Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 164

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Nanoengineering Construction of Cu2O Nanowire Arrays Encapsulated with g-C3N4 as 3D Spatial Reticulation All-Solid-State Direct Z-Scheme Photocatalysts for Photocatalytic Reduction of Carbon Dioxide

Open access
Zhao, Xin, Fan, Yingying, Zhang, Wensheng, Zhang, Xiaojing, Han, Dongxue, Niu, Li, Ivaska, Ari·2020·ACS Catalysis

Abstract

Artificial photosynthesis is a promising strategy to convert carbon dioxide into value-added fuels and chemicals with efficient sunlight utilization. Nevertheless, there are many shortcomings, such as high photogenerated electron–hole recombination rate and poor light stability, that hinder the further development of artificial photosynthesis. Thus, inspired by the natural photosynthesis of green plants, a 3D spatial reticulation all-solid-state artificial direct Z-scheme photocatalyst is manufactured to solve the above problems by using a graphite phase carbon nitride (g-C 3 N 4 ) shell encapsulating Cu 2 O nanowire arrays/Cu mesh (g-C 3 N 4 /Cu 2 O NAs/CM). Consequently, the CH 3 OH formation rate and the selectivity of g-C 3 N 4 /Cu 2 O NAs reached 22.6 ppm cm –2 h –1 and 94.9% under solar light irradiation, respectively. More importantly, the material retained 94.7% of its primary catalytic activity after 60 h test (ten reaction cycles). It is attributed to the protective g-C 3 N 4 layer and the synergetic effect between interface carrier separation modulation and relatively short radial transmission channel design. This strategy provides unique insights into the design and preparation of efficient practical photocatalysts for energy synthesis.

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/acscatal.0c01033

Cites

4 of 4 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 4 references

Metadata

Authors
Zhao, X., Fan, Y., Zhang, W., Zhang, X., Han, D., Niu, L., & Ivaska, A.
Venue
ACS Catalysis
Published
2020-05-13
Volume
10
Issue
11
Pages
6367-6376
ISSN
2155-5435 (ISSN-L)
OpenAlex
W3025128337
Cited by
164
References
4
FWCI
4.91
Type
journal article
Language
en
Open access
green

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.