Underwater <i>in-situ</i> low-frequency vibration sensor based on oriented electrospinning

With the increasing importance of low-frequency signals in underwater monitoring, earthquake early warning, environmental noise analysis, and biomedical imaging, traditional sensor technologies face challenges such as limited flexibility, slow response time, and poor adaptability. Although existing sensors, such as electromagnetic, piezoelectric, and capacitive sensors, have made progress in certain areas, their applications are often restricted by complex environments. This paper innovatively proposes an<i>in-situ</i>vibration monitoring method, designing a low-frequency<i>in-situ</i>detection system based on triboelectric nanogenerator technology. The system not only enables efficient low-frequency signal detection in complex underwater environments but also, by incorporating machine learning algorithms, identifies different signal sources, achieving accurate distinction of intrinsic signals. The application of this technology realizes the concept of<i>in-situ</i>detection, breaking through the limitations of traditional sensor systems and providing a new solution for real-time monitoring of low-frequency signals.

Authors

Institutions

Publication Details

Journal
Nanotechnology
Published
2025-06-19
DOI
https://doi.org/10.1088/1361-6528/ade5fb
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.00

Funders

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

Underwater in-situ low-frequency vibration sensor based on oriented electrospinning

Wei Zhai, Yang Deng, Qizheng Li, Huaisong Zhao et al.
Nanotechnology
Advanced Sensor and Energy Harvesting Materials
article

Underwater in-situ low-frequency vibration sensor based on oriented electrospinning

Wei Zhai, Yang Deng, Qizheng Li, Huaisong Zhao, Chongyang Fu, Yanqiang Li, Xiaoxiong Wang
article en

Abstract

With the increasing importance of low-frequency signals in underwater monitoring, earthquake early warning, environmental noise analysis, and biomedical imaging, traditional sensor technologies face challenges such as limited flexibility, slow response time, and poor adaptability. Although existing sensors, such as electromagnetic, piezoelectric, and capacitive sensors, have made progress in certain areas, their applications are often restricted by complex environments. This paper innovatively proposes an<i>in-situ</i>vibration monitoring method, designing a low-frequency<i>in-situ</i>detection system based on triboelectric nanogenerator technology. The system not only enables efficient low-frequency signal detection in complex underwater environments but also, by incorporating machine learning algorithms, identifies different signal sources, achieving accurate distinction of intrinsic signals. The application of this technology realizes the concept of<i>in-situ</i>detection, breaking through the limitations of traditional sensor systems and providing a new solution for real-time monitoring of low-frequency signals.

NanotechnologyVol. 36(30)
Qingdao University (CN), Weihai Municipal Hospital (CN), Weihai Chest Hospital (CN), Weihai Science and Technology Bureau (CN)
National Natural Science Foundation of China, State Key Laboratory of Bio-Fibers and Eco-Textiles
Life below water
Openalex Percentile: Top 87%
Advanced Sensor and Energy Harvesting Materials
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.

Underwater <i>in-situ</i> low-frequency vibration sensor based on oriented electrospinning — Wei Zhai, Yang Deng, et al. · Nanotechnology (2025) | TGRS Research Map | TGRS