Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 536

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Effective energy storage from a triboelectric nanogenerator

Open access
Zi, Yunlong, Wang, Jie, Wang, Sihong, Li, Shengming, Wen, Zhen, Guo, Hengyu, Wang, Zhong Lin·2016·Nature Communications

Abstract

To sustainably power electronics by harvesting mechanical energy using nanogenerators, energy storage is essential to supply a regulated and stable electric output, which is traditionally realized by a direct connection between the two components through a rectifier. However, this may lead to low energy-storage efficiency. Here, we rationally design a charging cycle to maximize energy-storage efficiency by modulating the charge flow in the system, which is demonstrated on a triboelectric nanogenerator by adding a motion-triggered switch. Both theoretical and experimental comparisons show that the designed charging cycle can enhance the charging rate, improve the maximum energy-storage efficiency by up to 50% and promote the saturation voltage by at least a factor of two. This represents a progress to effectively store the energy harvested by nanogenerators with the aim to utilize ambient mechanical energy to drive portable/wearable/implantable electronics.

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 · 10.1038/ncomms10987

Cites

5 of 5 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 5 references

Cited by

See all 536

Metadata

Authors
Zi, Y., Wang, J., Wang, S., Li, S., Wen, Z., Guo, H., & Wang, Z. L.
Venue
Nature Communications
Published
2016-03-11
Volume
7
Issue
1
Pages
10987-10987
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2325609638
Cited by
536
References
5
FWCI
26.14
Type
journal article
Language
en
Open access
gold

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: Effective energy storage from a triboelectric nanogenerator. https://app.cochat.ai/papers/10.1038/ncomms10987
The paper
Zi, Y., Wang, J., Wang, S., Li, S., Wen, Z., Guo, H., & Wang, Z. L. (2016). Effective energy storage from a triboelectric nanogenerator. Nature Communications. https://doi.org/10.1038/ncomms10987

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.