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Cited by 462

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Self-Assembly Combining Two Bioactive Peptide-Amphiphile Molecules into Nanofibers by Electrostatic Attraction

Niece, Krista L., Hartgerink, Jeffrey D., Donners, Jack J. J. M., Stupp, Samuel I.·2003·Journal of the American Chemical Society

Abstract

We describe a new approach to peptide-amphiphile (PA) nanofiber preparation that allows PAs with different bioactive amino acid sequences to be combined into a single fiber. Oppositely charged PAs are synthesized separately and then mixed to produce gels of nanofiber networks at physiological pH. Transmission electron microscopy reveals the formation of fibers approximately 7 nm in diameter and several micrometers long in these dimeric systems. On the basis of NMR and microscopy, we suggest that these nanofibers are cylindrical micelles of mixed composition, formed due to electrostatic attraction between the oppositely charged PAs. This strategy for self-assembly may be useful in cell therapies that can be implemented without invasive surgery or in in vitro tissue engineering.

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Metadata

Authors
Niece, K. L., Hartgerink, J. D., Donners, J. J. J. M., & Stupp, S. I.
Venue
Journal of the American Chemical Society
Published
2003-05-23
Volume
125
Issue
24
Pages
7146-7147
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2055124743
Cited by
462
References
10
FWCI
12.92
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Self-Assembly Combining Two Bioactive Peptide-Amphiphile Molecules into Nanofibers by Electrostatic Attraction. https://app.cochat.ai/papers/10.1021/ja028215r
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Niece, K. L., Hartgerink, J. D., Donners, J. J. J. M., & Stupp, S. I. (2003). Self-Assembly Combining Two Bioactive Peptide-Amphiphile Molecules into Nanofibers by Electrostatic Attraction. Journal of the American Chemical Society. https://doi.org/10.1021/ja028215r

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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.