Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 249

The DOI matches its Crossref record.

In-situ Pb2+ remediation using nano iron particles

Open access
Tehrani, Mohammad Reza Fadaei, Shamsai, Abolfazl, Vossughi, Manoochehr·2015·Journal of Environmental Health Science and Engineering

Abstract

Originally, application of nano zero valent iron (nZVI) particles for the removal of lead (Pb2+) in porous media was studied. At first, stabilized nZVI (S-nZVI) was prepared and characterized, then used in batch and continuous systems. Based on the batch experiments, corresponding reaction kinetics well fitted with the pseudo-first-order adsorption model, and reaction rate ranged from 0.01 to 0.04 g/mg/min depend on solution pH and the molar ratio between Fe and Pb. In batch tests, optimal condition with more than 90% removal efficiency at 60 min was observed at a pH range of 4 to 6 and Fe/Pb ratio more than 2.5. Continuous experiments exposed that Pb2+ remediation was as well influenced by seepage velocity, grain size, and type of porous media. The maximum Pb2+ removal efficiency in batch and bench-scale systems were 97% and 81%, correspondingly. The results have shown the ability of S-nZVI to use in permeable reactive barriers, as an efficient adsorbent for Pb2+, because of its excellent stability, high reducing power, and a large surface area.

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 · 10.1186/s40201-015-0157-3

Cites

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

Cited by

See all 249

Metadata

Authors
Tehrani, M. R. F., Shamsai, A., & Vossughi, M.
Venue
Journal of Environmental Health Science and Engineering
Published
2015-01-21
Volume
13
Issue
1
Pages
1-1
ISSN
  • 1735-1979 (ISSN-L)
  • 2052-336X
OpenAlex
W2114890484
Cited by
249
References
0
FWCI
16.04
Type
journal article
Language
en
Open access
bronze

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: In-situ Pb2+ remediation using nano iron particles. https://app.cochat.ai/papers/10.1186/s40201-015-0157-3
The paper
Tehrani, M. R. F., Shamsai, A., & Vossughi, M. (2015). In-situ Pb2+ remediation using nano iron particles. Journal of Environmental Health Science and Engineering. https://doi.org/10.1186/s40201-015-0157-3

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.