The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Rigid-Rod Molecules in Biomembrane Models: From Hydrogen-Bonded Chains to Synthetic Multifunctional Pores
Open accessAbstract
Synthetic ion channels and pores formed by rigid-rod molecules are summarized. This includes work on hydrogen-bonded chains installed along membrane-spanning rigid-rod scaffolds to transport protons. As a second topic, programmed assembly of p-septiphenyls with terminal iminodiacetate-copper complexes for potassium transport by cation-pi interactions is described. The third topic concerns rigid push-pull rods as fluorescent alpha-helix mimics to probe the importance of dipole-potential interactions for voltage gating, both on the functional and the structural level. Topic number four deals with p-octiphenyl staves as key scaffolds for the synthesis of rigid-rod beta-barrel pores. The description of internal and external design strategies for these rigid-rod beta-barrels covers a rich collection of pH-, pM-, voltage-, ligand-, and enzyme-gated synthetic multifunctional pores that can act as hosts, sensors, and catalysts. As far as practical applications are concerned, the possibility to detect chemical reactions with synthetic multifunctional pores appears most attractive. Recent molecular mechanics simulations are presented as a valuable approach to insights on the elusive suprastructures of multifunctional pores made from rigid rods.
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
Cites
Classical conditioning and brain systems: the role of awareness
Clark, R. E., & Squire, L. R. (1998) · Science · doi:10.1126/science.280.5360.77 · Cited by 1,138
Cited by
Yashima, E., Ousaka, N., Taura, D., Shimomura, K., Ikai, T., & Maeda, K. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00354 · Cited by 2,004
Anion Transport with Chalcogen Bonds
Benz, S., Macchione, M., Verolet, Q., Mareda, J., Sakai, N., & Matile, S. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b05779 · Cited by 297
Self-citation
Metadata
- Authors
- Sakai, N., Mareda, J., & Matile, S.
- Venue
- Accounts of Chemical Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2005-01-25
- Volume
- 38
- Issue
- 2
- Pages
- 79-87
- ISSN
- 0001-4842 (ISSN-L)
- 1520-4898
- OpenAlex
- W2125141220
- Cited by
- 244
- References
- 1
- FWCI
- 23.61
- Type
- journal article
- Language
- en
- Open access
- green
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.