The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Electrospun Polystyrene Nanofiber Membrane with Superhydrophobicity and Superoleophilicity for Selective Separation of Water and Low Viscous Oil
Abstract
The ability to prepare solid surfaces with well-controlled superhydrophobic and superoleophilic properties is of paramount importance to water-oil separation technology. Herein, we successfully prepared superhydrophobic-superoleophilic membranes by single-step deposition of polystyrene (PS) nanofibers onto a stainless steel mesh via electrospinning. The contact angles of diesel and water on the prepared PS nanofiber membrane were 0° and 155° ± 3°, respectively. Applications of the PS nanofiber membrane toward separating liquids with low surface tension, such as oil, from water were investigated in detail. Gasoline, diesel, and mineral oil were tested as representative low-viscosity oils. The PS nanofiber membranes efficiently separated several liters of oil from water in a single step, of only a few minutes' duration. The superhydrophobic PS nanofiber membrane selectively absorbs oil, and is highly efficient at oil-water separation, making it a very promising material for oil spill remediation.
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
Wettability of porous surfaces
Cassie, A. B. D., & Baxter, S. (1944) · Transactions of the Faraday Society · doi:10.1039/tf9444000546 · Cited by 13,807
RESISTANCE OF SOLID SURFACES TO WETTING BY WATER
Wenzel, R. N. (1936) · Industrial & Engineering Chemistry · doi:10.1021/ie50320a024 · Cited by 13,316
Cited by
Application of superhydrophobic coatings as a corrosion barrier: A review
Vazirinasab, E., Jafari, R., & Momen, G. (2018) · Surface and Coatings Technology · doi:10.1016/j.surfcoat.2017.11.053 · Cited by 659
Recent advances in the potential applications of bioinspired superhydrophobic materials
Darmanin, T., & Guittard, F. (2014) · J. Mater. Chem. A · doi:10.1039/c4ta02071e · Cited by 555
Metadata
- Authors
- Lee, M. W., An, S., Latthe, S. S., Lee, C., Hong, S., & Yoon, S. S.
- Venue
- ACS Applied Materials & Interfaces
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-10-22
- Volume
- 5
- Issue
- 21
- Pages
- 10597-10604
- ISSN
- 1944-8244 (ISSN-L)
- 1944-8252
- OpenAlex
- W2120911860
- Cited by
- 403
- References
- 2
- FWCI
- 11.83
- 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.