Evidence map›Paper›PMID 41894052›Full record

ArticleNano-micro letters2026

Graphene Aerogel-Based Flexible Pressure Sensor for Physiological Signal Detection and Human-Machine Interaction.

Zihan Wang, Zeshang Zhao, Qiyang Tu, Chengpeng Yao, Zhao Liu, Chengzhi Zhou, Luxiang Xu, Shijie Guo, Chuizhou Meng, Gaofeng Shao and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nano-micro letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Zihan WangState Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, People's Republic of China.
Zeshang ZhaoState Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, People's Republic of China.
Qiyang TuState Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, People's Republic of China.
Chengpeng YaoState Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, 300130, People's Republic of China.
Zhao LiuState Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, 300130, People's Republic of China.
Chengzhi ZhouState Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, People's Republic of China.
Luxiang XuState Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, People's Republic of China.
Shijie GuoState Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, People's Republic of China.
Chuizhou MengResearch Institute of Wearable Electronic Materials and Devices, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, People's Republic of China. 2018108@hebut.edu.cn.
Gaofeng ShaoSchool of Chemistry and Materials Science, Nanjing University of Information Science and Technology, Nanjing, 210044, People's Republic of China. gfshao@nuist.edu.cn.
Huanyu ChengDepartment of Engineering Science and Mechanics, The Pennsylvania State University, University Park, 16802, USA. huanyu.cheng@psu.edu.
Li YangState Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, 300130, People's Republic of China. yangli5781@126.com.

Funding

Smart skin grafts for quantitative assessment and treatment of diabetic woundsR21EB030140 · NIBIB · PENNSYLVANIA STATE UNIVERSITY, THE · PI CHENG, HUANYU · 2021 to 2021
$620k
NIBIB NIH HHS R21 EB030140
6 · The paper itself

Abstract

Despite extensive development of flexible pressure sensors, it is still difficult for them to simultaneously achieve high precision and a large response to subtle pressures. To address these challenges, this work demonstrates a flexible pressure sensing platform that features the reduced graphene oxide aerogel sandwiched between a polydimethylsiloxane encapsulation layer and a thin polyimide film with interdigital electrodes. The resulting pressure sensor exhibits a high sensitivity of 698.96 kPa

Indexed as

Force feedbackGraphene aerogelIntelligent object recognitionPiezoresistive pressure sensorTeleoperation

Identifiers

PMID41894052
PMCPMC13031469

What OpenQuestion holds

Textmetadata
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.