Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 241

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

Design Principles for Covalent Organic Frameworks as Efficient Electrocatalysts in Clean Energy Conversion and Green Oxidizer Production

Lin, Chun‐Yu, Zhang, Lipeng, Zhao, Zhenghang, Xia, Zhenhai·2017·Advanced Materials

Abstract

Covalent organic frameworks (COFs), an emerging class of framework materials linked by covalent bonds, hold potential for various applications such as efficient electrocatalysts, photovoltaics, and sensors. To rationally design COF‐based electrocatalysts for oxygen reduction and evolution reactions in fuel cells and metal‐air batteries, activity descriptors, derived from orbital energy and bonding structures, are identified with the first‐principle calculations for the COFs, which correlate COF structures with their catalytic activities. The calculations also predict that alkaline‐earth metal‐porphyrin COFs could catalyze the direct production of H 2 O 2, a green oxidizer and an energy carrier. These predictions are supported by experimental data, and the design principles derived from the descriptors provide an approach for rational design of new electrocatalysts for both clean energy conversion and green oxidizer production.

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 · 10.1002/adma.201606635

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 241

Metadata

Authors
Lin, C., Zhang, L., Zhao, Z., & Xia, Z.
Venue
Advanced Materials
Publisher
Wiley
Published
2017-02-23
Volume
29
Issue
17
ISSN
  • 0935-9648 (ISSN-L)
  • 1521-4095
OpenAlex
W2591409535
Cited by
241
References
2
FWCI
5.27
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: Design Principles for Covalent Organic Frameworks as Efficient Electrocatalysts in Clean Energy Conversion and Green Oxidizer Production. https://app.cochat.ai/papers/10.1002/adma.201606635
The paper
Lin, C., Zhang, L., Zhao, Z., & Xia, Z. (2017). Design Principles for Covalent Organic Frameworks as Efficient Electrocatalysts in Clean Energy Conversion and Green Oxidizer Production. Advanced Materials. https://doi.org/10.1002/adma.201606635

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.