Evidence map›Paper›PMID 34327113›Full record

ReviewBeilstein journal of nanotechnology2021

Nanogenerator-based self-powered sensors for data collection.

Yicheng Shao, Maoliang Shen, Yuankai Zhou, Xin Cui, Lijie Li, Yan Zhang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Beilstein journal of nanotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

6 authors.

Yicheng ShaoSchool of Physics, University of Electronic Science and Technology of China, Chengdu 610054, China.
Maoliang ShenSchool of Physics, University of Electronic Science and Technology of China, Chengdu 610054, China.
Yuankai ZhouSchool of Physics, University of Electronic Science and Technology of China, Chengdu 610054, China.
Xin CuiCollege of Chemistry and Chemical Engineering, Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China.
Lijie LiMultidisciplinary Nanotechnology Centre, College of Engineering, Swansea University, Swansea, SA1 8EN, UK.ORCID https://orcid.org/0000-0003-4630-7692
Yan ZhangSchool of Physics, University of Electronic Science and Technology of China, Chengdu 610054, China.ORCID https://orcid.org/0000-0002-7329-0382

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Self-powered sensors can provide energy and environmental data for applications regarding the Internet of Things, big data, and artificial intelligence. Nanogenerators provide excellent material compatibility, which also leads to a rich variety of nanogenerator-based self-powered sensors. This article reviews the development of nanogenerator-based self-powered sensors for the collection of human physiological data and external environmental data. Nanogenerator-based self-powered sensors can be designed to detect physiological data as wearable and implantable devices. Nanogenerator-based self-powered sensors are a solution for collecting data and expanding data dimensions in a future intelligent society. The future key challenges and potential solutions regarding nanogenerator-based self-powered sensors are discussed.

Indexed as

data collectionInternet of Thingsnanogeneratorself-powered sensorwearable device

Identifiers

PMID34327113
PMCPMC8275872

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.