Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 190

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Well-defined porous membranes for robust omniphobic surfaces via microfluidic emulsion templating

Open access
Zhu, Pingan, Kong, Tiantian, Tang, Xin, Wang, Liqiu·2017·Nature Communications

Abstract

Durability is a long-standing challenge in designing liquid-repellent surfaces. A high-performance omniphobic surface must robustly repel liquids, while maintaining mechanical/chemical stability. However, liquid repellency and mechanical durability are generally mutually exclusive properties for many omniphobic surfaces—improving one performance inevitably results in decreased performance in another. Here we report well-defined porous membranes for durable omniphobic surfaces inspired by the springtail cuticle. The omniphobicity is shown via an amphiphilic material micro-textured with re-entrant surface morphology; the mechanical durability arises from the interconnected microstructures. The innovative fabrication method—termed microfluidic emulsion templating—is facile, cost-effective, scalable and can precisely engineer the structural topographies. The robust omniphobic surface is expected to open up new avenues for diverse applications due to its mechanical and chemical robustness, transparency, reversible Cassie–Wenzel transition, transferability, flexibility and stretchability.

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 · 10.1038/ncomms15823

Cites

4 of 4 references verified0 citation errors · 0 not checkable

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

Cites

Show all 4 references

Cited by

See all 190

Metadata

Authors
Zhu, P., Kong, T., Tang, X., & Wang, L.
Venue
Nature Communications
Published
2017-06-12
Volume
8
Issue
1
Pages
15823-15823
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2626677198
Cited by
190
References
4
FWCI
8.66
Type
journal article
Language
en
Open access
gold

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: Well-defined porous membranes for robust omniphobic surfaces via microfluidic emulsion templating. https://app.cochat.ai/papers/10.1038/ncomms15823
The paper
Zhu, P., Kong, T., Tang, X., & Wang, L. (2017). Well-defined porous membranes for robust omniphobic surfaces via microfluidic emulsion templating. Nature Communications. https://doi.org/10.1038/ncomms15823

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.