Scientists at ORNL used their expertise in quantum biology, artificial intelligence and bioengineering to improve how CRISPR Cas9 genome editing tools work on organisms like microbes that can be modified to produce renewable fuels and chemicals.
Researchers from Oak Ridge National Laboratory and Northeastern University modeled how extreme conditions in a changing climate affect the land’s ability to absorb atmospheric carbon — a key process for mitigating human-caused emissions. They found that 88% of Earth’s regions could become carbon emitters by the end of the 21st century.
An advance in a topological insulator material — whose interior behaves like an electrical insulator but whose surface behaves like a conductor — could revolutionize the fields of next-generation electronics and quantum computing, according to scientists at ORNL.
A study led by Oak Ridge National Laboratory researchers identifies a new potential application in quantum computing that could be part of the next computational revolution.
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.
A study by Oak Ridge National Laboratory researchers has demonstrated how satellites could enable more efficient, secure quantum networks.
Zheng Gai, a senior staff scientist at ORNL’s Center for Nanophase Materials Sciences, has been selected as editor-in-chief of the Spin Crossover and Spintronics section of Magnetochemistry.
Critical Materials Institute researchers at Oak Ridge National Laboratory and Arizona State University studied the mineral monazite, an important source of rare-earth elements, to enhance methods of recovering critical materials for energy, defense and manufacturing applications.
Using existing experimental and computational resources, a multi-institutional team has developed an effective method for measuring high-dimensional qudits encoded in quantum frequency combs, which are a type of photon source, on a single optical chip.
A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.