Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 745

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Engineering Crystal Facet of α-MnO2 Nanowire for Highly Efficient Catalytic Oxidation of Carcinogenic Airborne Formaldehyde

Rong, Shaopeng, Zhang, Pengyi, Liu, Fang, Yang, Yajie·2018·ACS Catalysis

Abstract

The activity of exposed crystal facets directly determines its physicochemical properties. Thus, acquiring a high percentage of reactive facets by crystal facet engineering is highly desirable for improving the catalytic reactivity. Herein, single-crystalline α-MnO 2 nanowires with major exposed high-index {310} facets were synthesized via a facile hydrothermal route with the assistance of a capping agent of oxalate ions. Comparing with two other low-index facets ({100} and {110}), the resulting α-MnO 2 nanowires with exposed {310} facets exhibited much better activity and stability for carcinogenic formaldehyde (HCHO) oxidation, making 100% of 100 ppm of HCHO mineralize into CO 2 at 60 °C, even better than some Ag supported catalysts. The density functional theory (DFT) calculations were used to investigate the difference in the catalytic activity of α-MnO 2 with exposed {100}, {110}, and {310} facets. The experimental characterization and theoretical calculations all confirm that the {310} facets with high surface energy can not only facilitate adsorption/activation of O 2 and H 2 O but also be beneficial to the generation of oxygen vacancies, which result in significantly enhanced activity for HCHO oxidation. This is a valuable report on engineering surface facets in the preparation of α-MnO 2 as highly efficient oxidation catalysts. This study deepens the understanding of facet-dependent activity of α-MnO 2 and points out a strategy to improve their catalytic activity by crystal facet engineering.

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.8b00456

Cites

6 of 6 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 745

Metadata

Authors
Rong, S., Zhang, P., Liu, F., & Yang, Y.
Venue
ACS Catalysis
Published
2018-03-14
Volume
8
Issue
4
Pages
3435-3446
ISSN
2155-5435 (ISSN-L)
OpenAlex
W2789449801
Cited by
745
References
6
FWCI
19.25
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.