Database of Optical Characteristics and Conversion Efficiency from
Urchin-like Gold Nanoparticle Coated on Zinc Oxide Nanoflower Heterogeneous Interface (海膽狀金奈米粒子披覆於奈米花狀氧化鋅異質介面之光學特性與轉換效率資料庫)
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 (RET),
thereby resulting in significantly enhancing of the solar-light harvesting and
energy-conversion efficiency. Herein Urchin-like Au NPs decorated zinc oxide
nanoflowers (Urchin-like An/ZnO nanoflower) 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 of urchin-like Au/ZnO nanocomposites are evaluated with experimental
parameters including time of UV/Ozone treatment (5-30 min) and concentration of
Hydroquinone (1000-3000 ul). 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 0.016, and the false rate is less than
0.01.
貴金屬奈米結構如金奈米顆粒（Au
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-半導體異質結構的海膽狀金奈米粒子修飾的奈米花狀氧化鋅（Au NPs/ZnO Nanoflowers）。在資料庫中，使用包括紫外線/臭氧時間處理（5-30 min）和Hydroquinone添加量（1000-3000 ul）的實驗參數評估Au/ZnO奈米複合材料的吸收峰位置、FWHM、光至電漿轉換效率。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類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0.016，誤率則小於0.01。