Opening the gate to the next generation of information processing

Scientists have devised a means of achieving improved information processing with a new technology for effective gate operation. This technology has applications in classical electronics as well as quantum computing, communications and sensing.

Editors of MIT Technology Review name Argonne’s Jie Xu as a 2021 Innovator Under 35

The editors of MIT Technology Review have chosen Argonne’s Jie Xu as an Innovator Under 35 for 2021. She is one of only 35 innovators under the age of 35 named to this list. She is being recognized for her research on printable skin-like electronics.

Pivotal discovery in quantum and classical information processing

Researchers have achieved, for the first time, electronically adjustable interactions between microwaves and a phenomenon in certain magnetic materials called spin waves. This could have application in quantum and classical information processing.

Argonne and Sentient Science develop game-changing computer modeling program to improve discovery and design of new materials

Researchers collaborated to create a software program to accelerate discovery and design of new materials for applications allowing for a far more comprehensive understanding of materials from atomistic to mesoscopic scale than ever before.

Battery Breakthrough Gives Boost to Electric Flight and Long-Range Electric Cars

Researchers at Berkeley Lab, in collaboration with Carnegie Mellon University, have developed a new battery material that could enable long-range electric vehicles that can drive for hundreds of miles on a single charge, and electric planes called eVTOLs for fast, environmentally friendly commutes.

Six Argonne researchers receive DOE Early Career Research Program awards

Argonne scientists Michael Bishof, Maria Chan, Marco Govini, Alessandro Lovato, Bogdan Nicolae and Stefan Wild have received funding for their research as part of DOE’s Early Career Research Program.

Capturing 3D microstructures in real time

Argonne researchers have invented a machine-learning based algorithm for quantitatively characterizing material microstructure in three dimensions and in real time. This algorithm applies to most structural materials of interest to industry.