Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 669

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

An overview of advanced methods for the characterization of oxygen vacancies in materials

Ye, Kaihang, Li, Kunshan, Lu, Yirui, Guo, Zhongjie, Ni, Nan, Liu, Hong, Huang, Yongchao, Ji, Hongbing, Wang, Pingshan·2019·TrAC Trends in Analytical Chemistry

Abstract

Abstract Oxygen vacancy plays an important role in many fields, such as photocatalysis, energy storages, electro-catalysis, which has been widely investigated both by experiment and theoretical calculations. Herein, this review presents the new type and advanced methods to detect the existence of oxygen vacancies in nanomaterials, such as, electron paramagnetic resonance, synchrotron radiation-based X-ray absorption fine structure, X-ray photoelectron spectroscopy and positron annihilation spectroscopy measurements. In addition, we provide some perspectives on the challenge and new direction for future research in oxygen vacancy. We hope that this minireview can offer some useful contributions to the future development of defective nanomaterials for diverse application.

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.trac.2019.05.002

Cites

2 of 2 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 2 references

Cited by

See all 669

Metadata

Authors
Ye, K., Li, K., Lu, Y., Guo, Z., Ni, N., Liu, H., Huang, Y., Ji, H., & Wang, P.
Venue
TrAC Trends in Analytical Chemistry
Publisher
Elsevier BV
Published
2019-07
Volume
116
Pages
102-108
ISSN
0165-9936 (ISSN-L)
OpenAlex
W2944692848
Cited by
669
References
2
FWCI
29.71
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: An overview of advanced methods for the characterization of oxygen vacancies in materials. https://app.cochat.ai/papers/10.1016/j.trac.2019.05.002
The paper
Ye, K., Li, K., Lu, Y., Guo, Z., Ni, N., Liu, H., Huang, Y., Ji, H., & Wang, P. (2019). An overview of advanced methods for the characterization of oxygen vacancies in materials. TrAC Trends in Analytical Chemistry. https://doi.org/10.1016/j.trac.2019.05.002

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.