Creating 3D Nanoparticle Structural Space via Data Augmentation to Bidirectionally Predict Nanoparticle Mixture's Purity, Size, and Shape from Extinction Spectra

Nanoparticle (NP) characterization is essential because diverse shapes, sizes, and morphologies inevitably occur in as‐synthesized NP mixtures, profoundly impacting their properties and applications. Currently, the only technique to concurrently determine these structural parameters is electron micr...

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Bibliographic Details
Published in:Angewandte Chemie 2024-04, Vol.136 (14), p.n/a
Main Authors: Tan, Emily Xi, Tang, Jingxiang, Leong, Yong Xiang, Phang, In Yee, Lee, Yih Hong, Pun, Chi Seng, Ling, Xing Yi
Format: Article
Language:English
Subjects:
Data augmentation
data-driven knowledge discovery
Electric dipoles
Electron microscopy
Extinction
Extinction behavior
interpretable machine learning
Magnetic dipoles
Mixtures
nanocharacterization
Nanoparticles
Nanospheres
Nanotechnology
Nanowires
Parameters
Purity
Quadrupoles
Shape
Silver nanocubes
Spectra
Surface plasmon resonance
Synthesis
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Summary:Nanoparticle (NP) characterization is essential because diverse shapes, sizes, and morphologies inevitably occur in as‐synthesized NP mixtures, profoundly impacting their properties and applications. Currently, the only technique to concurrently determine these structural parameters is electron microscopy, but it is time‐intensive and tedious. Here, we create a three‐dimensional (3D) NP structural space to concurrently determine the purity, size, and shape of 1000 sets of as‐synthesized Ag nanocubes mixtures containing interfering nanospheres and nanowires from their extinction spectra, attaining low predictive errors at 2.7–7.9 %. We first use plasmonically‐driven feature enrichment to extract localized surface plasmon resonance attributes from spectra and establish a lasso regressor (LR) model to predict purity, size, and shape. Leveraging the learned LR, we artificially generate 425,592 augmented extinction spectra to overcome data scarcity and create a comprehensive NP structural space to bidirectionally predict extinction spectra from structural parameters with
ISSN:0044-8249
1521-3757
DOI:10.1002/ange.202317978