Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 228

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

Dechlorination of Trichloroacetic Acid Using a Noble Metal-Free Graphene–Cu Foam Electrode via Direct Cathodic Reduction and Atomic H*

Open access
Mao, Ran, Li, Ning, Lan, Huachun, Zhao, Xu, Liu, Huijuan, Qu, Jiuhui, Sun, Meng·2016·Environmental Science & Technology

Abstract

A three-dimensional graphene-copper (3D GR-Cu) foam electrode prepared by chemical vapor deposition method exhibited superior electrocatalytic activity toward the dechlorination of trichloroacetic acid (TCAA) as compared to the Cu foam electrode. The cyclic voltammetry and electrochemical impedance spectra analysis confirmed that GR accelerated the electron transfer from the cathode surface to TCAA. With the applied cathode potential of -1.2 V (vs SCE), 95.3% of TCAA (500 μg/L) was removed within 20 min at pH 6.8. TCAA dechlorination at the Cu foam electrode was enhanced at acidic pH, while a slight pH effect was observed at the GR-Cu foam electrode with a significant inhibition for Cu leaching. The electrocatalytic dechlorination of TCAA was accomplished via a combined stepwise and concerted pathway on both electrodes, whereas the concerted pathway was efficiently promoted on the GR-Cu foam electrode. The direct reduction by electrons was responsible for TCAA dechlorination at Cu foam electrode, while at GR-Cu foam electrode, the surface-adsorbed atomic H* also contributed to TCAA dechlorination owing to the chemical storage of hydrogen in the GR structure. Finally, the potential applicability of GR-Cu foam was revealed by its stability in the electrocatalytic dechlorination over 25 cycles.

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/acs.est.5b05006

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

Cited by

See all 228

Metadata

Authors
Mao, R., Li, N., Lan, H., Zhao, X., Liu, H., Qu, J., & Sun, M.
Venue
Environmental Science & Technology
Published
2016-03-23
Volume
50
Issue
7
Pages
3829-3837
ISSN
  • 0013-936X (ISSN-L)
  • 1520-5851
OpenAlex
W2298591544
Cited by
228
References
4
FWCI
9.26
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.