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標題: | 基於CO紅外光譜與深度學習研究二氧化鈰催化劑的表面特性 Surface Characterization of Cerium Oxide Catalysts using Deep Learning with Infrared Spectroscopy of CO |
作者: | 余欣諭 Hsin-Yu Yu |
指導教授: | 李奕霈 Yi-Pei Li |
關鍵字: | CO衍生物,CeO2表面,吸附能,紅外光譜,深度神經網絡, CO-derived adspecies,CeO2 facets,Adsorption energy,Infrared spectroscopy,Deep neural network, |
出版年 : | 2023 |
學位: | 碩士 |
摘要: | 材料表面的特性表徵對於理解其性質與行為至關重要。本研究利用CO作為探測分子的紅外光譜,結合深度學習技術,探索二氧化鈰催化劑的表面特性。通過對不同CeO2表面CO衍生物的密度泛函理論 (Density Functional Theory) 系統性研究,獲得了包含CO在CeO2表面的振動頻率、強度和吸附能等完整數據集。這些數據集被用來合成大量的複雜紅外光譜,用於訓練深度學習模型,以預測表面結構,包括CeO2表面的分布、CO衍生物和結合能的分佈。這些模型成功地分析了CO在不同類型CeO2表面吸附的實驗紅外光譜,大多數情況下預測與實驗觀察結果一致。本研究提供了一種機器學習方法,以理解多種CeO2材料的形態、局部環境排列、探測分子的交互作用行為和催化特性。 Characterization of material surfaces is crucial for understanding their properties and behavior. In this work, we utilized a deep learning technique, along with infrared (IR) spectrum of CO as a probe molecule, to explore the surface properties of cerium oxide (CeO2) catalysts. Through systematic density functional theory investigation of CO-derived adspecies on various CeO2 facets, we obtained an extensive dataset containing vibrational frequencies, intensities, and adsorption energies of CO on CeO2. This dataset was used to synthesize large quantities of complex IR spectra to train deep learning models for predicting surface structures, including the distribution of CeO2 facets, CO-derived adspecies, and binding energies. These models were successful in analyzing experimental IR spectra of CO adsorbed on different types of CeO2, and their predictions were consistent with experimental observations in most cases. This work provides a machine learning approach in understanding the morphology, local environmental arrangement, interaction behavior of probe molecules, and catalytic characteristics of diverse CeO2 materials. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88475 |
DOI: | 10.6342/NTU202302105 |
全文授權: | 同意授權(全球公開) |
顯示於系所單位: | 化學工程學系 |
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