Machine Learning-Based Interpretation of Optical Properties of Colloidal Gold with Convolutional Neural Networks
Journal article, 2024
Gold nanoparticles are used in a range of applications, but their properties depend on their shape, size, and polydispersity. A quick, easy, and accurate characterization of the particles is therefore of high importance, especially in flow synthesis settings where continuous monitoring of the characteristics is desired. Our hypothesis was that convolutional neural networks can be used to extract detailed information about structural parameters of gold nanoparticles from their UV-vis spectra, and we have shown that this is possible by predicting size distributions from in silico UV-vis spectra for colloidal gold with high accuracy. Here this was done for both spherical and rod-shaped gold nanoparticles. We also show that the addition of noise makes the prediction of diameter polydispersity more challenging, but the average diameter, and for rods also aspect ratio distribution, can be accurately predicted even with the highest evaluated level of noise. The model structure is promising and worthy of implementation to enable predictions beyond in silico generated spectra. The model, for instance, can find application in flow synthesis settings to create a machine learning-driven feedback loop for automated synthesis.
Author
Frida Bilén
Chalmers, Chemistry and Chemical Engineering, Applied Chemistry
Pernilla Ekborg-Tanner
Chalmers, Physics, Condensed Matter and Materials Theory
Antoine Balzano
Chalmers, Chemistry and Chemical Engineering, Applied Chemistry
Michaël Ughetto
AstraZeneca AB
Robson Rosa Da Silva
Chalmers, Chemistry and Chemical Engineering, Applied Chemistry
NanoScientifica Scandinavia AB
Hannes Schomaker
AutoSyn AB
Chalmers, Chemistry and Chemical Engineering, Applied Chemistry
Paul Erhart
Chalmers, Physics, Condensed Matter and Materials Theory
Kasper Moth-Poulsen
Catalan Institution for Research and Advanced Studies
Polytechnic University of Catalonia
Institute of Material Science of Barcelona (ICMAB)
Chalmers, Chemistry and Chemical Engineering, Applied Chemistry
Romain Bordes
Chalmers, Chemistry and Chemical Engineering, Applied Chemistry
Journal of Physical Chemistry C
1932-7447 (ISSN) 1932-7455 (eISSN)
Vol. 128 33 13909-13916Subject Categories
Nano Technology
Chemical Sciences
DOI
10.1021/acs.jpcc.4c02971