Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162377
Title: Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution
Authors: Tan, Emily Xi
Chen, Yichao
Lee, Yih Hong
Leong, Yong Xiang
Leong, Shi Xuan
Stanley, Chelsea Violita
Pun, Chi Seng
Ling, Xing Yi
Keywords: Science::Chemistry
Issue Date: 2022
Source: Tan, E. X., Chen, Y., Lee, Y. H., Leong, Y. X., Leong, S. X., Stanley, C. V., Pun, C. S. & Ling, X. Y. (2022). Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution. Nanoscale Horizons, 7(6), 626-633. https://dx.doi.org/10.1039/d2nh00146b
Project: RG97/19
NGF-2019-07-009
A20E5c0082 
NRF2020NRF-CG001-010
Journal: Nanoscale Horizons
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.
URI: https://hdl.handle.net/10356/162377
ISSN: 2055-6764
DOI: 10.1039/d2nh00146b
Rights: © 2022 The Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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