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The DOI matches its Crossref record; 357 of the 357 references it cites were found in a registry or an index.

Functional surface microstructures inspired by nature – From adhesion and wetting principles to sustainable new devices

Open access
Arzt, Eduard, Quan, Haocheng, McMeeking, Robert M., Hensel, René·2021·Progress in Materials Science

Abstract

In the course of evolution nature has arrived at startling materials solutions to ensure survival. Investigations into biological surfaces, ranging from plants, insects and geckos to aquatic animals, have inspired the design of intricate surface patterns to create useful functionalities. This paper reviews the fundamental interaction mechanisms of such micropatterns with liquids, solids, and soft matter such as skin for control of wetting, self-cleaning, anti-fouling, adhesion, skin adherence, and sensing. Compared to conventional chemical strategies, the paradigm of micropatterning enables solutions with superior resource efficiency and sustainability. Associated applications range from water management and robotics to future health monitoring devices. We finally provide an overview of the relevant patterning methods as an appendix.

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Metadata

Authors
Arzt, E., Quan, H., McMeeking, R. M., & Hensel, R.
Venue
Progress in Materials Science
Publisher
Elsevier BV
Published
2021-07
Volume
120
Pages
100823
ISSN
0079-6425 (ISSN-L)
OpenAlex
W4237303055
Cited by
253
References
357
FWCI
12.37
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Functional surface microstructures inspired by nature – From adhesion and wetting principles to sustainable new devices. https://app.cochat.ai/papers/10.1016/j.pmatsci.2021.100823
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Arzt, E., Quan, H., McMeeking, R. M., & Hensel, R. (2021). Functional surface microstructures inspired by nature – From adhesion and wetting principles to sustainable new devices. Progress in Materials Science. https://doi.org/10.1016/j.pmatsci.2021.100823

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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.