Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 257

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Breeze-driven triboelectric nanogenerator for wind energy harvesting and application in smart agriculture

Li, Xiang, Cao, Yuying, Yu, Xin, Xu, Yuhong, Yang, Yanfei, Liu, Shiming, Cheng, Tinghai, Wang, Zhong Lin·2022·Applied Energy

Abstract

the output performance of the BD-TENG is 330 V, 7 μ A, 137 nC, and the peak power is 2.81 mW. So, the BD-TENG is easier to operate normally even in low wind speed environments and can harvest natural breeze energy efficiently. Experiments prove that in natural environments, the BD-TENG successfully lights up 300 red and blue light- emitting diodes in series, which can be applied to increase lighting time for plants at night. Moreover, the BD-TENG can power a soil thermometer by harvesting natural breeze energy. Therefore, the BD-TENG can be widely used in farmland environments to provide energy for agricultural sensor networks. The BD-TENG has bright prospects in smart agriculture and can promote its sustainable development.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.apenergy.2021.117977

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 33 references

Cited by

See all 257

Metadata

Authors
Li, X., Cao, Y., Yu, X., Xu, Y., Yang, Y., Liu, S., Cheng, T., & Wang, Z. L.
Venue
Applied Energy
Publisher
Elsevier BV
Published
2022-01
Volume
306
Pages
117977
ISSN
0306-2619 (ISSN-L)
OpenAlex
W3206328624
Cited by
257
References
33
FWCI
11.76
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Breeze-driven triboelectric nanogenerator for wind energy harvesting and application in smart agriculture. https://app.cochat.ai/papers/10.1016/j.apenergy.2021.117977
The paper
Li, X., Cao, Y., Yu, X., Xu, Y., Yang, Y., Liu, S., Cheng, T., & Wang, Z. L. (2022). Breeze-driven triboelectric nanogenerator for wind energy harvesting and application in smart agriculture. Applied Energy. https://doi.org/10.1016/j.apenergy.2021.117977

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.