Machine learning to predict gold nanostar optical properties

Nanoscale Advances, vol. 7, pp. 4117–4128

Abstract

seedless protocols with Good's buffers, which are beneficial because of their simplicity and the use of biocompatible reagents. However, AuNS growth and optical properties are affected by various experimental factors during their seedless synthesis, which affects their performance in diagnosis and therapy. In this study, we develop a workflow based on machine learning models to predict AuNS optical properties. This approach includes data collection, feature selection, data generation, and model selection, resulting in predictions of the first and second localized surface plasmon resonance positions within 9 and 15% of their true values (root-mean-squared percentage error), respectively. Our results highlight the benefits of using machine learning models to infer the optical properties of AuNS from their synthesis conditions, potentially improving nanoparticle design and production for better disease diagnosis and therapy.

Authors 8

1. Peiying Wu Aachen Institute for Experimental Molecular Imaging

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital, Aachen 52074, Germany

2. Rui Zhang Aachen Institute for Experimental Molecular Imaging

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital, Aachen 52074, Germany

3. Céline Porte Aachen Institute for Experimental Molecular Imaging

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital, Aachen 52074, Germany

4. Fabian M. Kiessling Aachen Institute for Experimental Molecular Imaging

   Universitätsklinikum Aachen · Fraunhofer Institute for Digital Medicine · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Institute for Digital Medicine MEVIS, Bremen 28359, Germany

   Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital, Aachen 52074, Germany

5. Twan Lammers Aachen Institute for Experimental Molecular Imaging

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital, Aachen 52074, Germany

6. Sima Rezvantalab corresponding

   Urmia University of Technology

   Affiliation as printed

   Chemical Engineering Department, Urmia University of Technology, Urmia 57166-419, Iran

7. Sara Mihandoost corresponding

   Urmia University of Technology

   Affiliation as printed

   Electrical Engineering Department, Urmia University of Technology, Urmia 57166-419, Iran

8. Roger M. Pallares corresponding Aachen Institute for Experimental Molecular Imaging

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital, Aachen 52074, Germany