Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 210

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Highly Efficient Raindrop Energy-Based Triboelectric Nanogenerator for Self-Powered Intelligent Greenhouse

Zhang, Qi, Jiang, Chengmei, Li, Xunjia, Dai, Shufen, Ying, Yibin, Ping, Jianfeng·2021·ACS Nano

Abstract

Establishing a sustainable energy supply is necessary for intelligent greenhouse environmental management. Compared with traditional energy, green and eco-friendly energy is more conducive to protecting the agricultural production environment. In this study, a fluorinated superhydrophobic greenhouse film is proposed as a negative triboelectric layer material for the construction of a triboelectric nanogenerator that harvests raindrop energy (RDE-TENG). Moreover, an upgraded configuration is adopted, where the bulk effect between the lower/upper electrode and film replaces the interfacial effect of the liquid-solid interface, thereby promoting charge transfer. The results show that the RDE-TENG can serve as a sustainable energy source for greenhouse temperature and humidity sensors that assists in realizing intelligent control of the environment and guides agricultural production processes. This device exhibits high-voltage and a stable output; thus, it has the potential to replace traditional energy sources, which helps toward realizing a self-powered intelligent greenhouse planting mode.

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 · 10.1021/acsnano.1c04258

Cites

3 of 3 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

Cited by

See all 210

Metadata

Authors
Zhang, Q., Jiang, C., Li, X., Dai, S., Ying, Y., & Ping, J.
Venue
ACS Nano
Published
2021-06-30
Volume
15
Issue
7
Pages
12314-12323
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W3173543840
Cited by
210
References
3
FWCI
10.43
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

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.