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Oak Ridge National Laboratory researchers used big area additive manufacturing with metal to 3D print a steel component for a wind turbine, proving the technique as a viable alternative to conventional fabrication methods. Credit: ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory researchers recently used large-scale additive manufacturing with metal to produce a full-strength steel component for a wind turbine, proving the technique as a viable alternative to

Results show change in annual aridity for the years 2071-2100 compared to 1985-2014. Brown shadings (negative numbers) indicate drier conditions. Black dots indicate statistical significance at the 90% confidence level. Credit: Jiafu Mao/ORNL, U.S. Dept. of Energy

A new analysis from Oak Ridge National Laboratory shows that intensified aridity, or drier atmospheric conditions, is caused by human-driven increases in greenhouse gas emissions. The findings point to an opportunity to address and potentially reverse the trend by reducing emissions.

A new process developed by Oak Ridge National Laboratory leverages deep learning techniques to study cell movements in a simulated environment, guided by simple physics rules similar to video-game play. Credit: MSKCC and UTK

Scientists have developed a novel approach to computationally infer previously undetected behaviors within complex biological environments by analyzing live, time-lapsed images that show the positioning of embryonic cells in C. elegans, or roundworms. Their published methods could be used to reveal hidden biological activity. 

ORNL scientists used an electron beam for precision machining of nanoscale materials. Cubes were milled to change their shape and could also be removed from an array. Credit: Kevin Roccapriore/ORNL, U.S. Dept. of Energy

Drilling with the beam of an electron microscope, scientists at ORNL precisely machined tiny electrically conductive cubes that can interact with light and organized them in patterned structures that confine and relay light’s electromagnetic signal.

QLAN submit - A team from the U.S. Department of Energy’s Oak Ridge National Laboratory, Stanford University and Purdue University developed and demonstrated a novel, fully functional quantum local area network, or QLAN, to enable real-time adjustments to information shared with geographically isolated systems at ORNL using entangled photons passing through optical fiber. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

A rapidly emerging consensus in the scientific community predicts the future will be defined by humanity’s ability to exploit the laws of quantum mechanics.

An ORNL-led team studied the SARS-CoV-2 spike protein in the trimer state, shown here, to pinpoint structural transitions that could be disrupted to destabilize the protein and negate its harmful effects. Credit: Debsindhu Bhowmik/ORNL, U.S. Dept. of Energy

To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from ORNL developed a novel technique.

The Energy Exascale Earth System Model project reliably simulates aspects of earth system variability and projects decadal changes that will critically impact the U.S. energy sector in the future. A new version of the model delivers twice the performance of its predecessor. Credit: E3SM, Dept. of Energy

A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.

Ultra Safe Nuclear Corporation has licensed a novel method to 3D print highly resistant components for use in nuclear reactor designs. USNC Executive Vice President Kurt Terrani, formerly of ORNL, said the novel method will allow the company to make parts with desired complex shapes more efficiently. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

A novel method to 3D print components for nuclear reactors, developed by the Department of Energy’s Oak Ridge National Laboratory, has been licensed by Ultra Safe Nuclear Corporation.

This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography.  Credit: Ada Sedova/ORNL, U.S. Dept. of Energy

A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.