從貴金屬奈米結構如金奈米粒子引發表面電漿共振效應，用以促進高能熱電子的生成及共振能量轉移，從而顯著提高太陽光收集和能量轉換效率。本研究通過紫外光臭氧處理合成了具有貴金屬-半導體異質結構的金奈米粒子或銀奈米粒子修飾氧化鋅奈米柱。為了進一步從各個方面理解表面電漿共振效應與能量轉換效率，在此對Au @ ZnO及Ag@ ZnO奈米複合材料的吸收、光至電漿轉換效率、光電轉換效率和品質因子進行了表徵，由於表面電漿共振效應所產生的行為能通過調控實驗參數來影響結果，進而可以針對這些參數因素進行適當的調控，同時，採用了將機器學習法作為人工智能數據驅動方法的應用，建立用於評估材料的合成與性能之間關係的替代預測模組。在這方面，我們僅收集少量有限而足夠的實驗參數集及相對應的數據集作為訓練數據，並使用結合了人工神經網路與遺傳演算法來建立優化預測模組。根據實驗數據集和提出的預測模組的結果，藉由機器學習法可以有效評估Au @ ZnO及Ag@ ZnO奈米複合材料所引發的表面電漿共振效應與其相關的能量轉換效率，這可能對於電漿敏化太陽能電池及電漿雷射具有潛在的應用價值。

The effect of surface plasmon resonance (SPR) from noble metal nanostructures such as gold nanoparticles (Au NPs) and sliver nanoparticles (Ag NPs) has been proposed to promote the generation of energetic hot electrons as well as boosting resonant energy transfer (RET), thereby resulting in significantly enhancing of the solar-light harvesting and energy-conversion efficiency. Herein Au NPs decorated zinc oxide nanorods (ZnO NRs) and Ag NPs decorated ZnO NRs 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 and Ag@ZnO nanocomposites are further characterized in order to understand the related SPR effect and energy conversion efficiency from various aspects. The behavior generated from the effect of SPR can affect the results by adjusting the experimental parameters, and these parameters can also be appropriately adjusted. 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 data set as training data to establish the predictive model with an artificial neural network (ANN) incorporating genetic algorithm (GA). According to the results from experimental data sets and proposed predictive model, our analysis has revealed that the conversion efficiency and Q-factor associated with the SPR effect from Au@ZnO and Ag@ZnO nanocomposites can be efficiently evaluated through ML, which has potential application in plasmon-sensitized solar cell and plasmonic laser in the future.

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