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Cited by 2,609

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Synthetic Molecular Motors and Mechanical Machines

Kay, Euan R., Leigh, David A., Zerbetto, Francesco·2007·Angewandte Chemie International Edition

Abstract

The widespread use of controlled molecular‐level motion in key natural processes suggests that great rewards could come from bridging the gap between the present generation of synthetic molecular systems, which by and large rely upon electronic and chemical effects to carry out their functions, and the machines of the macroscopic world, which utilize the synchronized movements of smaller parts to perform specific tasks. This is a scientific area of great contemporary interest and extraordinary recent growth, yet the notion of molecular‐level machines dates back to a time when the ideas surrounding the statistical nature of matter and the laws of thermodynamics were first being formulated. Here we outline the exciting successes in taming molecular‐level movement thus far, the underlying principles that all experimental designs must follow, and the early progress made towards utilizing synthetic molecular structures to perform tasks using mechanical motion. We also highlight some of the issues and challenges that still need to be overcome.

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Crossref · 10.1002/anie.200504313

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Metadata

Authors
Kay, E. R., Leigh, D. A., & Zerbetto, F.
Venue
Angewandte Chemie International Edition
Publisher
Wiley
Published
2006-12-12
Volume
46
Issue
1-2
Pages
72-191
ISSN
  • 1433-7851 (ISSN-L)
  • 1521-3773
OpenAlex
W2147440332
Cited by
2,609
References
9
FWCI
93.72
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Synthetic Molecular Motors and Mechanical Machines. https://app.cochat.ai/papers/10.1002/anie.200504313
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Kay, E. R., Leigh, D. A., & Zerbetto, F. (2007). Synthetic Molecular Motors and Mechanical Machines. Angewandte Chemie International Edition. https://doi.org/10.1002/anie.200504313

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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.