JOURNAL ARTICLE
New Bio-inspired Materials: When Biology Meets Chemistry, Physics, Engineering, and Design.
Published In: Proceedings of the American Philosophical Society, 2026, v. 167, n. 1. P. 106 1 of 3
Database: Art Source Ultimate 2 of 3
Authored By: AIZENBERG, JOANNA 3 of 3
Abstract
The article focuses on the development of new bio-inspired materials through the integration of biology, chemistry, physics, engineering, and design. It highlights how studying natural systems—such as the superhydrophobic lotus leaf, the slippery pitcher plant, and structural colors in butterflies and seeds—can inspire innovative materials with properties like self-cleaning, anti-icing, self-healing, and dynamic optical responses. A key innovation discussed is Slippery Liquid-Infused Porous Surfaces (SLIPS), which mimic pitcher plant surfaces to create omniphobic, self-healing coatings with applications in medical devices, marine antifouling, and water harvesting. The article also presents Hydrogel Actuated Integrated Responsive Structures (HAIRS), which enable materials to autonomously change properties in response to environmental stimuli, and explores dynamic optical materials inspired by organisms like the brittlestar and tulips for uses in sensing and encryption. Overall, it emphasizes multidisciplinary approaches and combining diverse biological strategies to design multifunctional, adaptive materials. [Extracted from the article]
Additional Information
- Source:Proceedings of the American Philosophical Society. 2026/03, Vol. 167, Issue 1, p106
- Document Type:Article
- Subject Area:Physics
- Publication Date:2026
- ISSN:0003-049X
- DOI:10.1353/pro.2026.a985702
- Accession Number:192675881
- Copyright Statement:Copyright of Proceedings of the American Philosophical Society is the property of University of Pennsylvania Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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