Digital Database of Tuning Silver Nanoparticle on Zinc
Oxide Heterostructure for Photoelectric Reaction (調控成長奈米銀顆粒於氧化鋅異質結構光電反應之數位資料庫)
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。