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Cited by 273

The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.

Porous Organic Cage Nanocrystals by Solution Mixing

Hasell, Tom, Chong, Samantha Y., Jelfs, Kim E., Adams, Dave J., Cooper, Andrew I.·2012·Journal of the American Chemical Society

Abstract

We present here a simple method for the bottom-up fabrication of microporous organic particles with surface areas in the range 500-1000 m(2) g(-1). The method involves chiral recognition between prefabricated, intrinsically porous organic cage molecules that precipitate spontaneously upon mixing in solution. Fine control over particle size from 50 nm to 1 μm can be achieved by varying the mixing temperature or the rate of mixing. No surfactants or templates are required, and the resulting organic dispersions are stable for months. In this method, the covalent synthesis of the cage modules can be separated from their solution processing into particles because the modules can be dissolved in common solvents. This allows a "mix and match" approach to porous organic particles. The marked solubility change that occurs upon mixing cages with opposite chirality is rationalized by density functional theory calculations that suggest favorable intermolecular interactions for heterochiral cage pairings. The important contribution of molecular disorder to porosity and surface area is highlighted. In one case, a purposefully amorphized sample has more than twice the surface area of its crystalline analogue.

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Metadata

Authors
Hasell, T., Chong, S. Y., Jelfs, K. E., Adams, D. J., & Cooper, A. I.
Venue
Journal of the American Chemical Society
Published
2011-12-08
Volume
134
Issue
1
Pages
588-598
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2007421220
Cited by
273
References
73
FWCI
10.38
Type
journal article
Language
en
Open access
closed

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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.