Evidence map›Paper›PMID 40691130›Full record

ArticleMicrosystems & nanoengineering2025

Flexible calorimetric differential pressure sensor array integrated with a superhydrophobic surface for flight parameter estimation.

Xin Ke, Yu Gao, Zheng Gong, Yunfan Li, Zihao Dong, Bowei Wan, Yurun Guo, Yonggang Jiang

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 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.

Xin KeInstitute of Bionic and Micro-nano Systems, School of Mechanical Engineering and Automation, Beihang University, 100191, Beijing, China.
Yu GaoInstitute of Bionic and Micro-nano Systems, School of Mechanical Engineering and Automation, Beihang University, 100191, Beijing, China.
Zheng GongInstitute of Bionic and Micro-nano Systems, School of Mechanical Engineering and Automation, Beihang University, 100191, Beijing, China.
Yunfan LiInstitute of Bionic and Micro-nano Systems, School of Mechanical Engineering and Automation, Beihang University, 100191, Beijing, China.
Zihao DongInstitute of Bionic and Micro-nano Systems, School of Mechanical Engineering and Automation, Beihang University, 100191, Beijing, China.ORCID http://orcid.org/0000-0003-3236-7192
Bowei WanInstitute of Bionic and Micro-nano Systems, School of Mechanical Engineering and Automation, Beihang University, 100191, Beijing, China.
Yurun GuoInstitute of Bionic and Micro-nano Systems, School of Mechanical Engineering and Automation, Beihang University, 100191, Beijing, China.
Yonggang JiangInstitute of Bionic and Micro-nano Systems, School of Mechanical Engineering and Automation, Beihang University, 100191, Beijing, China. jiangyg@buaa.edu.cn.ORCID http://orcid.org/0000-0003-1979-996X

Funding

National Natural Science Foundation of China (National Science Foundation of China) T2121003National Natural Science Foundation of China (National Science Foundation of China) U23A20638
6 · The paper itself

Abstract

Distributed pressure sensor array is a promising approach for the estimation of flight parameters for small unmanned aerial vehicles. Current flexible pressure sensor arrays are conventionally subjected to limited sensor resolution, poor bending flexibility, and inadequate packaging protection, resulting in insufficient precision for flight parameter estimation. Here we present a high-resolution differential pressure sensor array using a calorimetric measurement method and a multilayer polyimide bonding technique. The proposed differential pressure sensor array reaches a detection limit of 36.5 mPa over a range of 500 Pa and shows high repeatability when attached to varying curved surfaces. In addition, a superhydrophobic packaging is integrated into the sensor fabrication process, endowing it with waterproof capability. Utilizing a multilayer perceptron neural network, we demonstrated the function of the sensor array in estimating airspeeds and angle of attacks, achieving average solving errors of 0.15 m/s and 0.37°, respectively.

Identifiers

PMID40691130
PMCPMC12280219

What OpenQuestion holds

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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.