The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Magnetic Nanocarbon Adsorbents with Enhanced Hexavalent Chromium Removal: Morphology Dependence of Fibrillar vs Particulate Structures
Abstract
Fibrillar and particulate structure magnetic carbons (MCFs and MCPs) were prepared from the same precursor (polyacrylonitrile and Fe(NO 3 ) 3 ·9H 2 O) by using a different method, displaying a significant morphology dependence on wastewater treatment. TEM, SEM, XPS, TGA, etc. were systematically carried out to characterize the carbon samples to verify the morphology difference between these two kinds of carbon adsorbents. The results demonstrated that, along with the increase of the Fe(NO 3 ) 3 ·9H 2 O loading in the precursor from 10 to 40 wt %, the fibrillar nanoadsorbents displayed an improved activity from 12.6% to 51.4% Cr(VI) removal percentage with the initial Cr(VI) concentration at 4 mg/L. For the maximum removal capacity, the fibrillar sample (MCFs-40) demonstrated 3 times higher removing capacity (43.17 mg/g) than that of particulate nanoadsorbents (MCPs-40, 15.88 mg/g) for the Cr(VI) removal with pH at 1, demonstrating that the fibrillar sample was more favorable for the wastewater treatment than particulate sample. This enhanced removal was mainly attributed to higher specific surface area of the fibrillar sample, leading to more active sites for the adsorption of Cr(VI) and produced Cr(III) ions. The chemical adsorption of Cr(VI) ions over two kinds of adsorbents were disclosed in this removal process. There was a good stability of 5 recycles for the Cr(VI) removal in the neutral solution over MCFs-40 (about 1.4 mg/g) and MCPs-40 (about 0.41 mg/g) with initial Cr(VI) concentration at 4 mg/L. This work can provide an understanding for the rational design of adsorbent in wastewater treatment.
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.
Integrity signals
Registry
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.
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
Mohammadi, N., Khani, H., Gupta, V. K., Amereh, E., & Agarwal, S. (2011) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2011.06.067 · Cited by 818
Gupta, V. K., Rastogi, A., & Nayak, A. (2010) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2009.09.065 · Cited by 644
Mittal, A., Mittal, J., Malviya, A., Kaur, D., & Gupta, V. K. (2010) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2009.10.046 · Cited by 496
Cited by
Electromagnetic Interference Shielding Polymers and Nanocomposites - A Review
Jiang, D., Murugadoss, V., Wang, Y., Lin, J., Ding, T., Wang, Z., Shao, Q., Wang, C., Liu, H., Lu, N., et al. (2019) · Polymer Reviews · doi:10.1080/15583724.2018.1546737 · Cited by 729
Self-citationElectrically conductive polymer composites for smart flexible strain sensors: a critical review
Liu, H., Li, Q., Zhang, S., Yin, R., Liu, X., He, Y., Dai, K., Shan, C., Guo, J., Liu, C., et al. (2018) · Journal of Materials Chemistry C · doi:10.1039/c8tc04079f · Cited by 709
Self-citationQi, H., Teng, M., Liu, M., Liu, S., Li, J., Yu, H., Teng, C., Huang, Z., Liu, H., Shao, Q., et al. (2019) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2018.12.047 · Cited by 640
Self-citationXu, G.-R., An, Z.-H., Xu, K., Liu, Q., Das, R., & Zhao, H.-L. (2021) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2020.213554 · Cited by 379
Kaur, R., Kaur, A., Umar, A., Anderson, W. A., & Kansal, S. K. (2019) · Materials Research Bulletin · doi:10.1016/j.materresbull.2018.07.025 · Cited by 292
Metadata
- Authors
- Huang, J., Cao, Y., Shao, Q., Peng, X., & Guo, Z.
- Venue
- Industrial & Engineering Chemistry Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-09-14
- Volume
- 56
- Issue
- 38
- Pages
- 10689-10701
- ISSN
- 0888-5885 (ISSN-L)
- 1520-5045
- OpenAlex
- W2750723537
- Cited by
- 286
- References
- 3
- FWCI
- 30.80
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Magnetic Nanocarbon Adsorbents with Enhanced Hexavalent Chromium Removal: Morphology Dependence of Fibrillar vs Particulate Structures. https://app.cochat.ai/papers/10.1021/acs.iecr.7b02835
- The paper
- Huang, J., Cao, Y., Shao, Q., Peng, X., & Guo, Z. (2017). Magnetic Nanocarbon Adsorbents with Enhanced Hexavalent Chromium Removal: Morphology Dependence of Fibrillar vs Particulate Structures. Industrial & Engineering Chemistry Research. https://doi.org/10.1021/acs.iecr.7b02835
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.