Hybrid-biotaxonomy-like machine learning enables an anticipated surface plasmon resonance of Au/Ag nanoparticles assembled on ZnO nanorods

Sustainable energy strategies, particularly alternatives to fossil fuels, e.g., solar-to-hydrogen production, are highly desired due to the energy crisis. Therefore, materials leading to hydrogen production by utilizing water and sunlight are extensively investigated, such as nanomaterials modified by gold nanoparticles (AuNPs) of different structures, which enable photoelectrochemical water splitting through light-to-plasmon resonance. However, light-to-plasmon resonance depends on the gold nanoparticles' properties. Therefore, an accurate projection model, which correlates the fabrication parameters and light-to-plasmon resonance, can facilitate the selection and the subsequent application of AuNPs. In this regard, we established a hybrid-biotaxonomy-like machine learning (ML) model based on genetic algorithm neural networks (GANN) to investigate the light-to-plasmon properties of a six-layer coating of noble metal nanoparticles (NMNPs) on ZnO nanorods. Meanwhile, we understood the plasmonic peak shift of every NMNP coating layer by exploiting the multivariate normal distribution method and the concept of phylogenetic nomenclature from evolutionary developmental biology.

Automated summary

To address the energy crisis, researchers extensively study materials that can produce hydrogen using water and sunlight, such as nanomaterials modified by different structures of gold nanoparticles for photoelectrochemical water splitting through light-to-plasmon resonance. However, the properties of gold nanoparticles play a crucial role in light-to-plasmon resonance. Therefore, an accurate projection model is established to correlate the fabrication parameters and light-to-plasmon resonance, facilitating the selection and application of gold nanoparticles.