Ultra-High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception

Monitoring minuscule mechanical signals, both in magnitude and direction, is imperative in many application scenarios, e.g., structural health monitoring and robotic sensing systems. However, the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix, and achieving sensitivity for detecting micrometer-scale deformations is also challenging. Herein, we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement, capable of detecting minute anisotropic deformations. The as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%, thereby enabling its utility in accurately discerning the 5 μm-height wrinkles in thin films and in monitoring human pulse waves. The ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate phase. Additionally, when integrated with machine learning techniques, the sensor's capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100% accuracy. The structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli, offering a novel perspective for enhancing recognition accuracy.

Authors

Institutions

Publication Details

Journal
Nano-Micro Letters
Published
2024-10-16
DOI
https://doi.org/10.1007/s40820-024-01539-6
Citations
50
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
13.04

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Ultra-High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception

Enfu Liang, Hao Yin, Yiping Guo, Qichao Li et al.
50 citations
Nano-Micro Letters
Structural Health Monitoring Techniques
13.04
article

Ultra-High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception

Enfu Liang, Hao Yin, Yiping Guo, Qichao Li, Zhiying Tian, Yanting Li, Chenhui Jiang
article en
50 citations

Abstract

Monitoring minuscule mechanical signals, both in magnitude and direction, is imperative in many application scenarios, e.g., structural health monitoring and robotic sensing systems. However, the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix, and achieving sensitivity for detecting micrometer-scale deformations is also challenging. Herein, we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement, capable of detecting minute anisotropic deformations. The as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%, thereby enabling its utility in accurately discerning the 5 μm-height wrinkles in thin films and in monitoring human pulse waves. The ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate phase. Additionally, when integrated with machine learning techniques, the sensor's capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100% accuracy. The structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli, offering a novel perspective for enhancing recognition accuracy.

Nano-Micro LettersVol. 17(1)
Shanghai Jiao Tong University (CN), Beijing Vaccum Electronic Research Institute (CN)
Shanghai Jiao Tong University, National Key Research and Development Program of China
Openalex Percentile: Top 1%
Structural Health Monitoring Techniques
13.04
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.