Scientists have created a fingertip‑scale spectrometer‑on‑a‑chip that brings lab‑grade hyperspectral sensing into the near‑infrared range long considered out of reach for silicon. By engineering photon‑trapping textures onto silicon photodiodes and using a neural network to decode their combined signals, the device accurately reconstructs spectra from 640 to 1100 nanometers—well beyond the limits of conventional silicon spectrometers. Despite its tiny 0.4‑mm footprint, the chip delivers ~8‑nm resolution, maintains high accuracy with fewer than 16 detectors, and remains stable even under heavy electronic noise. The team also demonstrated precise hyperspectral imaging of a butterfly dataset, highlighting the technology’s potential for compact biomedical, environmental, and remote‑sensing tools.

A UCLA-led, multi-institution research team has discovered a metallic material with the highest thermal conductivity measured among metals, challenging long-standing assumptions about the limits of heat transport in metallic materials. Thte team reported that metallic theta-phase tantalum nitride conducts heat nearly three times more efficiently than copper or silver, the best conventional heat-conducting metals. The study was published in Science.

SPIE, the international society for optics and photonics, is celebrating the inaugural Biophotonics Discovery's Impact of the Year Award as well as its first recipient, Stanford University Associate Professor of materials science and engineering, Guosong Hong. Hong was officially honored at the Biophotonics Focus: Light-Based Technologies for Reproductive, Maternal, and Neonatal Health plenary during SPIE Photonics West on Sunday evening.

A paper published in Nature Communications shows how AI at Sandia National Laboratories is advancing beyond a mere automation tool toward becoming a powerful engine for clear, comprehensible scientific discovery.

A joint research team from NIMS and University of Tsukuba developed "autonomous AI network" technology by which multiple autonomous AI systems can efficiently discover new materials by spontaneously collaborating with each other and forming a network. The team demonstrated the effectiveness of the technology through simulations. This research result was published in npj Computational Materials on December 9, 2025.

One of the primary challenges with prosthetic hands is the ability to properly tune the appropriate grip based on the object being handled. In Nanotechnology and Precision Engineering, researchers in China have developed an object identification system for prosthetic hands to guide appropriate grip strength decisions in real time. Their system uses an electromyography sensor at the user’s forearm to determine what the user intends to do with the object at hand.