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Information Entropy as a Reliable Measure of Nanoparticle Dispersity

preprint
submitted on 08.02.2020 and posted on 10.02.2020 by Niamh Mac Fhionnlaoich, Stefan Guldin
Nanoparticle size impacts properties vital to applications ranging from drug delivery to diagnostics and catalysis. As such, evaluating nanoparticle size dispersity is of fundamental importance. Conventional approaches, such as standard deviation, usually require the nanoparticle population to follow a known distribution and are illequipped to deal with highly poly- or heterodisperse populations. Herein, we propose the use of information entropy as an alternative and assumption-free method for describing nanoparticle size distributions. This approach works equally well for mono-, poly- and heterodisperse populations and provides an unbiased route to evaluation and optimisation of nanoparticle synthesis. We provide an intuitive tool for analysis with a user-friendly macro and provide guidelines for interpretation with respect to known standards.

Funding

EPSRC (EP/M507970/1)

History

Email Address of Submitting Author

s.guldin@ucl.ac.uk

Institution

University College London

Country

United Kingdom

ORCID For Submitting Author

0000-0002-4413-5527

Declaration of Conflict of Interest

No conflict to declare

Version Notes

Version 1 pre-submission for peer review.

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