Review of Self-Powered Wireless Sensors by Triboelectric Breakdown Discharge.

Shuzhe Liu, Jixin Yi, Guyu Jiang, Jiaxun Hou, Yin Yang, Guangli Li, Xuhui Sun, Zhen Wen

Abstract readReview

In one paragraph

Review in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Shuzhe LiuJiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China.

Jixin YiJiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China.

Guyu JiangJiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China.

Jiaxun HouJiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China.

Yin YangJiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China.

Guangli LiJiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China.

Xuhui SunJiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China.

Zhen WenJiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China.ORCID 0000-0001-9780-6876

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review systematically examines recent advances in self-powered wireless sensing technologies based on triboelectric nanogenerators (TENGs), focusing on innovative methods that leverage breakdown discharge effects to achieve high-precision and long-distance signal transmission. These methods offer novel technical pathways and theoretical frameworks for next-generation wireless sensing systems. To address the core limitations of conventional wireless sensors, such as a restricted transmission range, high power consumption, and suboptimal integration, this analysis elucidates the mechanism of the generation of high-frequency electromagnetic waves through localized electric field ionization induced by breakdown discharge. Key research directions are synthesized to enhance TENG-based sensing capabilities, including novel device architectures, the optimization of RLC circuit models, the integration of machine learning algorithms, and power management strategies. While current breakdown discharge sensors face challenges such as energy dissipation, multimodal coupling complexity, and signal interpretation barriers, future breakthroughs in material engineering and structural design are anticipated to drive advancements in efficiency, miniaturization, and intelligent functionality in this field.

Indexed as

breakdown dischargeRLC circuit modelsignal analysistriboelectric nanogenerator (TENG)wireless sensing

Identifiers

PMID40731674

DOI10.3390/mi16070765

PMCPMC12298944

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.