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

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

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...

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Bibliographic Details
Main Authors: Tan, Emily Xi, Chen, Yichao, Lee, Yih Hong, Leong, Yong Xiang, Leong, Shi Xuan, Stanley, Chelsea Violita, Pun, Chi Seng, Ling, Xing Yi
Other Authors: School of Physical and Mathematical Sciences
Format: Journal Article
Language:English
Published: 2022
Subjects:
Science::Chemistry
Nanoparticle Sizes
Plasmonics
Online Access:https://hdl.handle.net/10356/162377

Internet

https://hdl.handle.net/10356/162377

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