Performance Enhancement of Piezoelectric Nanomaterials for Intelligent Sensing and Their Electronic Signal Processing Solutions

Abstract

Piezoelectric nanomaterials, such as ZnO and PZT nanowires, are widely used in flexible electronics, health monitoring, and IoT sensors due to their ability to convert mechanical energy into electrical signals. However, challenges remain in optimizing their piezoelectric performance and improving signal processing for real-world applications. While significant progress has been made in material enhancement and signal processing, there is a lack of comprehensive integration between these two approaches, limiting the performance of piezoelectric sensors in dynamic environments. This study investigates the impact of material enhancements (doping and structural optimization) on the piezoelectric properties of ZnO and PZT nanowires and explores various signal processing techniques (filtering, amplification, noise reduction) to improve sensor performance. The effectiveness of these methods is validated through experimental testing and performance evaluation. The results show that Li-doped ZnO and optimized PZT nanowires significantly improve piezoelectric performance. Signal processing techniques, particularly low-pass filtering and adaptive amplification, increase the signal-to-noise ratio (SNR) by 25%, with overall sensor sensitivity improving by 30%. The findings contribute to the development of more efficient piezoelectric sensors by integrating material enhancement and signal processing, offering significant improvements in sensor performance for applications in flexible electronics, health monitoring, and IoT systems.