| Summary: | The effect of surface plasmon resonance (SPR) from noble metal nanostructures such as gold nanoparticles (Au NPs) has been proposed to promote the generation of energetic hot electrons as well as boosting resonant energy transfer, thereby resulting in significantly enhancing solar-light harvesting and energy conversion efficiency. Herein, Au NPs decorated zinc oxide nanorods with plasmonic metal–semiconductor heterostructures have been synthesized through UV/Ozone treatment. Absorption, light-to-plasmon conversion efficiency, plasmon-to-hot electron conversion efficiency, and quality (Q)-factor of Au@ZnO nanocomposites are further characterized in order to understand the related SPR effect from various aspects. Simultaneously, the use of machine learning (ML) as an artificial intelligence data-driven method to derive an alternative predictive model for evaluating the relationship between synthesis and properties of materials has been adopted. In this regard, we collect only a limited supply of experimental dataset as training data to establish the predictive model with an artificial neural network incorporating genetic algorithm. According to the results from experimental datasets and the proposed predictive model, our analysis has revealed that the conversion efficiency and Q-factor associated with the SPR effect from Au@ZnO nanocomposites can be efficiently evaluated through ML, which has potential application in plasmon-sensitized solar cells and plasmonic lasers in the future.
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