The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Molecular Architectonic on Metal Surfaces
Abstract
The engineering of highly organized systems from instructed molecular building blocks opens up new vistas for the control of matter and the exploration of nanodevice concepts. Recent investigations demonstrate that well-defined surfaces provide versatile platforms for steering and monitoring the assembly of molecular nanoarchitectures in exquisite detail. This review delineates the principles of noncovalent synthesis on metal substrates under ultrahigh vacuum conditions and briefly assesses the pertaining terminology—self-assembly, self-organization, and self-organized growth. It presents exemplary scanning-tunneling-microscopy observations, providing atomistic insight into the self-assembly of organic clusters, chains, and superlattices, and the metal-directed assembly of low-dimensional coordination architectures. This review also describes hierarchic-assembly protocols leading to intricate multilevel order. Molecular architectonic on metal surfaces represents a versatile rationale to realize structurally complex nanosystems with specific shape, composition, and functional properties, which bear promise for technological applications.
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Cites
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Cited by
Symmetry Breaking in Self-Assembled Nanoassemblies
Sang, Y., & Liu, M. (2019) · Symmetry · doi:10.3390/sym11080950 · Cited by 35
“With the support of scanning tunnelling microscopy (STM), the chirality properties of 2D molecular organization can be clearly observed with high resolution under extreme conditions [30,68,69].”
Method
Metadata
- Authors
- Barth, J. V.
- Venue
- Annual Review of Physical Chemistry
- Publisher
- Annual Reviews
- Published
- 2007-05-01
- Volume
- 58
- Issue
- 1
- Pages
- 375-407
- ISSN
- 0066-426X (ISSN-L)
- 1545-1593
- OpenAlex
- W2147592303
- Cited by
- 1,049
- References
- 74
- FWCI
- 6.46
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
