Evidence map›Paper›PMID 38725435›Full record

ArticleMicrosystems & nanoengineering2024

Bioinspired integrated triboelectric electronic tongue.

Jiaming Liu, Jingui Qian, Murtazt Adil, Yali Bi, Haoyi Wu, Xuefeng Hu, Zuankai Wang, Wei Zhang

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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  4. Review
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.

Jiaming Liu *Anhui Province Key Laboratory of Measuring Theory and Precision Instruments, School of Instrumental Science and Optoelectronics Engineering, Hefei University of Technology, 230009 Hefei, Anhui China.
Jingui Qian *Anhui Province Key Laboratory of Measuring Theory and Precision Instruments, School of Instrumental Science and Optoelectronics Engineering, Hefei University of Technology, 230009 Hefei, Anhui China.
Murtazt AdilSchool of Physics and Optoelectronic Engineering, Guangdong University of Technology, 510006 Guangzhou, Guangdong China.
Yali BiAnhui Province Key Laboratory of Measuring Theory and Precision Instruments, School of Instrumental Science and Optoelectronics Engineering, Hefei University of Technology, 230009 Hefei, Anhui China.
Haoyi WuSchool of Physics and Optoelectronic Engineering, Guangdong University of Technology, 510006 Guangzhou, Guangdong China.
Xuefeng HuAnhui Province Key Laboratory of Measuring Theory and Precision Instruments, School of Instrumental Science and Optoelectronics Engineering, Hefei University of Technology, 230009 Hefei, Anhui China.
Zuankai WangDepartment of Mechanical Engineering, The Hong Kong Polytechnical University, Hong Kong SAR, China.ORCID 0000-0002-3510-1122
Wei ZhangSchool of Physics and Optoelectronic Engineering, Guangdong University of Technology, 510006 Guangzhou, Guangdong China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An electronic tongue (E-tongue) comprises a series of sensors that simulate human perception of taste and embedded artificial intelligence (AI) for data analysis and recognition. Traditional E-tongues based on electrochemical methods suffer from a bulky size and require larger sample volumes and extra power sources, limiting their applications in in vivo medical diagnosis and analytical chemistry. Inspired by the mechanics of the human tongue, triboelectric components have been incorporated into E-tongue platforms to overcome these limitations. In this study, an integrated multichannel triboelectric bioinspired E-tongue (TBIET) device was developed on a single glass slide chip to improve the device's taste classification accuracy by utilizing numerous sensory signals. The detection capability of the TBIET was further validated using various test samples, including representative human body, environmental, and beverage samples. The TBIET achieved a remarkably high classification accuracy. For instance, chemical solutions showed 100% identification accuracy, environmental samples reached 98.3% accuracy, and four typical teas demonstrated 97.0% accuracy. Additionally, the classification accuracy of NaCl solutions with five different concentrations reached 96.9%. The innovative TBIET exhibits a remarkable capacity to detect and analyze droplets with ultrahigh sensitivity to their electrical properties. Moreover, it offers a high degree of reliability in accurately detecting and analyzing various liquid samples within a short timeframe. The development of a self-powered portable triboelectric E-tongue prototype is a notable advancement in the field and is one that can greatly enhance the feasibility of rapid on-site detection of liquid samples in various settings.

Indexed as

BiosensorsElectrical and electronic engineering

Identifiers

PMID38725435
PMCPMC11079038

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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.