Population-specific diversity within fungi species could enable improved drug discovery

Scientists at Oak Ridge National Laboratory and the University of Wisconsin–Madison have discovered that genetically distinct populations within the same species of fungi can produce unique mixes of secondary metabolites, which are organic compounds with applications in medicine, industry and agriculture. The finding could open new avenues for drug discovery and provide a deeper understanding of fungal evolution.


Tiny but mighty precipitates toughen a structural alloy

Scientists at the Department of Energy’s Oak Ridge National Laboratory and the University of Tennessee, Knoxville, have found a way to simultaneously increase the strength and ductility of an alloy by introducing tiny precipitates into its matrix and tuning their size and spacing. The precipitates are solids that separate from the metal mixture as the alloy cools. The results, published in the journal Naturewill open new avenues for advancing structural materials.


Manufacturing – Powered by nature

A team of researchers at Oak Ridge National Laboratory demonstrated the ability to additively manufacture power poles from bioderived and recycled materials, which could more quickly restore electricity after natural disasters.

Using the Big Area Additive Manufacturing system, the team 3D printed a 55-foot pole designed as a closed cylindrical structure. They evaluated three different composite materials with glass fibers including cellulose ester, recycled polycarbonate and bamboo fiber reinforced polystyrene.


Electric vehicles – Charged-up planning

Researchers at Oak Ridge National Laboratory have developed a nationwide modeling tool to help infrastructure planners decide where and when to locate electric vehicle charging stations along interstate highways. The goal is to encourage the adoption of EVs for cross-country travel.


Energy – Building a better thermostat

Oak Ridge National Laboratory researchers designed and field-tested an algorithm that could help homeowners maintain comfortable temperatures year-round while minimizing utility costs.

The algorithm learns over time to keep the home at residents’ desired temperature settings while minimizing energy costs and adjusting to environmental conditions, all with no existing knowledge of the building. Results suggest the algorithm could save homeowners as much as 25% on annual utility bills.