Evidence map›Paper›PMID 35624624›Full record

ReviewBiosensors2022

From Triboelectric Nanogenerator to Polymer-Based Biosensor: A Review.

Yin Lu, Yajun Mi, Tong Wu, Xia Cao, Ning Wang

Abstract readReview
In one paragraph

Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Ferroelectric Material in Triboelectric Nanogenerator.Materials (Basel, Switzerland) · 2024
    Review
  6. Review
  7. Article
  8. Review
  9. 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

5 authors.

Yin LuCenter for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Yajun MiCenter for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Tong WuCenter for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Xia CaoBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China.
Ning WangCenter for Green Innovation, School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0001-9772-7270

Funding

National Key R&D Project from Ministry of Science and Technology, China 2016YFA0202702, 2016YFA0202701National Natural Science Foundation of China NSFC No. 51873020University Basic Scientific Research Business Fee FRF-MP-20-38
6 · The paper itself

Abstract

Nowadays, self-powered wearable biosensors that are based on triboelectric nanogenerators (TENGs) are playing an important role in the continuous efforts towards the miniaturization, energy saving, and intelligence of healthcare devices and Internets of Things (IoTs). In this review, we cover the remarkable developments in TENG-based biosensors developed from various polymer materials and their functionalities, with a focus on wearable and implantable self-powered sensors for health monitoring and therapeutic devices. The functions of TENGs as power sources for third-party biosensors are also discussed, and their applications in a number of related fields are concisely illustrated. Finally, we conclude the review with a discussion of the challenges and problems of leveraging TENG-based intelligent biosensors.

Indexed as

Biosensing TechniquesPolymersElectric Power SuppliesNanotechnologyProstheses and ImplantsPolymersenergy harvestingpolymer biosensorsself-powertriboelectric nanogenerator

Identifiers

PMID35624624
PMCPMC9138307

What OpenQuestion holds

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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.