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Media Contacts
![Pella Marion](/sites/default/files/styles/list_page_thumbnail/public/2021-03/WMMPA%20Pella%20Marion%20790_Small.jpg?h=f14a4ec1&itok=ItU-Ca6U)
A new Department of Energy report produced by Oak Ridge National Laboratory details national and international trends in hydropower, including the role waterpower plays in enhancing the flexibility and resilience of the power grid.
![Deep-sea hydrothermal vent chimneys on Brother’s Volcano’s northwest caldera wall create a unique environment for microbes. Credit: Anna-Louise Reysenbach, NSF, ROV Jason and 2018 ©Woods Hole Oceanographic Institution](/sites/default/files/styles/list_page_thumbnail/public/2021-01/Hydrothermal%20vent_0.jpg?h=c1a8531e&itok=ddzDJY79)
Oak Ridge National Laboratory contributed to an international study that found almost 300 novel types of microbes living near a deep sea volcano. These microbes, which could be used in biotechnology, reveal new insights about their extreme underwater environment.
![The TRITON model provides a detailed visualization of the flooding that resulted when Hurricane Harvey stalled over Houston for four days in 2017. Credit: Mario Morales-Hernández/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-01/TRITON%20screenshot.png?h=4a7d1ed4&itok=IEra5eDk)
A new tool from Oak Ridge National Laboratory can help planners, emergency responders and scientists visualize how flood waters will spread for any scenario and terrain.
![Cars and coronavirus](/sites/default/files/styles/list_page_thumbnail/public/2020-08/Transportation-Gauging_pandemic_impact_ORNL_0.jpg?h=4a7d1ed4&itok=Xqx4kknO)
Oak Ridge National Laboratory researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.
![SPRUCE experiment](/sites/default/files/styles/list_page_thumbnail/public/2020-08/SPRUCE_0.png?h=9afda364&itok=zCibJUsI)
Oak Ridge National Laboratory scientists evaluating northern peatland responses to environmental change recorded extraordinary fine-root growth with increasing temperatures, indicating that this previously hidden belowground mechanism may play an important role in how carbon-rich peatlands respond to warming.
![Map with focus on sub-saharan Africa](/sites/default/files/styles/list_page_thumbnail/public/2020-07/firms3-Africa-NASA_0.jpg?h=27f1d52b&itok=G8uUS5cH)
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.
An international team of scientists found that rules governing plant growth hold true even at the edges of the world in the Arctic tundra.
![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.
![Beneficial microbes, shown in red, aid Sphagnum mosses in using nitrogen from the air to fuel plant growth. ORNL scientists have shown this nitrogen fixing activity declines with warming temperatures. Credit: David Weston/Oak Ridge National Laboratory, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2019-11/full_leaf.jpg?h=5a90f57a&itok=FLlZkQ40)
A team of scientists found that critical interactions between microbes and peat moss break down under warming temperatures, impacting moss health and ultimately carbon stored in soil.
![Project bridges compute staff, resources at ORNL and VA health data to speed suicide risk screening for US veterans. Image Credit: Carlos Jones, ORNL](/sites/default/files/styles/list_page_thumbnail/public/2019-08/VA_REACHVET1%5B6%5D_0.jpg?h=173ee000&itok=-eA5t15j)
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.