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A methodology for characterising nanoparticle size and shape using nanopores
1Department of Chemistry, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK. m.platt@lboro.ac.uk.
Nanoscale
|December 10, 2019
Summary
This study introduces a new method combining resistive pulse sensing with logistic regression models (RPS-LRM) for rapid nanomaterial characterization. RPS-LRM accurately distinguishes and quantifies mixtures of nanorods and nanospheres in solution.
Area of Science:
- Nanotechnology
- Materials Science
- Analytical Chemistry
Background:
- Nanomaterial characterization is complex, requiring multiple technologies to assess size, shape, concentration, and surface charge.
- Existing methods struggle to analyze complex mixtures of different nanomaterial types simultaneously.
Purpose of the Study:
- To develop a rapid, single-platform method for characterizing mixtures of nanorods and nanospheres.
- To accurately determine size, aspect ratio, shape, and concentration of nanomaterials in solution.
Main Methods:
- Combined resistive pulse sensing (RPS) with predictive logistic regression models (LRM), creating the RPS-LRM methodology.
- Applied RPS-LRM to analyze mixtures of nanorods and nanospheres across various sizes and aspect ratios.
Main Results:
- RPS-LRM successfully characterized nanomaterials over a wide size range and varying aspect ratios.
- The method distinguished nanorods from nanospheres with aspect ratios greater than two.
- Accurate classification rates of 91% for nanospheres and 72% for nanorods were achieved, even with low nanorod fractions (20%).
Conclusions:
- RPS-LRM offers a rapid and effective solution for analyzing complex nanomaterial mixtures.
- This methodology has potential applications in classifying nanomedicines, novel nanomaterials, and biological analytes.
