Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 398

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

Mechanism of Arsenic Adsorption on Magnetite Nanoparticles from Water: Thermodynamic and Spectroscopic Studies

Liu, Cheng-Hua, Chuang, Ya-Hui, Chen, Tsan-Yao, Tian, Yuan, Li, Hui, Wang, Ming-Kuang, Zhang, Wei·2015·Environmental Science & Technology

Abstract

Removal of arsenic (As) from water supplies is needed to reduce As exposure through drinking water and food consumption in many regions of the world. Magnetite nanoparticles (MNPs) are promising and novel adsorbents for As removal because of their great adsorption capacity for As and easy separation. This study aimed to investigate the adsorption mechanism of arsenate, As(V), and arsenite, As(III), on MNPs by macroscopic adsorption experiments in combination with thermodynamic calculation and microspectroscopic characterization using synchrotron-radiation-based X-ray absorption spectroscopy (XAS) and X-ray photoelectron spectroscopy (XPS). Adsorption reactions are favorable endothermic processes as evidenced by increased adsorption with increasing temperatures, and high positive enthalpy change. EXAFS spectra suggested predominant formation of bidentate binuclear corner-sharing complexes ((2)C) for As(V), and tridentate hexanuclear corner-sharing ((3)C) complexes for As(III) on MNP surfaces. The macroscopic and microscopic data conclusively identified the formation of inner-sphere complexes between As and MNP surfaces. More intriguingly, XANES and XPS results revealed complex redox transformation of the adsorbed As on MNPs exposed to air: Concomitant with the oxidation of MNPs, the oxidation of As(III) and MNPs was expected, but the observed As(V) reduction was surprising because of the role played by the reactive Fe(II).

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.5b00381

Cites

56 of 56 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 398

Metadata

Authors
Liu, C.-H., Chuang, Y.-H., Chen, T.-Y., Tian, Y., Li, H., Wang, M.-K., & Zhang, W.
Venue
Environmental Science & Technology
Published
2015-06-19
Volume
49
Issue
13
Pages
7726-7734
ISSN
  • 0013-936X (ISSN-L)
  • 1520-5851
OpenAlex
W2398334696
Cited by
398
References
56
FWCI
25.44
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.