Credit: Jenny Woodbery/Oak Ridge National Laboratory, U.S. Dept. of Energy
Plants–Surviving the heat
A study by Oak Ridge National Laboratory detailed the response and recovery of certain tree species after short-term, extreme weather events such as heat waves. Scientists exposed sets of four different saplings to dramatic temperature swings that peaked above 120 F, or around 50 C, in a climate-controlled test chamber. Sensors attached to each tree and located throughout the chamber tracked telltale signs of heat and drought stress such as fluxes in carbon uptake and shifts in water demand. "By monitoring specific trait behavior, we characterized each tree's reaction to being kicked into survival mode for brief periods of time," said ORNL's Anirban Guha. "We found that during simulated heat waves, the entire plant mechanism was impacted, which affects its year-long survival." The ORNL-led team's findings, which were published in Environmental Research Letters, will improve predictive Earth system models. [Contact: Sara Shoemaker, (865) 576-9219; [email protected]]
Image: https://www.ornl.gov/sites/default/files/ORNL_heat_wave_chamber2.jpg
Caption: ORNL's Jeffrey Warren (left) and Anirban Guha used a climate-controlled test chamber to simulate heat waves that peaked above 120 F and analyzed the impact on certain tree species. Credit: Genevieve Martin/Oak Ridge National Laboratory, U.S. Dept. of Energy.
Video: https://youtu.be/5j-jEEJwLCU
Caption: ORNL scientists exposed sets of four different saplings to dramatic temperature swings that peaked above 120 F, or around 50 C, in a climate-controlled test chamber. Credit: Jenny Woodbery/Oak Ridge National Laboratory, U.S. Dept. of Energy.
Sensors–Vehicle fingerprinting
Algorithms designed to parse data gathered by roadside sensors could make it easier to identify vehicles sought in AMBER Alerts and to assist researchers studying traffic patterns. Oak Ridge National Laboratory scientists built a sensor platform to collect detailed images of cars, as well as electrical pulses and audio signals from engines, to uniquely identify vehicles. "Two cars with an identical make, model and color would be difficult to differentiate on the road," said ORNL's Ryan Kerekes. "With data pulled from sensors, we can use machine learning to extract important features to create a vehicle 'fingerprint.'" The algorithms could be deployed with specialized sensor arrays or modified to apply to existing traffic cameras. The ORNL team presented their work at an IEEE Vehicle Technology Conference. Research is ongoing to upgrade sensors to capture larger vehicles and to improve matching algorithms. [Contact: Stephanie Seay, (865) 576-9894; [email protected]]
Image #1: https://www.ornl.gov/sites/default/files/VehicleIDSensorinCone2.jpg
Caption: ORNL researchers test a sensor array inside a traffic cone as they developed a method to more accurately identify vehicles. Credit: Jason Richards/Oak Ridge National Laboratory, U.S. Dept. of Energy.
Image #2: https://www.ornl.gov/sites/default/files/VehicleIDSensorKerekesTokola.jpg
Caption: ORNL scientists Ryan Kerekes (left) and Ryan Tokola codeveloped a prototype of the sensor platform for vehicle fingerprinting. Credit: Jason Richards/Oak Ridge National Laboratory, U.S. Dept. of Energy.
Computing–Filling the gaps
Researchers from the Max Planck Institute, renowned for advances in nonmetallic catalysis, leveraged computational modeling support from Oak Ridge National Laboratory to overcome a major limiting factor in the breakdown of simple organic compounds called olefins. Olefins are among nature's most abundant chemical compounds and are commonly obtained from crude oil. But current industrial processes to catalyze olefins into useful products are energy-intensive and costly. ORNL's Dmytro Bykov ran a series of calculations on experimental data provided by Max Planck to fill in the gaps. "The team had theorized the most promising catalysis candidates, but they needed details on the reaction mechanisms that are difficult to determine in a lab setting," Bykov said. The resulting three-dimensional models helped the team better predict the most plausible ways to catalyze olefins. Their discovery was published in the journal Science. [Contact: Sara Shoemaker, (865) 576-9219; [email protected]]
Image: https://www.ornl.gov/sites/default/files/ORNL_catalysis_of_olefins_0.png
Caption: Computational modeling helped researchers visualize possible reactions when olefins are exposed to various catalysts. Credit: Dmytro Bykov/Oak Ridge National Laboratory, U.S. Dept. of Energy.
Materials–Taking the heat
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 the heat near the Sun, will get closer to the Sun than prior missions. The ORNL team exposed the shield assembly to a searing 3,227 F for up to 72 hours and simulated solar intensity of 65 watts per square centimeter using ORNL's Radioisotope Power Systems Program and Plasma-Arc Lamp facilities, respectively. This exceeded the worst conditions that the mission is predicted to experience in the corona. Andrew Driesman, project manager of the Johns Hopkins University Applied Physics Laboratory, which designed, built and will operate the spacecraft for NASA, said, "ORNL's support was crucial in completing our testing on time by helping to solve difficult materials and technical challenges." [Contact: Dawn Levy, (865) 576-6448; [email protected]]
Image: https://www.ornl.gov/sites/default/files/NASA_Parker_Solar_Probe_rendering.jpg
Caption: NASA's Parker Solar Probe, shown in this artist's concept, is scheduled to launch on July 31 to contribute data that may improve the agency's ability to forecast space weather, which can disrupt communications satellites and power grids, and may demystify why the Sun's corona is hotter than its surface. Credit: Steve Gribben/NASA, Johns Hopkins APL.
Neutrons–On the down-low
An Oak Ridge National Laboratory-led team has observed how a prolific class of antibiotics may be losing its effectiveness as certain bacteria develop drug resistance by acquiring enzymes known as aminoglycoside modifying enzymes. Aminoglycosides are commonly used in antibiotics to treat tuberculosis, meningitis and listeriosis. Using X-rays and neutron diffraction, researchers found a known but previously undetected biological architecture called a catalytic triad within this enzyme group. The team identified a low-barrier hydrogen bond, involving a single hydrogen atom formed in this catalytic triad, which is crucial for the enzyme's ability to disrupt the drugs' molecular structure and render them ineffective to combat pathogenic bacteria. Detailed in Science Advances, this information provides new insights that could help improve future drug design. [Contact: Sara Shoemaker, (865) 576-9219; [email protected]]
Image: https://www.ornl.gov/sites/default/files/ORNL_neutrons_low-barrierH.png
Caption: The ORNL-led team identified a low-barrier hydrogen bond, involving a single hydrogen atom formed in this catalytic triad, which is crucial for the enzyme's ability to disrupt the drugs' molecular structure and render them ineffective to combat pathogenic bacteria. Credit: Oak Ridge National Laboratory, U.S. Dept. of Energy.
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Media Contact
Sara Shoemaker
[email protected]
865-576-9219
@ORNL
http://www.ornl.gov
Original Source
https://www.ornl.gov/news/story-tips http://dx.doi.org/10.1088/1748-9326/aabcd8
