The DOI matches its Crossref record.
Unraveling the Mechanism of Nanotube Formation by Chiral Self-Assembly of Amphiphiles
Abstract
The self-assembly of nanotubes from chiral amphiphiles and peptide mimics is still poorly understood. Here, we present the first complete path to nanotubes by chiral self-assembly studied with C(12)-β(12) (N-α-lauryl-lysyl-aminolauryl-lysyl-amide), a molecule designed to have unique hybrid architecture. Using the technique of direct-imaging cryo-transmission electron microscopy (cryo-TEM), we show the time-evolution from micelles of C(12)-β(12) to closed nanotubes, passing through several types of one-dimensional (1-D) intermediates such as elongated fibrils, twisted ribbons, and coiled helical ribbons. Scattering and diffraction techniques confirm that the fundamental unit is a monolayer lamella of C(12)-β(12), with the hydrophobic tails in the gel state and β-sheet arrangement. The lamellae are held together by a combination of hydrophobic interactions, and two sets of hydrogen-bonding networks, supporting C(12)-β(12) monomers assembly into fibrils and associating fibrils into ribbons. We further show that neither the "growing width" model nor the "closing pitch" model accurately describe the process of nanotube formation, and both ribbon width and pitch grow with maturation. Additionally, our data exclusively indicate that twisted ribbons are the precursors for coiled ribbons, and the latter structures give rise to nanotubes, and we show chirality is a key requirement for nanotube formation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Novel ionic liquid-type Gemini surfactants: Synthesis, surface property and antimicrobial activity
Li, H., Yu, C., Chen, R., Li, J., & Li, J. (2012) · Colloids and Surfaces A: Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2011.12.014 · Cited by 342
Newcomb, C. J., Moyer, T. J., Lee, S. S., & Stupp, S. I. (2012) · Current Opinion in Colloid & Interface Science · doi:10.1016/j.cocis.2012.09.004 · Cited by 76
Metadata
- Authors
- Ziserman, L., Lee, H.-Y., Raghavan, S. R., Mor, A., & Danino, D.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-01-18
- Volume
- 133
- Issue
- 8
- Pages
- 2511-2517
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2084225184
- Cited by
- 258
- References
- 0
- FWCI
- 10.99
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.