Evidence map›Paper›PMID 42512997›Full record

ArticleMicromachines2026

Self-Powered Z-Shaped Hybrid Triboelectric-Electromagnetic Vibration Sensor for Coal Mine Fracturing Condition Monitoring.

Yanping Miao, Da Liu, Zexu Zuo, Yanjun Feng, Chuan Wu

Abstract read
In one paragraph

Article in Micromachines, 2026. 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

5 authors.

Yanping MiaoShaanxi Coal Caojiatan Mining Co., Ltd., Yulin 719100, China.
Da LiuFaculty of Mechanical and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China.
Zexu ZuoFaculty of Mechanical and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China.
Yanjun FengTiandi Science and Technology Co., Ltd., Beijing 100013, China.
Chuan WuFaculty of Mechanical and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China.ORCID 0000-0002-7275-0295

Funding

National Key Research and Development Program of China 2023YFC2907500National Key Research and Development Program of China 2023YFC2907502
6 · The paper itself

Abstract

During coal mine fracturing operations, real-time monitoring of the vibration frequency of the drilling assembly is crucial for assessing crack development, optimizing fracturing parameters, and ensuring the safety of downhole equipment. However, traditional active vibration sensors are limited by their reliance on external power supplies in the complex environment of underground mining, reducing their operational efficiency and effectiveness. Accordingly, a self-powered Z-shaped vibration sensor based on hybrid triboelectric and electromagnetic mechanisms was developed for monitoring coal mine fracturing drilling. This sensor utilizes the vibrations of the drilling tool to induce frictional electric pulse signals that correspond to the vibration frequency, enabling simultaneous vibration monitoring and energy generation. Experimental results demonstrate the stable performance of the proposed sensor under thermal conditions up to 150 °C and moisture levels reaching 90% relative humidity. The proposed sensor exhibits an operating frequency range of 0 to 11 Hz, with the measurement deviation constrained within a 5% threshold. Under optimal impedance matching, the triboelectric and electromagnetic units deliver peak power outputs of 0.04 mW and 110.5 mW when connected to external loads of 10

Indexed as

coal mine fracturingself-poweredtriboelectric nanogeneratorvibration sensor

Identifiers

PMID42512997
PMCPMC13414057

What OpenQuestion holds

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LicenceCC BY
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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.