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Creating 3D Nanoparticle Structural Space via Data Augmentation to Bidirectionally Predict Nanoparticle Mixture's
Emily Xi Tan1, Jingxiang Tang2, Yong Xiang Leong1
1Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Singapore, 637371, Singapore.
Angewandte Chemie (International Ed. in English)
|February 15, 2024
Summary
We developed a 3D nanoparticle structural space to rapidly determine nanoparticle size, shape, and purity from extinction spectra. This method overcomes limitations of electron microscopy for nanoparticle characterization.
Area of Science:
- Materials Science
- Nanotechnology
- Spectroscopy
Background:
- Nanoparticle (NP) characterization is crucial for understanding their properties and applications.
- Current methods like electron microscopy are time-consuming and labor-intensive.
- Accurate characterization of NP mixtures with diverse shapes and sizes remains a challenge.
Purpose of the Study:
- To develop a rapid and accurate method for concurrent determination of NP purity, size, and shape.
- To establish a predictive model using extinction spectra.
- To create a comprehensive 3D nanoparticle structural space.
Main Methods:
- Utilized plasmonically-driven feature enrichment to extract localized surface plasmon resonance attributes.
- Developed a lasso regressor (LR) model to predict structural parameters from spectra.
- Employed artificial data augmentation to generate a large dataset of extinction spectra.
Main Results:
- Achieved low predictive errors (2.7-7.9%) for purity, size, and shape of silver nanocube mixtures.
- Created a 3D NP structural space capable of bidirectional prediction with <4% error.
- Identified higher-order electric and magnetic dipole contributions as key predictors.
Conclusions:
- The developed 3D NP structural space offers a rapid alternative to electron microscopy for NP characterization.
- Data augmentation strategies are vital for overcoming data scarcity in NP analysis.
- This approach can facilitate on-demand, autonomous synthesis-characterization platforms for nanoparticles.
