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 V2O5 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.
Keywords: V2O5; data interpolation; flexible sensors; machine learning; nanowires.