Design and Characteristics of a Thin-Film ThermoMesh for the Efficient Embedded Sensing of a Spatio-Temporally Sparse Heat Source
Abstract
This work presents ThermoMesh, a passive thin-film thermoelectric mesh sensor designed to detect and characterize spatio-temporally sparse heat sources through conduction-based thermal imaging. The device integrates thermoelectric junctions with linear or nonlinear interlayer resistive elements to perform simultaneous sensing and in-sensor compression. We focus on the single-event (1-sparse) operation and define four performance metrics: range, efficiency, sensitivity, and accuracy. Numerical mo...
Description / Details
This work presents ThermoMesh, a passive thin-film thermoelectric mesh sensor designed to detect and characterize spatio-temporally sparse heat sources through conduction-based thermal imaging. The device integrates thermoelectric junctions with linear or nonlinear interlayer resistive elements to perform simultaneous sensing and in-sensor compression. We focus on the single-event (1-sparse) operation and define four performance metrics: range, efficiency, sensitivity, and accuracy. Numerical modeling shows that a linear resistive interlayer flattens the sensitivity distribution and improves minimum sensitivity by approximately tenfold for a mesh. Nonlinear temperature-dependent interlayers further enhance minimum sensitivity at scale: a ceramic negative-temperature-coefficient (NTC) layer over 973--1273K yields a higher minimum sensitivity than the linear design at a mesh, while a VO interlayer modeled across its metal--insulator transition (MIT) over 298--373K yields a improvement. Using synthetic 1-sparse datasets with white boundary-channel noise at a signal-to-noise ratio of 40~dB, the VO case achieved localization accuracy, a mean absolute temperature error of ~K, and a noise-equivalent temperature (NET) of ~K. For the ceramic-NTC case no localization errors were observed under the tested conditions, with a mean absolute temperature error of ~K and a NET of ~K. These results indicate that ThermoMesh could enable energy-efficient embedded thermal sensing in scenarios where conventional infrared imaging is limited, such as molten-droplet detection or hot-spot monitoring in harsh environments.
Source: arXiv:2604.28148v1 - http://arxiv.org/abs/2604.28148v1 PDF: https://arxiv.org/pdf/2604.28148v1 Original Link: http://arxiv.org/abs/2604.28148v1
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May 1, 2026
Biomedical Engineering
Engineering
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