Evidence map›Paper›PMID 37861802›Full record

ArticleNano-micro letters2023

A Broad Range Triboelectric Stiffness Sensor for Variable Inclusions Recognition.

Ziyi Zhao, Zhentan Quan, Huaze Tang, Qinghao Xu, Hongfa Zhao, Zihan Wang, Ziwu Song, Shoujie Li, Ishara Dharmasena, Changsheng Wu and 1 more

Erratum issuedOpen access · diamondAbstract read
In one paragraph

Article in Nano-micro letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.4field-weighted citation impact, top 7% of its field
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

5 citing papers in PubMed, 31 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 4 countries.

Ziyi Zhao *Tsinghua-Berkeley Shenzhen Institute, Institute of Data and Information, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
Zhentan Quan *Institute of Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
Huaze Tang *Tsinghua-Berkeley Shenzhen Institute, Institute of Data and Information, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
Qinghao XuTsinghua-Berkeley Shenzhen Institute, Institute of Data and Information, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
Hongfa ZhaoTsinghua-Berkeley Shenzhen Institute, Institute of Data and Information, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
Zihan WangTsinghua-Berkeley Shenzhen Institute, Institute of Data and Information, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
Ziwu SongTsinghua-Berkeley Shenzhen Institute, Institute of Data and Information, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
Shoujie LiTsinghua-Berkeley Shenzhen Institute, Institute of Data and Information, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
Ishara DharmasenaWolfson School of Mechanical Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, UK.
Changsheng WuDepartment of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore.
Wenbo DingTsinghua-Berkeley Shenzhen Institute, Institute of Data and Information, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China. ding.wenbo@sz.tsinghua.edu.cn.
Tsinghua–Berkeley Shenzhen Institute · CNTsinghua University · CNLoughborough University · GBNational University of Singapore · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the development of artificial intelligence, stiffness sensors are extensively utilized in various fields, and their integration with robots for automated palpation has gained significant attention. This study presents a broad range self-powered stiffness sensor based on the triboelectric nanogenerator (Stiff-TENG) for variable inclusions in soft objects detection. The Stiff-TENG employs a stacked structure comprising an indium tin oxide film, an elastic sponge, a fluorinated ethylene propylene film with a conductive ink electrode, and two acrylic pieces with a shielding layer. Through the decoupling method, the Stiff-TENG achieves stiffness detection of objects within 1.0 s. The output performance and characteristics of the TENG for different stiffness objects under 4 mm displacement are analyzed. The Stiff-TENG is successfully used to detect the heterogeneous stiffness structures, enabling effective recognition of variable inclusions in soft object, reaching a recognition accuracy of 99.7%. Furthermore, its adaptability makes it well-suited for the detection of pathological conditions within the human body, as pathological tissues often exhibit changes in the stiffness of internal organs. This research highlights the innovative applications of TENG and thereby showcases its immense potential in healthcare applications such as palpation which assesses pathological conditions based on organ stiffness.

Indexed as

Decoupling methodHealthcare applicationsHeterogeneous stiffnessStiffness sensorVariable inclusions

Identifiers

PMID37861802
PMCPMC10589179
OpenAlexW4387798350

What OpenQuestion holds

Textmetadata
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.