The DOI matches its Crossref record; 128 of the 128 references it cites were found in a registry or an index.
Multilayered graphene oxide membranes for water treatment: A review
Abstract
Abstract Resolution of the global fresh-water crisis requires advanced separation systems and hence the search for perfect separation materials is being undertaken vigorously. The ideal membrane material should be capable of being easily fabricated and modified to obtain a controllable performance. Graphene oxide (GO) is a graphene-based material with oxygen-containing functional groups. In recent years, GO-based separation membranes have received a lot of attention in the field of separation technology. In this review, we discuss the methods to fabricate or modify multilayered GO membranes. GO membranes and conventional separation membranes are compared and discussed. Further, we compared the molecular dynamics simulation results and the applications of GO membranes. Finally, we summarize the challenges and point out possible future research directions. This review is expected to help researchers obtain a quick idea of multilayered GO membranes and aid in future research for designing and fabricating multilayered GO membranes.
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
The chemistry of graphene oxide
Dreyer, D. R., Park, S., Bielawski, C. W., & Ruoff, R. S. (2010) · Chem. Soc. Rev. · doi:10.1039/b917103g · Cited by 11,653
Science and technology for water purification in the coming decades
Shannon, M. A., Bohn, P. W., Elimelech, M., Georgiadis, J. G., Mariñas, B. J., & Mayes, A. M. (2008) · Nature · doi:10.1038/nature06599 · Cited by 8,465
Preparation and characterization of graphene oxide paper
Dikin, D. A., Stankovich, S., Zimney, E. J., Piner, R. D., Dommett, G. H. B., Evmenenko, G., Nguyen, S. T., & Ruoff, R. S. (2007) · Nature · doi:10.1038/nature06016 · Cited by 5,594
Graphene oxide as a chemically tunable platform for optical applications
Loh, K. P., Bao, Q., Eda, G., & Chhowalla, M. (2010) · Nature Chemistry · doi:10.1038/nchem.907 · Cited by 3,340
Unimpeded Permeation of Water Through Helium-Leak–Tight Graphene-Based Membranes
Nair, R. R., Wu, H. A., Jayaram, P. N., Grigorieva, I. V., & Geim, A. K. (2012) · Science · doi:10.1126/science.1211694 · Cited by 3,073
Precise and Ultrafast Molecular Sieving Through Graphene Oxide Membranes
Joshi, R. K., Carbone, P., Wang, F. C., Kravets, V. G., Su, Y., Grigorieva, I. V., Wu, H. A., Geim, A. K., & Nair, R. R. (2014) · Science · doi:10.1126/science.1245711 · Cited by 2,511
Water Desalination across Nanoporous Graphene
Cohen-Tanugi, D., & Grossman, J. C. (2012) · Nano Letters · doi:10.1021/nl3012853 · Cited by 2,204
Reduced graphene oxide by chemical graphitization
Moon, I. K., Lee, J., Ruoff, R. S., & Lee, H. (2010) · Nature Communications · doi:10.1038/ncomms1067 · Cited by 2,135
Tunable sieving of ions using graphene oxide membranes
Abraham, J., Vasu, K. S., Williams, C. D., Gopinadhan, K., Su, Y., Cherian, C. T., Dix, J., Prestat, E., Haigh, S. J., Grigorieva, I. V., et al. (2017) · Nature Nanotechnology · doi:10.1038/nnano.2017.21 · Cited by 1,875
Structural evolution during the reduction of chemically derived graphene oxide
Bagri, A., Mattevi, C., Acik, M., Chabal, Y. J., Chhowalla, M., & Shenoy, V. B. (2010) · Nature Chemistry · doi:10.1038/nchem.686 · Cited by 1,819
Cited by
2D Laminar Membranes for Selective Water and Ion Transport
Kang, Y., Xia, Y., Wang, H., & Zhang, X. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201902014 · Cited by 325
Mohammed, H., Kumar, A., Bekyarova, E., Al-Hadeethi, Y., Zhang, X., Chen, M., Ansari, M. S., Cochis, A., & Rimondini, L. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00465 · Cited by 312
Recent progress in the chemical reduction of graphene oxide by green reductants–A Mini review
Kurian, M. (2021) · Carbon Trends · doi:10.1016/j.cartre.2021.100120 · Cited by 85
Metadata
- Authors
- Wei, Y., Zhang, Y., Gao, X., Ma, Z., Wang, X., & Gao, C.
- Venue
- Carbon
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 139
- Pages
- 964-981
- ISSN
- 0008-6223 (ISSN-L)
- OpenAlex
- W2883717359
- Cited by
- 300
- References
- 128
- FWCI
- 13.15
- 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: Multilayered graphene oxide membranes for water treatment: A review. https://app.cochat.ai/papers/10.1016/j.carbon.2018.07.040
- The paper
- Wei, Y., Zhang, Y., Gao, X., Ma, Z., Wang, X., & Gao, C. (2018). Multilayered graphene oxide membranes for water treatment: A review. Carbon. https://doi.org/10.1016/j.carbon.2018.07.040
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.