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Scientists genetically engineered bacteria for itaconic acid production, creating dynamic controls that separate microbial growth and production phases for increased efficiency and acid yield. Credit: NREL

A research team led by Oak Ridge National Laboratory bioengineered a microbe to efficiently turn waste into itaconic acid, an industrial chemical used in plastics and paints.

Urban climate modeling

Researchers at Oak Ridge National Laboratory have identified a statistical relationship between the growth of cities and the spread of paved surfaces like roads and sidewalks. These impervious surfaces impede the flow of water into the ground, affecting the water cycle and, by extension, the climate.

Researchers Adam Guss and Melissa Tumen-Velasquez work with microbes to understand how the organisms consume plastics and break them into chemical components that can be used to make higher-value products.

From soda bottles to car bumpers to piping, electronics, and packaging, plastics have become a ubiquitous part of our lives. 

Suman Debnath is using simulation algorithms to accelerate understanding of the modern power grid and enhance its reliability and resilience. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Planning for a digitized, sustainable smart power grid is a challenge to which Suman Debnath is using not only his own applied mathematics expertise, but also the wider communal knowledge made possible by his revival of a local chapter of the IEEE professional society.

ORNL scientists have optimized the Pseudomonas putida bacterium to digest five of the most abundant components of lignocellulosic biomass simultaneously, supporting a highly efficient conversion process to create renewable fuels and chemicals from plants. Credit: Alli Werner/NREL,U.S. Dept of Energy

ORNL scientists have modified a single microbe to simultaneously digest five of the most abundant components of lignocellulosic biomass, a big step forward in the development of a cost-effective biochemical conversion process to turn plants into 

Coronavirus graphic

In the race to identify solutions to the COVID-19 pandemic, researchers at the Department of Energy’s Oak Ridge National Laboratory are joining the fight by applying expertise in computational science, advanced manufacturing, data science and neutron science.

Data scientists at Oak Ridge National Laboratory have completed a study of long-term trends in the relationship between the timing of tree leafing and rising temperatures in the United States. The information is being incorporated into DOE’s Energy Exascale Earth System Model. Photo Credit: Oak Ridge National Laboratory, U.S. Dept. of Energy

A team of scientists led by Oak Ridge National Laboratory found that while all regions of the country can expect an earlier start to the growing season as temperatures rise, the trend is likely to become more variable year-over-year in hotter regions.

ORNL-developed cryogenic memory cell circuit designs fabricated onto these small chips by SeeQC, a superconducting technology company, successfully demonstrated read, write and reset memory functions. Credit: Carlos Jones/Oak Ridge National Laboratory, U.S. Dept. of Energy

Scientists at have experimentally demonstrated a novel cryogenic, or low temperature, memory cell circuit design based on coupled arrays of Josephson junctions, a technology that may be faster and more energy efficient than existing memory devices.

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Researchers across the scientific spectrum crave data, as it is essential to understanding the natural world and, by extension, accelerating scientific progress.

An artist rendering of the SKA’s low-frequency, cone-shaped antennas in Western Australia. Credit: SKA Project Office.

For nearly three decades, scientists and engineers across the globe have worked on the Square Kilometre Array (SKA), a project focused on designing and building the world’s largest radio telescope. Although the SKA will collect enormous amounts of precise astronomical data in record time, scientific breakthroughs will only be possible with systems able to efficiently process that data.