Temperature-Responsive Microrobot for High-Temperature Sensing in Constrained Environments - Shaobo Ding; Junmin Liu; Jiaxu Dong; Rencheng Zhuang; Enbo Shi; Shutong Wang; Yuhang Xiao; Dekai Zhou; Longqiu Li; Xiaocong Chang (Research)
Temperature measurement in confined environments has long been a substantial challenge. Due to poor accessibility to constrained space, and low visibility, conventional thermometry and existing nanoscale thermometers struggle to achieve efficient and accurate temperature detection. As an emerging technology, microrobots offer great potential for temperature sensing in such challenging conditions. Here, we propose a temperature-responsive microrobot (TRM) that integrates artificial neural networks into microscale thermal sensing, enabling quantitative temperature measurement in complex and constrained environments. The TRM undergoes irreversible color changes in a high-temperature range of 160 to 240 °C. It features a Janus structure composed of a Cu(NH3)4SO4-based thermochromic material and a nickel-coated magnetic actuation layer, allowing reliable operation in nontransparent and geometrically confined environments such as porous geological structures and constrained microspaces. The thermochromic mechanism and motion dynamics of the TRM under elevated temperatures were systematically investigated. The microrobot exhibits distinct chromatic responses at different temperatures. Based on the correlation between chromaticity and temperature, a multilayer perceptron neural network was developed. By inputting the observed color features into the trained model, the surrounding temperature can be quantitatively determined. Experimental results in a simulated porous microchannel model confirmed the feasibility and effectiveness of the TRM for localized high-temperature detection. This work provides a new solution for temperature sensing in restricted environments and lays a solid foundation for the application of microrobots in industrial high-temperature monitoring, highlighting their potential for real-world deployment in complex conditions.