Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Pan, Fei and Wu, Chia-Chen and Chen, Yu-Lin and Kung, Po-Yen and Su, Yen-Hsun. (2022) Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance. Nanoscale, 14 (37). pp. 13532-13541.

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Official URL: https://edoc.unibas.ch/90029/

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Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed,; e.g.; , ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties.
Faculties and Departments:05 Faculty of Science > Departement Chemie
05 Faculty of Science > Departement Chemie > Chemie > Physikalische Chemie (Palivan)
UniBasel Contributors:Pan, Fei
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Royal Society of Chemistry
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:19 Oct 2022 08:45
Deposited On:19 Oct 2022 08:45

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