Evidence map›Paper›PMID 42627883›Full record

ArticleScience advances2026

A triboelectric airflow field sensor enables cross-scale multi-parameter sensing in meter-scale flapping-wing aircrafts.

Wenyu Zhao, Jiaze Li, Waner Lin, Yingtian Xu, Yunlong Hu, Jiahao Fan, Haoyu Wang, Yu Chen, Erzhen Pan, Zhenglong Sun and 1 more

Abstract read
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In one paragraph

Article in Science advances, 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

11 authors.

Wenyu ZhaoShenzhen Key Laboratory of Photonics and Biophotonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0003-2128-5976
Jiaze LiShenzhen Key Laboratory of Photonics and Biophotonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0009-0007-4245-9820
Waner LinShenzhen Key Laboratory of Photonics and Biophotonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Yingtian XuShenzhen Key Laboratory of Photonics and Biophotonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0009-0006-8068-8758
Yunlong HuShenzhen Key Laboratory of Photonics and Biophotonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Jiahao FanSchool of Robotics and Advanced Manufacture, Harbin Institute of Technology, Shenzhen 518055, China.
Haoyu WangSchool of Robotics and Advanced Manufacture, Harbin Institute of Technology, Shenzhen 518055, China.
Yu ChenShenzhen Key Laboratory of Photonics and Biophotonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0002-6693-9059
Erzhen PanSchool of Robotics and Advanced Manufacture, Harbin Institute of Technology, Shenzhen 518055, China.ORCID 0000-0002-3449-6361
Zhenglong SunSchool of Science and Engineering & School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China.ORCID 0000-0002-8135-1659
Ziya WangShenzhen Key Laboratory of Photonics and Biophotonics, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0002-9582-8139

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate in-situ sensing of unsteady aerodynamic parameters is essential yet challenging for closed-loop control in meter-scale flapping-wing aircraft. This task requires simultaneous monitoring of three coupled parameters: local wind speed (LWS), angle of attack (AoA), and flapping frequency (FF). Here, we report a conformal triboelectric airflow field sensor that converts airflow-induced aeroelastic vibrations into electrical signals. Its low-damping apertured cantilever design provides a broad sensing range and rich vibration features, enabling accurate aerodynamic parameter decoding with a deep-learning model. To address cross-scale discrepancies, we use transfer learning to adapt representations learned from low-cost small-scale wind tunnel data to data-limited full-scale conditions, achieving mean absolute errors of 0.04 m·s

Identifiers

PMID42627883
PMCPMC13496186

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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.