply a subset of the vaginal microbiome and that microbial diversity increased with stage III endometriosis,” said ORNL’s Melissa Cregger, lead author of a pilot study published in Reproductive Immunology. The team plans to further analyze the microbiome to diagnosis ovarian and endometrial cancers and evaluate responses to treatment. [Contact: Sara Shoemaker, (865) 576-9219; shoemakerms@ornl.gov]

 

Image: https://www.ornl.gov/sites/default/files/BraundmeierA_0011_0.jpg

 

Caption: Studying reproductive microbiomes could help identify women with endometriosis without an invasive surgical procedure, even before symptoms start. Credit: Jason Johnson/SIU School of Medicine

 

Data—Plug-in learning

 

For smarter data management and analysis, researchers have developed a low-power neuromorphic device based on spiking neural networks that can quickly and more efficiently analyze and classify data. The versatile platform, which will be compatible with instruments that collect data during scientific experiments, becomes “smarter” as it classifies large amounts of information into smaller, more manageable datasets. “The device is designed to get better at the task it was trained to do,” said Oak Ridge National Laboratory’s Catherine Schuman, who developed the device’s training algorithms. She and University of Tennessee collaborator Garrett Rose advise UT students who demonstrated the technology’s data-crunching abilities on well-known biology and medical research datasets. The ORNL-UT team published their results in an IEEE journal. The researchers are testing the device’s capabilities on scientific data such as complex neutrino collision data. [Contact: Sara Shoemaker, (865) 576-9219; shoemakerms@ornl.gov]

 

Image: https://www.ornl.gov/sites/default/files/news/images/Spiking_neural_network_ORNL.jpg

 

Caption: An example of a spiking neural network shows how data can be classified using the neuromorphic device. Credit: Catherine Schuman and Margaret Drouhard/Oak Ridge National Laboratory, U.S. Dept. of Energy

 

Fossil energy—Neutrons run deep

 

To improve models for drilling, hydraulic fracturing and underground storage of carbon dioxide, Oak Ridge National Laboratory scientists used neutrons to understand how water flows through fractured rock. Researchers used neutrons bouncing off the hydrogen in water molecules to see inside the rock’s microstructure without destroying it and quantify water uptake in real time. “One of the biggest challenges with shale is that it’s such a complex system,” ORNL’s Victoria DiStefano said. “Neutrons help us grasp the complex rock and fracture properties, which determine how quickly water uptake occurs in the rock.” Results of the study, which used rock samples from the oil- and gas-rich Eagle Ford Shale Formation in Texas, are detailed in the Journal of Earth Science. Future research will explore how fracture characteristics, such as roughness and mineralogy, affect these interactions. [Contact: Stephanie Seay, (865) 576-9894; seaysg@ornl.gov]

 

Image #1: https://www.ornl.gov/sites/default/files/Fossil_energy_ORNL1.jpg

 

Caption #1: Victoria DiStefano, University of Tennessee Bredesen Center graduate student researcher, and her adviser, Lawrence Anovitz of ORNL, study rock samples from the Eagle Ford Shale Formation in Texas. Credit: Jason Richards/Oak Ridge National Laboratory, U.S. Dept. of Energy

 

Image #2: https://www.ornl.gov/sites/default/files/Fossil_energy_ORNL2.jpg

 

Caption #2: An ORNL-led team used neutrons to understand how water flows through fractured rock. Credit: Jason Richards/Oak Ridge National Laboratory, U.S. Dept. of Energy

 

Image #3: https://www.ornl.gov/sites/default/files/Fossil_energy_ORNL3.jpg

 

Caption #3: A computed tomography image details fractures in rock samples from the Eagle Ford Shale Formation in Texas. Credit: University of Texas at Austin

 

Video clip: https://www.ornl.gov/sites/default/files/giphy-13.gif

 

Video caption: A computed tomography image details fractures in rock samples from the Eagle Ford Shale Formation in Texas. Credit: University of Texas at Austin

 

Transportation—Better charging access

 

Officials responsible for anticipating the demand for electric vehicle charging stations could get help through a sophisticated new method developed at Oak Ridge National Laboratory. The method considers electric vehicle volume and the random timing of vehicles arriving at charging stations to determine an optimal number of chargers needed in the near and long term. “Our method can provide insights for planners to strategically balance the cost of new infrastructure with establishing a level of service that can enable and sustain increased use of electric vehicles,” said ORNL’s Zhenhong Lin. The study, published in Transportation Research Part E, mapped the number of direct current fast chargers needed at new stations between California cities if regional infrastructure were added in stages through 2029. The method can also be applied to other states, regions and the nation. [Contact: Kim Askey, (865) 576-2841; askeyka@ornl.gov]

 

Image: https://www.ornl.gov/sites/default/files/news/images/Untitled-1%20%281%29.jpg

 

Caption: An analysis from Oak Ridge National Laboratory shows the optimal number of fast chargers needed at electric vehicle charging stations between California cities in a multi-stage deployment through 2029. Credit: Fie Xie/Oak Ridge National Laboratory, U.S. Dept. of Energy

 

Materials—Shape-memory conductors

 

A novel approach that creates a renewable, leathery material—programmed to remember its shape—may offer a low-cost alternative to conventional conductors for applications in sensors and robotics. To make the bio-based, shape-memory material, Oak Ridge National Laboratory scientists streamlined a solvent-free process that mixes rubber with lignin—the by-product of woody plants used to make biofuels. They fashioned the leathery material into small strips and brushed on a thin layer of silver nanoparticles to activate electrical conductivity. The strips were stretched or curled and then frozen as part of the process to program the material to return to its intended shape, which occurs after the application of low heat. “The performance of this polymer can be tuned further,” said ORNL’s Amit Naskar. “Variant lignins can be used at different ratios, which determines the material’s pliability.” ORNL detailed their method in Macromolecules. [Contact: Sara Shoemaker, (865) 576-9219; shoemakerms@ornl.gov]

 

Image: https://www.ornl.gov/sites/default/files/Screen%20Shot%202017-12-22%20at%202.01.38%20PM.jpg

 

Caption: An Oak Ridge National Laboratory team developed a novel approach that creates a renewable, leathery material—programmed to remember its shape—which may offer a low-cost alternative to conventional conductors for applications in sensors and robotics.

Credit: Jenny Woodbery/Oak Ridge National Laboratory, U.S. Dept. of Energy

 

Video: https://www.youtube.com/watch?time_continue=1&v=CGGkJo6WJJc

 

Video caption: An Oak Ridge National Laboratory team developed a novel approach that creates a renewable, leathery material—programmed to remember its shape—which may offer a low-cost alternative to conventional conductors for applications in sensors and robotics. Credit: Jenny Woodbery/Oak Ridge National Laboratory, U.S. Dept. of Energy

 

Neutrons—Exotic particles

 

A novel approach for studying magnetic behavior in a material called alpha-ruthenium trichloride may have implications for quantum computing. By suppressing the material’s magnetic order, scientists from Oak Ridge National Laboratory and the University of Tennessee observed behavior consistent with exotic particles that are predicted to emerge when energy is added to a quantum spin liquid, or QSL. QSLs exist in certain materials where magnetic moments fluctuate in a liquid-like state rather than forming an ordered pattern. The team disrupted the material’s magnetic order by substituting iridium ions for ruthenium, then used neutron scattering to characterize the resulting magnetic behavior. “Through this process, we saw hints of highly sought-after particles, which were robust and perhaps even more intense in the QSL state,” said UT’s Paige Kelley, coauthor of a study published in Physical Review Letters. “This discovery could be the future basis for a topologically protected qubit in a quantum computer.” [Contact: Paul Boisvert, 865-576-9047; boisvertpl@ornl.gov]

 

Image: https://www.ornl.gov/sites/default/files/Neutrons-Exotic_particles.jpg

 

Caption: Long-range ordering of magnetic ions in a graphene-like material (on left) is disrupted by placing nonmagnetic ions on the honeycomb lattice, resulting in a quantum spin liquid state (on right). As neutrons (blue line) scatter off the magnetically disordered material, they produce unusual particles such as Majorana fermions (purple wave) that move through the lattice disrupting or breaking apart magnetic interactions between “spinning” electrons. Credit: Jill Hemman/Oak Ridge National Laboratory, U.S. Dept. of Energy.