Feature Articles

Recent advancements at the U.S. Department of Energy’s Oak Ridge National Laboratory show that 3D-printed metal molds offer a faster, more cost-effective and flexible approach to producing large composite components for mass-produced vehicles than traditional tooling methods.

 

The research, conducted at the Manufacturing Demonstration Facility, or MDF, at ORNL, confirms that large-scale additive manufacturing is well-suited for creating complex metal molds, with efficiencies that could accelerate the adoption of lightweight composite materials in the automotive sector.

 

Scientists at ORNL have developed a first-ever method of detecting ribonucleic acid, or RNA, inside plant cells using a technique that results in a visible fluorescent signal. The technology can help researchers detect and track changes in RNA and gene expression in real time, providing a powerful tool for the development of hardier bioenergy and food crops and for detection of unwanted plant modifications, pathogens and pests.  

A research team from the Department of Energy’s Oak Ridge National Laboratory, in collaboration with North Carolina State University, has developed a simulation capable of predicting how tens of thousands of electrons move in materials in real time, or natural time rather than compute time.

The Heartbeat Detector, which utilizes an algorithm developed by researchers at the Department of Energy’s Oak Ridge National Laboratory, detects individuals attempting to hide in vehicles at security checkpoints. The technology has become the industry standard since it was first licensed by Geovox Security Inc. from ORNL nearly 30 years ago and it continues to evolve.

Microbiologist and entrepreneur Jesse Labbé excels at cultivating discovery and innovation. As the new director of the Biosciences Division at the Department of Energy’s Oak Ridge National Laboratory, he’s leading a team focused on science and technologies to boost U.S. competitiveness, strengthen national and energy security and advance human health.

To create a more resilient electric grid that meets the nation’s increasing power demands, utilities are incorporating a wider array of energy sources. But this shift requires the ability to predict how the grid will react to fluctuations in the flow of electricity from new sources of power. Researchers at the Department of Energy’s Oak Ridge National Laboratory have developed a dynamic modeling method that uses machine learning to provide accurate simulations of grid behavior while maintaining what is called a “black box” approach. This technique does not require details about the proprietary technology inside the equipment — in this case, a type of power electronics called an inverter. 

ORNL has developed a network of autonomous science laboratories outfitted with state-of-the-art artificial intelligence and robotic systems and connected to the lab’s world-class user facilities. The goal is to supercharge the research process while producing scientific breakthroughs that would be unachievable in a traditional setting.

Scientists at the Department of Energy’s Oak Ridge National Laboratory and the University of Cincinnati achieved a breakthrough in understanding the vulnerability of microbes to the butanol they produce during fermentation of plant biomass. The discovery could pave the way for more efficient production of domestic fuels, chemicals and materials.

Researchers at Georgia State University used the Summit supercomputer to study an elaborate molecular pathway called nucleotide excision repair, or NER. They built a computer model of a critical NER component called the pre-incision complex, or PInC, which plays a key role in regulating DNA repair processes in the latter stages of the NER pathway. Decoding NER’s sophisticated sequence of events and the role of PInC in the pathway could provide key insights into developing novel treatments and preventing conditions that lead to premature aging and certain types of cancer.

A workshop led by scientists at ORNL sketched a road map toward a longtime goal: development of autonomous, or self-driving, next-generation research laboratories.