The development of bionic sensing devices with advanced physiological functionalities has attracted significant attention in flexible electronics. In this study, we innovatively develop an air-stable photo-induced n-type dopant and a sophisticated photo-induced patterning technology to construct high-resolution joint-free p–n integrated thermoelectric devices. The exceptional stability of the photo-induced n-type dopant, combined with our meticulously engineered joint-free device architecture, results in extremely low temporal and spatial variations. These minimized variations, coupled with superior linearity, position our devices as viable candidates for artificial thermoreceptors capable of sensing external thermal noxious stimuli. By integrating them into a robotic arm with a pain perception system, we demonstrate accurate pain responses to external thermal stimuli. The system accurately discerns pain levels and initiates appropriate protective actions across varying intensities. Our findings present a novel strategy for constructing high-resolution thermoelectric sensing devices toward precise biomimetic thermoreceptors.

Due to their high mechanical compliance and excellent biocompatibility, conductive hydrogels exhibit significant potential for applications in flexible electronics. However, as the demand for high sensitivity, superior mechanical properties, and strong adhesion performance continues to grow, many conventional fabrication methods remain complex and costly. Herein, we propose a simple and efficient strategy to construct an entangled network hydrogel through a liquid–metal-induced cross-linking reaction, hydrogel demonstrates outstanding properties, including exceptional stretchability (1643%), high tensile strength (366.54 kPa), toughness (350.2 kJ m⁻³), and relatively low mechanical hysteresis. The hydrogel exhibits long-term stable reusable adhesion (104 kPa), enabling conformal and stable adhesion to human skin. This capability allows it to effectively capture high-quality epidermal electrophysiological signals with high signal-to-noise ratio (25.2 dB) and low impedance (310 ohms). Furthermore, by integrating advanced machine learning algorithms, achieving an attention classification accuracy of 91.38%, which will significantly impact fields like education, healthcare, and artificial intelligence.

A Texas A&M AgriLife Research study shines fresh light — literally — on forensic death investigations.

Researchers from the Texas A&M College of Agriculture and Life Sciences Department of Entomology and Department of Biochemistry and Biophysics have developed a technique that uses infrared light and machine learning to reveal the sex of blow fly larvae found on human remains. This innovative approach may help investigators estimate time of death with greater speed and accuracy.

The study, published in the Journal of Forensic Sciences, was led by Aidan Holman, a doctoral student in the lab of Dmitry Kurouski, Ph.D., associate professor in the Department of Biochemistry and Biophysics, who supervised the research.