The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Biomimetic materials research: what can we really learn from nature's structural materials?
Open accessAbstract
Nature provides a wide range of materials with different functions and which may serve as a source of bio-inspiration for the materials scientist. The article takes the point of view that a successful translation of these ideas into the technical world requires more than the observation of nature. A thorough analysis of structure-function relations in natural tissues must precede the engineering of new bio-inspired materials. There are, indeed, many opportunities for lessons from the biological world: on growth and functional adaptation, about hierarchical structuring, on damage repair and self-healing. Biomimetic materials research is becoming a rapidly growing and enormously promising field. Serendipitous discovery from the observation of nature will be gradually replaced by a systematic approach involving the study of natural tissues in materials laboratories, the application of engineering principles to the further development of bio-inspired ideas and the generation of specific databases.
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Cites
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Prenucleation clusters and non-classical nucleation
Gebauer, D., & Cölfen, H. (2011) · Nano Today · doi:10.1016/j.nantod.2011.10.005 · Cited by 682
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Lu, L., Tian, T., Wu, S., Xiang, T., & Zhou, S. (2019) · Polymer Chemistry · doi:10.1039/c9py00015a · Cited by 103
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Metadata
- Authors
- Fratzl, P.
- Venue
- Journal of The Royal Society Interface
- Publisher
- The Royal Society
- Published
- 2007-03-06
- Volume
- 4
- Issue
- 15
- Pages
- 637-642
- ISSN
- 1742-5662 (ISSN-L)
- 1742-5689
- OpenAlex
- W2168464016
- Cited by
- 616
- References
- 52
- FWCI
- 10.12
- Type
- journal article
- Language
- en
- Open access
- green
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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.
