The size of nanoparticles is crucial for their functioning in many applications, ranging from drug delivery to diagnostics and catalysis. Adequate size reporting is therefore of fundamental importance. Nevertheless, the most commonly used approaches, such as the measure of mean size and standard deviation, are based on the assumption that nanoparticle populations follow a normal distribution and, thus, are ill-equipped to deal with poly- or heterodisperse populations.
We have developed a novel, assumption-free tool to describe nanoparticle dispersity, which is based on the use of information entropy. This measure has already been successfully implemented in the study of species diversity, population genetics, molecular analysis and finance. It works equally well for mono-, poly-, and heterodisperse populations and represents an unbiased route to evaluation and optimization of nanoparticle synthesis targeted toward achieving size uniformity. As an accurate and un-biased descriptor of dispersity, nanoparticle entropy will further facilitate the use of advanced statistical tools, including design of experiment and machine learning.
Our work includes the release of free intuitive software tools for analysis (based on either an Excel Macro or a MATLAB GUI) and we further supply guidelines for interpretation with respect to known standards.
Article (Gold Open Access):
Information entropy as a reliable measure of nanoparticle dispersity
Niamh Mac Fhionnlaoich and Stefan Guldin
Chemistry of Materials, DOI: 10.1021/acs.chemmater.0c00539, 2020.
Software Repository (Open Source):