Summer 2021

Congratulations to Nathan Youngblood and his colleagues, who recently received a $1.2 million 4-year grant from the NSF to develop a new type of computer chip that uses laser light for AI and machine learning computation.

AI and machine learning’s rapid growth in sophistication and large-scale implementation has caused the demand for computing power to increase at a rate where conventional computing paradigms and hardware platforms are struggling to keep up.

The chip, called a “hybrid co-processing unit” or HCU, aims to address this challenge by combining traditional electronics with photonics and using light generated by lasers instead of electricity for data processing. This will greatly accelerate the computing speed and efficiency of AI and machine learning applications, while at the same time reducing energy consumption. 

The team will be fabricating thousands of photonic elements and millions of transistors together in a cost-effective and scalable manner as well as building computer models to simulate every aspect of the device. By 2025, they expect to have a working, physical prototype and be poised to manufacture the device in larger quantities and at a scale capable of moving into the marketplace.
 

Read some of their recent work here!

Congratulations to Heng Ban, Paul Ohodnicki, and Kevin Chen for winning $1.6 million of advanced nuclear energy R&D funding from the U.S. Department of Energy! 

Heng Ban’s project, titled “Fragmentation and Thermal Energy Transport of Chromia-doped Fuels Under Transient Conditions,” will use various aspects of engineering-scale modeling and experimental testing to understand thermal energy transport from high burnup accident tolerant fuels. The team hopes to fill a major knowledge gap for modeling and simulating transient fuel performance and safety for future integral testing and fuel licensing.

Paul Ohodnicki’s and Kevin Chen’s project, titled “Fusion of Distributed Fiber Optics, Acoustic NDE, and Physics-Based AI for Spent Fuel Monitoring,” will leverage the fusion between fiber optic distributed acoustic sensing and advanced acoustic nondestructive evaluation techniques with artificial intelligence enhanced classification frameworks to quantitatively characterize the internal state of dry cask storage systems without introducing additional risks of failure.
 

 Venkat Viswanathan and his colleagues recently published a paper in Nature describing their research in the anionic reduction-oxidation mechanism of lithium-rich cathodes. Normal Li-ion batteries work because of cationic redox, where a metal ion changes its oxidation state as lithium is added or removed. However, only one lithium ion can be stored per metal ion. Lithium-rich cathodes on the other hand can store more, and researchers attribute this to the anionic redox mechanism. 

The team set out to find conclusive evidence of this by using Compton scattering, a phenomenon where a photon deviates from a trajectory after interacting with a particle such as an electron. They observed how electron beams’ orbits in the anionic redox activity can be imaged and visualized and its character and symmetry determined. 

Previous research has not been able to provide a clear image of the quantum mechanical electronic orbitals related to redox reactions because standard experiments could not measure it. However, when the team saw the agreement in redox character between theory and experimental results, they realized that they could image the oxygen states that are responsible for the redox mechanism. 

The gathered evidence supports the anionic redox mechanism in a lithium-rich battery material. Furthermore, the study provides a clear image of a lithium-rich battery at the atomic level and suggests pathways for improving and designing next generation cathodes for electric aviation.