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Higher‐Order Organization by Mesoscale Self‐Assembly and Transformation of Hybrid Nanostructures

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Cölfen, Helmut, Mann, Stephen·2003·Angewandte Chemie International Edition

Abstract

The organization of nanostructures across extended length scales is a key challenge in the design of integrated materials with advanced functions. Current approaches tend to be based on physical methods, such as patterning, rather than the spontaneous chemical assembly and transformation of building blocks across multiple length scales. It should be possible to develop a chemistry of organized matter based on emergent processes in which time‐ and scale‐dependent coupling of interactive components generate higher‐order architectures with embedded structure. Herein we highlight how the interplay between aggregation and crystallization can give rise to mesoscale self‐assembly and cooperative transformation and reorganization of hybrid inorganic–organic building blocks to produce single‐crystal mosaics, nanoparticle arrays, and emergent nanostructures with complex form and hierarchy. We propose that similar mesoscale processes are also relevant to models of matrix‐mediated nucleation in biomineralization.

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Metadata

Authors
Cölfen, H., & Mann, S.
Venue
Angewandte Chemie International Edition
Publisher
Wiley
Published
2003-05-28
Volume
42
Issue
21
Pages
2350-2365
ISSN
  • 1433-7851 (ISSN-L)
  • 1521-3773
OpenAlex
W2162475282
Cited by
1,816
References
3
FWCI
47.44
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Higher‐Order Organization by Mesoscale Self‐Assembly and Transformation of Hybrid Nanostructures. https://app.cochat.ai/papers/10.1002/anie.200200562
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Cölfen, H., & Mann, S. (2003). Higher‐Order Organization by Mesoscale Self‐Assembly and Transformation of Hybrid Nanostructures. Angewandte Chemie International Edition. https://doi.org/10.1002/anie.200200562

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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.