Evidence map›Paper›PMID 34203757›Full record

ReviewMicromachines2021

Advances in Smart Sensing and Medical Electronics by Self-Powered Sensors Based on Triboelectric Nanogenerators.

Min Jiang, Yi Lu, Zhiyuan Zhu, Wenzhu Jia

Abstract readReview
In one paragraph

Review in Micromachines, 2021. 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. Wearable disposable electrotherapy.Nature communications · 2025
    Article
  2. Review
  3. Universal Energy Solution for Triboelectric Sensors Toward the 5G Era and Internet of Things.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. What can AI-TENG do for Low Abundance Biosensing?Frontiers in bioengineering and biotechnology · 2022
    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

4 authors.

Min JiangFaculty of Mathematics and Information Science, Southwest University, Chongqing 400700, China.
Yi LuSchool of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Zhiyuan ZhuFaculty of Mathematics and Information Science, Southwest University, Chongqing 400700, China.
Wenzhu JiaFaculty of Mathematics and Information Science, Southwest University, Chongqing 400700, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid progress of artificial intelligence, humans are moving toward the era of the intelligent connection of all things. Therefore, the demand for sensors is drastically increasing with developing intelligent social applications. Traditional sensors must be triggered by an external power source and the energy consumption is high for equipment that is widely distributed and working intermittently, which is not conducive to developing sustainable green and healthy applications. However, self-powered sensors based on triboelectric nanogenerators (TENG) can autonomously harvest energy from the surrounding environment and convert this energy into electrical energy for storage. Sensors can also be self-powered without an external power supply, which is vital for smart cities, smart homes, smart transportation, environmental monitoring, wearable devices, and bio-medicine. This review mainly summarizes the working mechanism of TENG and the research progress of self-powered sensors based on TENG about the Internet of Things (IoT), robotics, human-computer interaction, and intelligent medical fields in recent years.

Indexed as

self-powered sensorsTENG

Identifiers

PMID34203757
PMCPMC8232818

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.