Issue 6, 2022

Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution

Abstract

Determination of nanoparticle size and size distribution is important because these key parameters dictate nanomaterials’ properties and applications. Yet, it is only accomplishable using low-throughput electron microscopy. Herein, we incorporate plasmonic-domain-driven feature engineering with machine learning (ML) for accurate and bidirectional prediction of both parameters for complete characterization of nanoparticle ensembles. Using gold nanospheres as our model system, our ML approach achieves the lowest prediction errors of 2.3% and ±1.0 nm for ensemble size and size distribution respectively, which is 3–6 times lower than previously reported ML or Mie approaches. Knowledge elicitation from the plasmonic domain and concomitant translation into featurization allow us to mitigate noise and boost data interpretability. This enables us to overcome challenges arising from size anisotropy and small sample size limitations to achieve highly generalizable ML models. We further showcase inverse prediction capabilities, using size and size distribution as inputs to generate spectra with LSPRs that closely match experimental data. This work illustrates a ML-empowered total nanocharacterization strategy that is rapid (<30 s), versatile, and applicable over a wide size range of 200 nm.

Graphical abstract: Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution

Supplementary files

Article information

Article type
Communication
Submitted
22 mrt 2022
Accepted
27 apr 2022
First published
04 mei 2022
This article is Open Access
Creative Commons BY-NC license

Nanoscale Horiz., 2022,7, 626-633

Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution

E. X. Tan, Y. Chen, Y. H. Lee, Y. X. Leong, S. X. Leong, C. V. Stanley, C. S. Pun and X. Y. Ling, Nanoscale Horiz., 2022, 7, 626 DOI: 10.1039/D2NH00146B

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