Database of Surface Plasmon Resonance in Au NPs(金奈米粒子表面電漿共振資料庫)
Introduction

The effect of surface plasmon resonance (SPR) from noble metal nanostructures such as silver 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 Ag NPs decorated zinc oxide nanorods (ZnO NRs) with plasmonic metal-semiconductor heterostructures have been synthesized through UV/Ozone treatment. In this database, position of absorption peak, FWHM, light-to-plasmon conversion efficiency, light-to-electron conversion efficiency and quality factor (or Q factor) of Ag/ZnO nanocomposites are evaluated with experimental parameters including time of UV/Ozone treatment (1-90 min) and concentration of AgNO₃ (2*10^-4-10^-4 M). The database is established by data mining and machine learning through the strategy of combining artificial neural networks with genetic algorithm. The learning process is up to 1,000,000 times, the root mean square (RMS) error is less than 0.011, and the false rate is less than 0.001.

貴金屬奈米結構如銀奈米顆粒（Ag NPs）的表面電漿共振（SPR）的作用已被提出，以促進高能熱電子的產生及促進共振能量轉移（RET），從而導致大幅增強太陽光收集和能量轉換效率。本文通過紫外/臭氧處理合成了具有表面電漿金屬-半導體異質結構的銀奈米粒子修飾的氧化鋅奈米棒（ZnO NRs）。在資料庫中，使用包括紫外線/臭氧時間處理（1-90 min）和AgNO₃的濃度（2*10^-4-10^-4 M）的實驗參數評估Ag/ZnO奈米複合材料的吸收峰位置、FWHM、光至電漿轉換效率、光電轉換效率和質量因子（或Q因子）。資料庫中的數據是以資料探勘和機器學習方式，經結合基因演算與人工神經網絡的深度學習所建立。學習程序為1,000,000次，均方根（RMS）誤差小於0.011，誤率則小於0.001。