Much of the appeal of self-driving vehicles is just that: the fact that they drive themselves. Still, there are times when a driver needs to put their hands on the wheel, and a new study from Binghamton University suggests that task could be more difficult if you have a history of head injuries.

The explosion of artificial intelligence (AI) and the era of Big Data have exposed a critical flaw in traditional computer hardware: the slow and energy-consuming data transfer between memory and processors, commonly known as the von Neumann bottleneck. Every day, when our devices process AI queries, they must move massive amounts of data back and forth, causing severe delays and battery drain.

To address this challenge, researchers at The University of Hong Kong (HKU) have created a chip with a minimalist architecture that performs complex searches directly where the data is stored, fundamentally bypassing this bottleneck. The research team, led by Professor Can Li from the Department of Electrical and Computer Engineering of the Faculty of Engineering and the Centre for Advanced Semiconductors and Integrated Circuits (CASIC), has developed a novel analogue content-addressable memory (CAM) utilising two-dimensional (2D) MoS2 flash memories.

Lithium–oxygen batteries have great potential as next-generation energy storage devices; however, sluggish oxygen reactions have long hindered their commercial adoption. Researchers from Shibaura Institute of Technology developed a heterostructured lanthanum cobalt oxide-cobalt oxide composite that accelerates these reactions, achieving outstanding bifunctional catalytic performance with a record-low potential gap of just 1.14 V. The platinum-free catalyst could allow longer-range electric vehicles and extended-endurance drones, offering a cost-effective and sustainable path toward next-generation energy storage.