The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Artificial lotus leaf structures from assembling carbon nanotubes and their applications in hydrophobic textiles
Abstract
Artificial lotus leaf structures were fabricated on cotton substrates via the controlled assembly of carbon nanotubes onto the surface of cotton substrates. Both pristine carbon nanotubes (CNTs) and surface modified carbon nanotubes (PBA-g-CNTs) were used as building blocks to biomimic the surface microstructures of lotus leaves at the nanoscale. Cotton fabrics, which otherwise have perfect water absorbabilities, have been endowed with super-hydrophobic properties—water contact angles greater than 150° were measured. The method provides a bionic route to create hydrophobic textiles. Furthermore, considering the novel mechanical and electric properties of carbon nanotubes, these carbon nanotubes coated cotton fabrics will find potential application in sensing, conducting and special textiles.
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Cited by
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Bio‐Inspired, Smart, Multiscale Interfacial Materials
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A review of stimuli-responsive polymers for smart textile applications
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Metadata
- Authors
- Liu, Y., Tang, J., Wang, R., Lu, H., Li, L., Kong, Y., Qi, K., & Xin, J. H.
- Venue
- J. Mater. Chem.
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2007
- Volume
- 17
- Issue
- 11
- Pages
- 1071-1078
- ISSN
- 0959-9428 (ISSN-L)
- 1364-5501
- OpenAlex
- W1976193236
- Cited by
- 273
- References
- 35
- FWCI
- 5.27
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, Y., Tang, J., Wang, R., Lu, H., Li, L., Kong, Y., Qi, K., & Xin, J. H. (2007). Artificial lotus leaf structures from assembling carbon nanotubes and their applications in hydrophobic textiles. J. Mater. Chem. https://doi.org/10.1039/b613914k
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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.
