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Media Contacts
![The 2021 Fuel Economy Guide, compiled by ORNL researchers, provides tips for keeping fuel costs down and helps consumers find the most fuel-efficient vehicle. Credit: ORNL/U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-01/Transportation%20-%20Easy%20on%20the%20pedals_0.jpg?h=f0649f60&itok=11HQCqNO)
Fuel economy can take a tumble when temperatures plummet, according to the Department of Energy’s 2021 Fuel Economy Guide. Compiled by researchers at Oak Ridge National Laboratory, the guide includes several tips to improve a vehicle’s fuel performance.
![An X-ray CT image of a 3D-printed metal turbine blade was reconstructed using ORNL’s neural network and advanced algorithms. Credit: Amir Ziabari/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-01/Manufacturing%20-%20Defect%20detection%202_0.jpg?h=259e5a75&itok=CwpLQv6U)
Algorithms developed at Oak Ridge National Laboratory can greatly enhance X-ray computed tomography images of 3D-printed metal parts, resulting in more accurate, faster scans.
![Drawing of air taxi](/sites/default/files/styles/list_page_thumbnail/public/2020-11/airTaxi_730x457_0.jpg?h=f017b3e4&itok=FiV6MYk7)
If air taxis become a viable mode of transportation, Oak Ridge National Laboratory researchers have estimated they could reduce fuel consumption significantly while alleviating traffic congestion.
![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.
![Batteries - The 3D connection](/sites/default/files/styles/list_page_thumbnail/public/2020-05/Batteries_3D%20story%20tip_2.jpg?h=aeb34e32&itok=puhZ_584)
Oak Ridge National Laboratory researchers have developed a thin film, highly conductive solid-state electrolyte made of a polymer and ceramic-based composite for lithium metal batteries.
![Wireless charging – Special delivery for UPS](/sites/default/files/styles/list_page_thumbnail/public/2020-05/UPS_wireless_power_story%20tip_3000.jpg?h=3748d94f&itok=Xf2MDLEi)
Researchers at Oak Ridge National Laboratory demonstrated a 20-kilowatt bi-directional wireless charging system on a UPS plug-in hybrid electric delivery truck, advancing the technology to a larger class of vehicles and enabling a new energy storage method for fleet owners and their facilities.
![A new computational approach by ORNL can more quickly scan large-scale satellite images, such as these of Puerto Rico, for more accurate mapping of complex infrastructure like buildings. Credit: Maxar Technologies and Dalton Lunga/Oak Ridge National Laboratory, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2020-02/Puerto_Rico_Resflow9.png?h=a0a1befd&itok=5n2fss_e)
A novel approach developed by scientists at ORNL can scan massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours versus days.