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Determining accurate molecular sizes in solution through NMR diffusion spectroscopy

Macchioni, Alceo, Ciancaleoni, Gianluca, Zuccaccia, Cristiano, Zuccaccia, Daniele·2008·Chem. Soc. Rev.

Abstract

This tutorial review deals with the methodological procedures that can be used to obtain accurate molecular sizes in solution from diffusion NMR spectroscopy. The critical aspects associated with the estimation of the size of molecules from the measured translational self-diffusion coefficient, using the Stokes-Einstein equation, are highlighted and discussed. From a theoretical point of view, it is shown how to take into account the size of the solute with respect to that of the solvent and its non-spherical shape using the appropriate correction factors in the frictional coefficient. From a practical point of view, the advantages of introducing an internal standard in the sample are presented. Initially, non-aggregating systems are considered in an attempt to clarify what hydrodynamic dimensions mean. Successively, aggregating systems are addressed showing how it is possible to understand the aggregation level and derive the thermodynamic parameters for some illustrative aggregation processes.

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Metadata

Authors
Macchioni, A., Ciancaleoni, G., Zuccaccia, C., & Zuccaccia, D.
Venue
Chem. Soc. Rev.
Published
2008
Volume
37
Issue
3
Pages
479-489
ISSN
  • 0306-0012 (ISSN-L)
  • 1460-4744
OpenAlex
W2005692644
Cited by
591
References
19
FWCI
2.25
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Determining accurate molecular sizes in solution through NMR diffusion spectroscopy. https://app.cochat.ai/papers/10.1039/b615067p
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Macchioni, A., Ciancaleoni, G., Zuccaccia, C., & Zuccaccia, D. (2008). Determining accurate molecular sizes in solution through NMR diffusion spectroscopy. Chem. Soc. Rev. https://doi.org/10.1039/b615067p

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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.