The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Under seawater superoleophobic PVDF membrane inspired by polydopamine for efficient oil/seawater separation
Open accessAbstract
Abstract Under seawater superoleophobic polyvinylidene fluoride (PVDF) membrane inspired by mussel is successfully fabricated for both surfactant-free and surfactant-stabilized oil/seawater separation. The conventional PVDF membrane is modified by a simple solution-immersion method, which was immersed in dopamine aqueous solution for 24 h. The morphology and chemistry of dopamine inspired PVDF membrane was characterized by scanning electron microscope (SEM), atomic force microscopy (AFM), attenuated total reflectance Fourier transform infrared spectra (FTIR-ATR) and X-ray photoelectron spectroscopy (XPS) respectively. The as-prepared membrane obtained stable superoleophobicity under seawater with an oil contact angle of 152±0.3° and extremely low oil-adhesion. Separation experiments for both surfactant-free and surfactant-stabilized emulsions showed that our membrane exhibited high oil/seawater separation efficiency (oil concentration in filtrate below 80 ppm) and substantially high permeability, which are several times higher than that current traditional filtration membrane. Fouling resistance to proteins was also investigated for long-term use purpose. This study provides a facile solution-immersion method to fabricate under seawater superoleophobic membrane, highlighting its great potential in practical oil/seawater separation.
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
Mussel-inspired surface chemistry for multifunctional coatings
Lee, H., Dellatore, S. M., Miller, W. M., & Messersmith, P. B. (2007) · Science · doi:10.1126/science.1147241 · Cited by 10,965
Characterization of Dopamine−Melanin Growth on Silicon Oxide
Bernsmann, F., Ponche, A., Ringwald, C., Hemmerlé, J., Raya, J., Bechinger, B., Voegel, J.-C., Schaaf, P., & Ball, V. (2009) · The Journal of Physical Chemistry C · doi:10.1021/jp901188h · Cited by 348
Cited by
Antifouling membranes for sustainable water purification: strategies and mechanisms
Zhang, R., Liu, Y., He, M., Su, Y., Zhao, X., Elimelech, M., & Jiang, Z. (2016) · Chemical Society Reviews · doi:10.1039/c5cs00579e · Cited by 1,365
Ge, J., Zong, D., Jin, Q., Yu, J., & Ding, B. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201705051 · Cited by 677
Surface engineering of polymer membranes via mussel-inspired chemistry
Yang, H.-C., Luo, J., Lv, Y., Shen, P., & Xu, Z.-K. (2015) · Journal of Membrane Science · doi:10.1016/j.memsci.2015.02.027 · Cited by 406
Barclay, T. G., Hegab, H. M., Clarke, S. R., & Ginic‐Markovic, M. (2017) · Advanced Materials Interfaces · doi:10.1002/admi.201601192 · Cited by 384
Specially Wettable Membranes for Oil–Water Separation
Wei, Y., Qi, H., Gong, X., & Zhao, S. (2018) · Advanced Materials Interfaces · doi:10.1002/admi.201800576 · Cited by 304
Metadata
- Authors
- Xiang, Y., Liu, F., & Xue, L.
- Venue
- Journal of Membrane Science
- Publisher
- Elsevier BV
- Published
- 2015-02
- Volume
- 476
- Pages
- 321-329
- ISSN
- 0376-7388 (ISSN-L)
- OpenAlex
- W1999209871
- Cited by
- 162
- References
- 2
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- green
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: Under seawater superoleophobic PVDF membrane inspired by polydopamine for efficient oil/seawater separation. https://app.cochat.ai/papers/10.1016/j.memsci.2014.11.052
- The paper
- Xiang, Y., Liu, F., & Xue, L. (2015). Under seawater superoleophobic PVDF membrane inspired by polydopamine for efficient oil/seawater separation. Journal of Membrane Science. https://doi.org/10.1016/j.memsci.2014.11.052
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.
