Evidence map›Paper›PMID 39064363›Full record

ArticleMicromachines2024

Portable Multi-Layer Capsule-Shaped Triboelectric Generator for Human Motion Energy Harvesting.

Xinglin Yang, Da Huo, Jianye Su, Zhouyu He

Abstract read
In one paragraph

Article in Micromachines, 2024. 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

4 authors.

Xinglin YangSchool of Energy and Power, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Da HuoSchool of Energy and Power, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Jianye SuSchool of Energy and Power, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Zhouyu HeSchool of Energy and Power, Jiangsu University of Science and Technology, Zhenjiang 212003, China.

Funding

Postgraduate Research & Practice Innovation Program of Jiangsu Province (China) KYCX24_4090
6 · The paper itself

Abstract

This paper introduces a novel portable multi-layer capsule-shaped triboelectric generator (CP-TEG), aimed at optimizing the performance of triboelectric generator technology in terms of miniaturization, modularity, and efficient energy collection. The CP-TEG utilizes a unique multi-layer, stacked structure and an elliptical cylindrical design to increase the effective frictional area and enhance power generation efficiency. Its portable design allows for flexible application in various environments and scenarios. Experimental results demonstrate that the CP-TEG can maintain stable and efficient electrical output under various motion amplitudes and frequencies, and it shows good adaptability to the direction of motion excitation. With a motion amplitude of 7 cm and a frequency of 1.94 Hz, the CP-TEG can charge a 220 μF capacitor to 1.3 V within 100 s. The power generation unit's output voltage and current are more than three times higher than that of traditional single-layer contact-separation mode triboelectric devices. Particularly, its performance in harvesting energy from human motion underscores its effectiveness as a renewable energy solution for wearable devices. Through its innovative structural design and optimized working mechanism, the CP-TEG demonstrates excellent energy collection efficiency and application potential, offering new options for sustainable energy solutions and development.

Indexed as

human motion energymodularmulti-layer capsule structureportabletriboelectric generator (TEG)

Identifiers

PMID39064363
PMCPMC11278884

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.