AI Article Synopsis

  • * This study presents a novel VO nanowire network temperature sensor fabricated on a flexible film, achieving long-term stability (>110 hours) and high sensitivity of approximately -1.5%/°C.
  • * The use of machine learning for data interpolation among sensors enhances measurement accuracy and allows for tactile mapping without increasing the number of sensors needed.

Article Abstract

High-performance flexible temperature sensors are crucial in various technological applications, such as monitoring environmental conditions and human healthcare. The ideal characteristics of these sensors for stable temperature monitoring include scalability, mechanical flexibility, and high sensitivity. Moreover, simplicity and low power consumption will be essential for temperature sensor arrays in future integrated systems. This study introduces a solution-based approach for creating a VO nanowire network temperature sensor on a flexible film. Through optimization of the fabrication conditions, the sensor exhibits remarkable performance, sustaining long-term stability (>110 h) with minimal hysteresis and excellent sensitivity (∼-1.5%/°C). In addition, this study employs machine learning techniques for data interpolation among sensors, thereby enhancing the spatial resolution of temperature measurements and adding tactile mapping without increasing the sensor count. Introducing this methodology results in an improved understanding of temperature variations, advancing the capabilities of flexible-sensor arrays for various applications.

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Source
http://dx.doi.org/10.1021/acsami.4c01333DOI Listing

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