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研究生: 廖之敖
Liao, Chih-Ao
論文名稱: 石墨負極表面氧化還原反應分析與電解液溶劑分子開發
Graphite anode surface redox reaction analysis and development of new electrolyte solvent molecules
指導教授: 許文東
Hsu, Wen-Dung
學位類別: 碩士
Master
系所名稱: 工學院 - 材料科學及工程學系
Department of Materials Science and Engineering
論文出版年: 2019
畢業學年度: 107
語文別: 中文
論文頁數: 97
中文關鍵詞: 鋰離子電池固態電解質介面第一原理機器學習
外文關鍵詞: Lithium ion battery, SEI, First principle, Machine learning
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  • 本研究針分成兩個部分做討論,第一部分以材料計算科學的方式針對石墨負極表面電解液分子氧化還原反應以及SEI膜生成機制進行探討,主要參考前緣分子軌域理論提出之電極材料的化學勢能(電子費米能階)相對於電解液分子電化學穩定視窗,可以近似性的預測電子轉移的發生。其中我們發現溶劑化在Li離子旁邊的EC分子與有相對於其他電解液更強烈的還原性,因而在實驗上可以觀察到不少EC分子還原分解的產物,因此本研究的第二部分以結合類神經網路模型結合之高通量分子改質演算法協助我們大量設計分子,篩選電化學穩定性上相對於EC更優秀的候補分子,並更一步計算候補分子的結構穩定性、偶極矩以及極化性。
    由於實際上的石墨負極表面有許多的缺陷以及各式不同官能機存在,非常難以在模擬上實現,因此本研究假設在完美單晶的條件下討論單一官能機條件下不同表面的模型。其中,本研究討論到純石墨、氫以及氫氧官能機的表面。本研究第一部分以DFT計算分析鋰離子電池充電初期負極表面費米能階在不同電荷密度下在不同表面下的能量變化,其中我們發現表面化學對於費米能階的變化有不小的影響,換句話說,藉由表面改質,可以改變表面SEI膜的生成。本研究第二部分利用類神經網路模型結合高通量分子改質演算法大量預測分子EA與IE並篩選出相對於EC更寬的電化學穩定視窗的新分子結構,分別以S、N、P、B、C、O原子作為基底的改質及預測,篩選出共4種分子,並以DFT計算篩選分子EA與IE後發現B為中心分子具有在電化學穩定性上優秀的特性,經後續計算發現該分子有取代DEC的潛力。

    By comparing graphite anode electron potential (Fermi level) and electrolytes’ electron affinity (EA), the tendency of electron transfer between electrode and electrolytes is revealed. The transfer has long been considered as the key mechanism to initiate SEI formation at the early stage of batteries charging. There is difficulty to compare the two energy levels due to different reference energy by different computational tools. The background charge, however, artificially change vacuum electrostatic potential resulting in incorrect work function, so as misalignment of fermi levels. To solve it, the fixed density of state assumption was adopted to estimate the Fermi level shifting due to add or extract additional charges. We calculated graphite armchair and zigzag surfaces with three conditions, pristine, hydrogen terminated and hydroxide terminated surfaces. DOS and work function are calculated to compare each surfaces Fermi level as respect to vacuum level. We finally found that the surfaces with hydrogen and hydroxide terminated surfaces have higher Fermi level as respect to pristine ones.
    Eelectrochemical stability window also shows that lithium ion solvated EC is the most reactive molecule in electrolytes that receive electron. To improve it, high-throughput modification and EA/IE prediction algorism is used for searching alterative molecules. Result shows that B-centered with 3 methoxy functional groups molecule has potential to be the alterative molecule due to the broaden electrochemical stability window and stable structure during redox reaction. Moreover, with low dipole moment and polarizability, we consider that B_methoxy molecule has potential to replace DEC.

    摘要 I Abstract II 致謝 XXII 目錄 XXIII 表目錄 XXVI 圖目錄 XXVII 第一章 前言 1 第二章 文獻回顧 7 2.1 鋰離子電池文獻回顧 7 2.2 負極材料文獻回顧 9 2.3 石墨材料文獻回顧[25] 11 2.4 固態電解質界面文獻回顧[22, 29] 15 2.5 電解液模擬文獻回顧 19 第三章 基礎理論介紹 21 3.1 第一原理計算(First Principle) [36] 21 3.2 密度泛函(DFT)理論[36] 21 3.2.1 Hartree–Fock近似[39] 22 3.2.2 Hohenberg-Kohn及Kohn-Sham 近似[40, 41] 23 3.3 分子動力學(MD)理論[42, 43] 25 3.3.1 諾斯-胡佛恆溫法(Nosé–Hoover thermostat)[44] [45] 26 3.3.2 第一原理分子動力學模擬(ab-initio MD simulations)[46] [47] 27 3.4 週期性邊界條件(Periodic Boundary Condition , PBC) [48] 28 3.5 溶劑化效應(Solvent Effect) [49] 29 3.6 機器學習基礎理論 30 3.6.1 神經網路(Neuron Network , NN)基本架構 30 3.6.2 感知器 31 3.6.3 反向傳播算法(Backpropagation , BP) 33 第四章 物理模型與模擬設計 37 4.1 石墨負極材料模型 37 4.1.1 石墨負極塊材 37 4.1.2 石墨負極表面 38 4.2 電解液分子簡介 39 4.2.1 碳酸乙烯酯(Ethylene carbonate , EC) 39 4.2.2 碳酸二乙酯(Diethyl carbonate , DEC) 40 4.2.3 六氟磷酸鋰(Lithium hexafluorophosphate , LiPF6) 40 4.3 石墨負極模擬方法 41 4.4 電解液模擬方法 42 4.4.1 結構優化與能量計算 42 4.4.2 高通量電解液分子計算模組 43 4.5 充電初期電解液電化學穩定性分析 45 4.6 電解液分子性質機器學習模型 46 4.7 高通量分子修飾及預測模組 47 第五章 結果與討論 51 5.1 石墨負極 51 5.1.1 塊材與表面模型 51 5.1.2 官能機模型與生成能計算 60 5.1.3 分子動力學模擬 61 5.1.4 態密度及態密度積分 63 5.1.5 功函數計算 66 5.2 電解液電化學穩定視窗 69 5.3 石墨負極表面充電初期電化學穩定分析 71 5.4 電解液分子修飾與預測 72 5.4.1 電解液分子取代基修飾與預測性 76 5.4.2 電解液分子取代基修飾與篩選 80 第六章 結論 84 第七章 參考文獻 85 第八章 附錄 92 8.1 固態電解質介面成分與分析文獻整理 92 8.2 分子官能基資料庫 94

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