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Media Contacts
![Geothermal energy storage system](/sites/default/files/styles/list_page_thumbnail/public/2020-01/Geothermal_graphic_0.jpg?h=f9060f2b&itok=W6LxcJwG)
Oak Ridge National Laboratory researchers created a geothermal energy storage system that could reduce peak electricity demand up to 37% in homes while helping balance grid operations.
![Smart Neighborhood homes](/sites/default/files/styles/list_page_thumbnail/public/2020-01/04.09.TD-SMartHome_0.jpg?h=5b5a5437&itok=22S5Tle1)
To better determine the potential energy cost savings among connected homes, researchers at Oak Ridge National Laboratory developed a computer simulation to more accurately compare energy use on similar weather days.
![Shown here is a computer-aided design of the hot stamping die with visible cooling channels. Credit: Oak Ridge National Laboratory, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2019-11/Built-to-last.png?h=a86e7ddf&itok=3DoSQK7P)
Researchers demonstrated that an additively manufactured hot stamping die can withstand up to 25,000 usage cycles, proving that this technique is a viable solution for production.
![Layering on the strength](/sites/default/files/styles/list_page_thumbnail/public/2019-09/Z-pinning-printed%20wall_ORNL-2_0.png?h=c8a62123&itok=EnqQdQih)
A team including Oak Ridge National Laboratory and University of Tennessee researchers demonstrated a novel 3D printing approach called Z-pinning that can increase the material’s strength and toughness by more than three and a half times compared to conventional additive manufacturing processes.
![Heat impact map](/sites/default/files/styles/list_page_thumbnail/public/2019-07/Winter_HDD_Change_ORNL.gif?h=e87b941e&itok=8t83D_u_)
A detailed study by Oak Ridge National Laboratory estimated how much more—or less—energy United States residents might consume by 2050 relative to predicted shifts in seasonal weather patterns
![Desalination process](/sites/default/files/styles/list_page_thumbnail/public/2019-07/hydrophopicDesal04_0.jpg?h=5473d993&itok=bUBkpGOa)
A new method developed at Oak Ridge National Laboratory improves the energy efficiency of a desalination process known as solar-thermal evaporation.
![Batteries—Polymers that bind](/sites/default/files/styles/list_page_thumbnail/public/2019-06/Batteries-Polymers_that_bind_0.png?h=dec22bcf&itok=oJ7mroY1)
A team of researchers at Oak Ridge National Laboratory have demonstrated that designed synthetic polymers can serve as a high-performance binding material for next-generation lithium-ion batteries.
![Low-cost, compact, printed sensor that can collect and transmit data on electrical appliances for better load monitoring](/sites/default/files/styles/list_page_thumbnail/public/2019-03/2019-P01301_0.jpg?h=c6980913&itok=y0S4bq0p)
Scientists at Oak Ridge National Laboratory have developed a low-cost, printed, flexible sensor that can wrap around power cables to precisely monitor electrical loads from household appliances to support grid operations.
![Laminations such as these are compiled to form the core of modern electric vehicle motors. ORNL has developed a software toolkit to speed the development of new motor designs and to improve the accuracy of their real-world performance.](/sites/default/files/styles/list_page_thumbnail/public/2019-02/Motors_OeRSTED_0.jpg?h=af53702d&itok=mT24R4WI)
Oak Ridge National Laboratory scientists have created open source software that scales up analysis of motor designs to run on the fastest computers available, including those accessible to outside users at the Oak Ridge Leadership Computing Facility.
![Researchers used machine learning methods on the ORNL Compute and Data Environment for Science, or CADES, to map vegetation communities in the Kougarok Watershed on the Seward Peninsula of Alaska. The colors denote different types of vegetation, such as w Researchers used machine learning methods on the ORNL Compute and Data Environment for Science, or CADES, to map vegetation communities in the Kougarok Watershed on the Seward Peninsula of Alaska. The colors denote different types of vegetation, such as w](/sites/default/files/styles/list_page_thumbnail/public/rs2019_highlight_plot_3d.png?itok=5bROV_ys)
A team of scientists led by Oak Ridge National Laboratory used machine learning methods to generate a high-resolution map of vegetation growing in the remote reaches of the Alaskan tundra.