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On-line compositional measurements of AuAg aerosol nanoparticles generated by spark ablation using optical emission spectroscopy

Snellman, Markus LU ; Samuelsson, Per LU ; Eriksson, Axel LU orcid ; Li, Zhongshan LU and Deppert, Knut LU orcid (2022) In Journal of Aerosol Science 165.
Abstract

Spark ablation is an established technique for generating aerosol nanoparticles. Recent demonstrations of compositional tuning of bimetallic aerosols have led to a demand for on-line stoichiometry measurements. In this work, we present a simple, non-intrusive method to determine the composition of a binary AuAg nanoparticle aerosol on-line using the optical emission from the electrical discharges. Machine learning models based on the least absolute shrinkage and selection operator (LASSO) were trained on optical spectra datasets collected during aerosol generation and labelled with X-ray fluorescence spectroscopy (XRF) compositional measurements. Models trained for varying discharge energies demonstrated good predictability of... (More)

Spark ablation is an established technique for generating aerosol nanoparticles. Recent demonstrations of compositional tuning of bimetallic aerosols have led to a demand for on-line stoichiometry measurements. In this work, we present a simple, non-intrusive method to determine the composition of a binary AuAg nanoparticle aerosol on-line using the optical emission from the electrical discharges. Machine learning models based on the least absolute shrinkage and selection operator (LASSO) were trained on optical spectra datasets collected during aerosol generation and labelled with X-ray fluorescence spectroscopy (XRF) compositional measurements. Models trained for varying discharge energies demonstrated good predictability of nanoparticle stoichiometry with mean absolute errors <10 at. %. While the models utilized the emission spectra at different wavelengths in the predictions, a combined model using spectra from all discharge energies made accurate predictions of the AuAg nanoparticle composition, showing the method's robustness under variable synthesis conditions.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bimetallic nanoparticles, Machine learning, Optical diagnostics, Plasma spectroscopy, Spark ablation
in
Journal of Aerosol Science
volume
165
article number
106041
pages
11 pages
publisher
Elsevier
external identifiers
  • scopus:85133444603
ISSN
0021-8502
DOI
10.1016/j.jaerosci.2022.106041
project
Aerosol Synthesis and Characterization of Heterogeneous Bimetallic Nanoparticles
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2022 The Authors
id
bb328012-bda1-4600-8d8e-b1da9e509d93
date added to LUP
2022-07-29 09:33:37
date last changed
2024-02-18 02:03:25
@article{bb328012-bda1-4600-8d8e-b1da9e509d93,
  abstract     = {{<p>Spark ablation is an established technique for generating aerosol nanoparticles. Recent demonstrations of compositional tuning of bimetallic aerosols have led to a demand for on-line stoichiometry measurements. In this work, we present a simple, non-intrusive method to determine the composition of a binary AuAg nanoparticle aerosol on-line using the optical emission from the electrical discharges. Machine learning models based on the least absolute shrinkage and selection operator (LASSO) were trained on optical spectra datasets collected during aerosol generation and labelled with X-ray fluorescence spectroscopy (XRF) compositional measurements. Models trained for varying discharge energies demonstrated good predictability of nanoparticle stoichiometry with mean absolute errors &lt;10 at. %. While the models utilized the emission spectra at different wavelengths in the predictions, a combined model using spectra from all discharge energies made accurate predictions of the AuAg nanoparticle composition, showing the method's robustness under variable synthesis conditions.</p>}},
  author       = {{Snellman, Markus and Samuelsson, Per and Eriksson, Axel and Li, Zhongshan and Deppert, Knut}},
  issn         = {{0021-8502}},
  keywords     = {{Bimetallic nanoparticles; Machine learning; Optical diagnostics; Plasma spectroscopy; Spark ablation}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Aerosol Science}},
  title        = {{On-line compositional measurements of AuAg aerosol nanoparticles generated by spark ablation using optical emission spectroscopy}},
  url          = {{http://dx.doi.org/10.1016/j.jaerosci.2022.106041}},
  doi          = {{10.1016/j.jaerosci.2022.106041}},
  volume       = {{165}},
  year         = {{2022}},
}