第一原理計算對於氮摻石墨烯在氧氣還原反應與拉曼增強的探討

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戴世宣(Shih-Hsuan Tai) 查詢紙本館藏

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化學工程與材料工程學系

論文名稱

第一原理計算對於氮摻石墨烯在氧氣還原反應與拉曼增強的探討
(Ab Initio Study of Oxygen Reduction Reaction & Raman Enhancement Potential of Nitrogen-Doped Graphene)

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摘要(中)

燃料電池可以直接將化學能轉化成高密度、高效率的電能，且燃料電池被認為是對環境友善的一種電池。燃料電池的陰極的主要反應是氧氣還原反應(oxygen reduction reaction, ORR)，因為氧氣還原反應在動力學上是一個速度較慢的反應，進而影響燃料電池的整體表現 [1]。傳統上，陰極是使用金屬材料像是鉑或是鉑的合金。近年來，更多的研究是應用非金屬材料像是奈米碳管(carbon nanotubes)及氮掺石墨烯(nitrogen-doped graphene, NG)在燃料電池的陽極上。石墨烯及其衍生物的電性對燃料電池的應用是很有幫助的。有研究指出，氮掺石墨烯及含缺陷碳材料可以有助於加快氧氣還原反應在燃料電池的陽極裡的表現 [2]。
拉曼光譜(Raman spectroscopy)被用來迅速且精準辨識分子的種類。然而，一般分子的非常小的拉曼散射截面(scattering-cross section)及其相對弱的訊號會造成拉曼光譜不好辨識分子。表面增強拉曼散射 (surface-enhanced Raman scattering, SERS)是一個對表面敏感的技術去偵測分子的拉曼訊號。表面增強拉曼散射的機制可以分為兩種：一種是電磁場增強機制(electromagnetic mechanism, EM)，另一種則是化學增強機制 (chemical enhancement mechanism, CM)。石墨烯增強拉曼散射(Graphene-enhanced Raman scattering, GERS)，是利用石墨烯作為基材，去增強拉曼訊號的一個方法，並且被認為是一個新的方法去研究化學增強機制。此外，實驗及模擬的研究中，都顯示出利用氮掺石墨烯為基材去探討表面增強拉曼訊號的效應，會比使用一般石墨烯為基材有更好的效果 [3-6]。
在本研究中，我們利用密度泛函理論(density functional theory, DFT)去探討氮掺石墨烯在兩個不同的領域的效果。在氧氣還原反應的部分，我們全面地探討四種不同的氮摻雜型態及五種模型：四及氮(quaternary nitrogen or graphitic nitrogen, NQ)、五環氮(pyrrolic nitrogen, N5)、六環氮(pyridinic-N, N6 and N6nH)、三個六環氮(three-pyridinic-N, 3N6)。我們建造並且呈現初始的五種模型，利用五種模型去模擬氧氣還原反應的各個步驟，並且算出每個步驟的自由能。計算出的自由能可以得出氧氣還原反應的自由能反應路徑能階圖，利用自由能反應路徑能階圖，可以得到哪一種氮摻雜型態是最適合當作氧氣還原反應的基材。所有的模型，除了五環氮是進行解離機構反應以外，其他的氮摻雜型態都是進行聯合機構反應。計算得到的自由能路徑顯示出五種氮摻雜型態對氧氣還原反應的反應性，排名由高至低是N6、NQ、N6nH、3N6、N5。計算得出的不同氮摻雜石墨烯的自旋密度及電荷密度的結果，更進一步輔助我們的結論。
在石墨烯增強拉曼散射的部分，我們探討石墨烯、氮掺石墨烯、羅丹明6G (Rhodamine 6G, R6G)的拉曼光譜及電性。我們系統性的描述不同種氮摻雜石墨烯與R6G的接觸。我們用模擬計算得出的R6G的拉曼光譜去對比R6G在氮掺石墨烯上的拉曼光譜，結果顯示拉曼訊號的增強因子(enhancement factor, EF)的範圍落在3至68倍。理論計算得出的態密度(density of state, DOS)可以讓我們得到最低未佔分子軌域減去費米能階的值(energy gap of LUMO-EF)，而綜觀比較五種氮掺石墨烯與R6G的態密度，R6G在NQ上有最低的能階，這也代表在石墨烯增強拉曼裡，散射NQ是最有潛力的氮型態。計算出來的拉曼光譜也顯示出NQ是在石墨烯增強拉曼散射中最好的氮型態。

摘要(英)

Fuel cells can directly convert chemical energy from a fuel into electricity with high power density, efficiency and in a more environmentally friendly fashion. The oxygen reduction reaction (ORR) is the main reaction on the cathode of fuel cells, and this reaction is limited by its kinetically slow reaction, which in turn decides the overall performance of fuel cells. Traditionally, metallic materials such as platinum and its alloys are used at the cathode. Recently, non-metallic materials such as carbon nanotubes and nitrogen-doped graphene (NG) have seen increased research in the field. Graphene and its derivatives are helpful for electrocatalytical application in fuel cells because of their electronic properties. There has been report that NG and carbon defects facilitate the oxygen reduction reaction (ORR) on the cathode in fuel cells.
Raman spectroscopy is used for quick, robust and precise molecular identification. However, the quite small cross-section of common molecules and rather weak signal. Surface-enhanced Raman scattering (SERS) is a surface-sensitive technique that enhances Raman signal of molecules. The SERS effects come from two major mechanisms: electromagnetic mechanism (EM) and chemical enhancement mechanism (CM). Graphene-enhanced Raman scattering (GERS), used graphene as substrate for Raman enhancement, is developing up a new way to study CM and reinforce the practical application of the SERS. In addition, NG on SERS effects has been investigated recently on both experimental and theoretical study which show better SERS effects than pristine graphene.
In this study, for the ORR section, we investigate the ORR reactivity of NG by using density functional theory (DFT), a computational quantum mechanical technique. Four doped sites and five models are comprehensively studied: quaternary nitrogen (NQ), pyrrolic nitrogen (N5), pyridinic nitrogen (N6, N6nH) and three-pyridinic nitrogen (3N6). Models for possible sites during each step of the oxygen reduction reaction were set up and visualized to provide a platform to calculate the free energy of ORR reaction pathway to determine the suitability of each doping scenario for ORR reaction. All models except N5 react in associative mechanisms and N5 react in dissociative mechanisms. The calculated free energy pathway demonstrated that the ranking of the reactivity of ORR reaction of different nitrogen configurations from high to low is N6, NQ, N6nH, 3N6, N5. Spin density and charge density aid in describing levels of reactivity.
For the GERS section, we investigate the Raman spectra and electronic properties of periodic and cluster pristine and nitrogen-doped graphene models, and the dye molecule R6G. We describe the interaction between R6G and a systematic series of nitrogen-doped graphene: quaternary (NQ), pyrrolic (N5), pyridinic (N6, N6nH) and three-pyridinic (3N6). Density of state (DOS) and work function are calculated to quantify the GERS mechanism. We compared the simulated Raman spectrum of both R6G and R6G on NG, and the result shows enhancement factor (EF) of 3-68 times. Results of density of state (DOS) has shown that R6G on NQ has the energy gap of LUMO-EF which indicate that NQ can have highest potential on GERS effects. Our calculated results of Raman spectra also demonstrated that NQ is the best candidate to the GERS effects.

關鍵字(中)

★ 氧氣還原反應
★ 拉曼增強
★ 密度泛函理論
★ 氮掺石墨烯
★ 羅丹明6G

關鍵字(英)

★ Oxygen reduction reaction (ORR)
★ Density functional theory (DFT)
★ Raman enhancement
★ Rhodamine 6G
★ Nitrogen-doped graphene

論文目次

Table of Content
摘要 I
Abstract IV
Acknowledgment VI
Table of Content VII
List of Figures IX
List of Tables XVI
Chapter 1 Background 1
1.1 Introduction 1
1.2 Literature Review 5
1.2.1 Nitrogen doping configurations on carbon materials 5
1.2.2 Oxygen reduction reaction (ORR) 6
1.2.3 Raman Spectroscopy 19
1.2.4 Surface-enhanced Raman scattering (SERS) 20
1.3 Motivation 26
Chapter 2 Theory 27
2.1 Density functional theory (DFT) 27
2.2 Bloch’s theorem 30
2.3 Self-consistent field (SCF) 30
2.4 Pseudopotential 31
2.5 Cutoff energy 33
2.6 K-point sampling 34
2.7 Monte Carlo methods 35
Chapter 3 Computational Details 37
3.1 Software 37
3.2 Modules 37
3.3 Free energy pathway of ORR 38
3.4 Models setup 40
3.4.1 Supercell models 40
3.4.2 Cluster models 43
3.4.3 Rhodamine 6G (R6G) model 46
3.5 Convergence testing 48
3.6 Computational details 53
Chapter 4 Results and Discussion 56
4.1 Oxygen reduction reaction (ORR) on nitrogen-doped graphene 56
4.1.1 Structure with geometrical optimization 56
4.1.2 Denotation of reaction pathway at each step 57
4.1.3 Calculation of free energy pathway 58
4.1.4 Discussion of the reaction activities of different nitrogen configurations 75
4.2 SERS effect of R6G interacting with nitrogen-doped graphene 79
4.2.1 DFT results of Raman enhancement 79
4.2.2 Results of Raman enhancement of different nitrogen configurations 89
4.2.3 Analysis of density of states (DOS) on different nitrogen-doped graphene 95
Chapter 5 Conclusions 100
Chapter 6 Future Work 101
References 102

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指導教授

張博凱(Bor Kae Chang)

審核日期

2018-7-12

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