In International Journal of Extreme Manufacturing, researchers report a neuromorphic chip that processes and learns information using light and electronics tightly integrated on a single platform, much like biological neurons combine sensing, communication, and memory in one physical structure.

The complex fluid–structure interaction underlying blood flow through vessels has proved challenging to analyze both numerically and experimentally. Addressing this gap, researchers developed a new experimental platform that uses polarized light to directly visualize stress fields in artificial blood vessels and in the flowing blood analogue in real time. Their findings reveal how stress is distributed during pulsating flow and could help design safer devices and improve the diagnosis and treatment of cardiovascular diseases.

A way to electrically modify the chirality of organic–inorganic hybrid materials, in which chiral molecules adsorb onto inorganic surfaces, has been demonstrated by researchers at Science Tokyo. By using an electric double-layer transistor with a chiral electrolyte, specific chirality was imposed on an otherwise achiral molybdenum disulfide surface. This reversible method enables tunable chiral electronic states and opens new possibilities for advanced spintronic devices and the emerging field of “chiral iontronics.”

There is an increasing demand for novel materials with high-temperature oxidation resistance in harsh environments. Now, a joint research team from Jeonbuk National University and Korea Institute of Materials Science have demonstrated promising alumina-forming ferritic alloys that exhibit high-temperature oxidation resistance even under prolonged steam exposure. They achieve an outstanding balance between steam oxidation resistance, high-temperature strength, and cost- effectiveness, making them lucrative for high-temperature structural applications in extreme environments.

A research team from Peking University has successfully developed a vanadium oxide (VO₂)-based “locally active memristive oscillator” that operates at the edge of chaos. Through simple signal injection, the device exhibits diverse nonlinear dynamic behaviors such as frequency division, stochastic oscillation, and frequency locking. Remarkably, a single device demonstrates powerful frequency-domain feature extraction capability in speech recognition tasks, achieving performance comparable to a two-layer convolutional neural network. This breakthrough opens a new pathway for future energy-efficient and intelligent neuromorphic computing chips.