Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 236

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

Fe2+/HClO Reaction Produces FeIVO2+: An Enhanced Advanced Oxidation Process

Liang, Sheng, Zhu, Liuyi, Hua, Jian, Duan, Weijian, Yang, Puu-Tai, Wang, Shan-Li, Wei, Chaohai, Liu, Chengshuai, Feng, Chunhua·2020·Environmental Science & Technology

Abstract

Abstract The reaction between Fe2+ and HClO constitutes a promising advanced oxidation process (AOP) for removing pollutants from wastewater, and •OH has been considered the dominant reactive oxidant despite limited evidence for this. Herein, we demonstrate that the Fe2+/HClO reaction enables the production of FeIVO2+ rather than •OH in acid medium, a finding that is strongly supported by multiple lines of evidence. Both X-ray absorption near-edge structure spectroscopic tests and Mössbauer spectroscopic tests confirmed the appearance of FeIVO2+ as the reactive intermediate in the reaction between Fe2+ and HClO. The determination of FeIVO2+ generation was also derived from the methyl phenyl sulfoxide (PMSO)-based probe experiments with respect to the formation of PMSO2 without •OH adducts and the density functional theory studies according to the lower energy barrier for producing FeIVO2+ compared with •OH. A dual-anode electrolytic system was established for the in situ generation of Fe2+ and HClO that allows the production of FeIVO2+. The system exhibits an enhanced capacity for oxidizing a model pollutant (e.g., phosphite) from industrial wastewater, making it an attractive and promising AOP for the abatement of aqueous contaminants.

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.0c00218

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

Cited by

See all 236

Metadata

Authors
Liang, S., Zhu, L., Hua, J., Duan, W., Yang, P.-T., Wang, S.-L., Wei, C., Liu, C., & Feng, C.
Venue
Environmental Science & Technology
Published
2020-03-11
Volume
54
Issue
10
Pages
6406-6414
ISSN
  • 0013-936X (ISSN-L)
  • 1520-5851
OpenAlex
W3012072870
Cited by
236
References
2
FWCI
8.13
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.