Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 524

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

NiO/CoN Porous Nanowires as Efficient Bifunctional Catalysts for Zn–Air Batteries

Yin, Jie, Li, Yuxuan, Lv, Fan, Fan, Qiaohui, Zhao, Yong-Qing, Zhang, Qiaolan, Wang, Wei, Cheng, Fangyi, Xi, Pinxian, Guo, Shaojun·2017·ACS Nano

Abstract

The development of highly efficient bifunctional catalysts for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) is crucial for improving the efficiency of the Zn–air battery. Herein, we report porous NiO/CoN interface nanowire arrays (PINWs) with both oxygen vacancies and a strongly interconnected nanointerface between NiO and CoN domains for promoting the electrocatalytic performance and stability for OER and ORR. Extended X-ray absorption fine structure spectroscopy, electron spin resonance, and high-resolution transmission electron microscopy investigations demonstrate that the decrease of the coordination number for cobalt, the enhanced oxygen vacancies on the NiO/CoN nanointerface, and strongly coupled nanointerface between NiO and CoN domains are responsible for the good bifunctional electrocatalytic performance of NiO/CoN PINWs. The primary Zn–air batteries, using NiO/CoN PINWs as an air–cathode, display an open-circuit potential of 1.46 V, a high power density of 79.6 mW cm –2, and an energy density of 945 Wh kg –1 . The three-series solid batteries fabricated by NiO/CoN PINWs can support a timer to work for more than 12 h. This work demonstrates the importance of interface coupling and oxygen vacancies in the development of high-performance Zn–air batteries.

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.7b00417

Cites

8 of 8 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 524

Metadata

Authors
Yin, J., Li, Y., Lv, F., Fan, Q., Zhao, Y.-Q., Zhang, Q., Wang, W., Cheng, F., Xi, P., & Guo, S.
Venue
ACS Nano
Published
2017-02-16
Volume
11
Issue
2
Pages
2275-2283
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2588986811
Cited by
524
References
8
FWCI
18.86
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.