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研究生: 陳傳仁
Chen, Chuan-Ren
論文名稱: 以機械學習尋找影響電雙層電容碳材料電容值的關鍵表徵及一全新可有效增加孔洞碳材料之平均孔徑的合成方法
Use of machine learning to identify key factors in performance of carbon materials in electrical double-layer capacitance and new synthesis method for porous carbon materials with increased pore size
指導教授: 林弘萍
Lin, Hong-Ping
學位類別: 碩士
Master
系所名稱: 理學院 - 化學系
Department of Chemistry
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 75
中文關鍵詞: 機械學習電雙層電容ANNXGBoost多重孔洞碳材模板沉降法
外文關鍵詞: Mechanical learning, electric double-layer capacitor, ANN, XGBoost, multipore carbon material, precipitate
相關次數: 點閱:253下載:38
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  • 本研究透過收集 SCI 期刊上的文獻,共收集了 340 筆數據,並以此作為資料庫,且分別使用機械學習中的人工神經網路與極限梯度提升演算法進行預測模型之建立,藉由調整演算法之參數,以獲得最佳預測結果。並對兩種演算法進行評估,比較兩種演算法的效率與預測成效。最後以最佳之預測結果,判讀碳材料的各項表徵中,哪些對於電雙層電容器比電容值有關鍵性的影響,以供後續研究人員一研究方向。

    此外,本研究也提供一個新的多重孔洞碳材的合成方法,該方法透過吸附鈣離子酸性廢液,再透過調整 pH 值後,可使鈣離子化合物沉降於碳材料的奈米孔洞中,達到活化後擴孔的目的。相較於傳統的物理混合法,模板沉降法可使平均孔徑增加 1.72 倍,應用於電雙層電容器中,可使比電容值從 83.02 F/g 提升至 137.65 F/g (掃描速率皆為 5 mV/s),而在增加掃描速率後(500 mV/s),比電容值的保留率也從 48.3% 提升至 61.5%。總結來說,模板沉降法在提升電雙層電容器性能的同時,也回收了廢液中的鈣離子,因此比起傳統物理混合法更具發展前景。

    In the last decade, the market for smart handheld devices and electric vehicles has grown exponentially, and has driven the need for new high-performance energy storage devices accordingly. In addition to lithium-ion batteries (LIBs) and fuel cells, electric double-layer capacitors (EDLCs) have gained significant interest in the academic and industrial fields. However, the critical factors which govern the performance of EDLCs are still not clear. Accordingly, the present study used two machine learning techniques, namely an artificial neural network (ANN) and an Extreme Gradient Boosting (XGBoost) algorithm, to identify the key factors which determine the capacitance value of an EDLC. The results showed that the capacitance is determined mainly by the voltage window, and the specific surface area, total pore volume, average pore size and elemental oxygen content of the carbon electrode material, respectively. Moreover, the prediction performance of the trained XGBoost model was shown to be around 77%. The electrolytes used in commercial EDLCs are generally organic systems, and hence have an ion radius much larger than that of traditional water-based electrolytes. Accordingly, the present study also developed a new synthesis method for preparing carbon materials with an increased pore size for EDLC applications. The experimental results showed that the proposed method increased the pore size by around 1.72 times compared to that achieved by a traditional synthesis method.

    第一章 緒論 1 1.1 前言 1 1.2 實驗緣起 1 1.3 實驗方法及目的 2 第二章 理論說明 3 2.1 機械學習(Machine Learning, ML) 3 2.2 人工神經網路(Artificial neural network, ANN)演算法12 4 2.3 極限梯度提升(eXtreme Gradient Boosting, XGBoost)演算法 6 2.4 電容器 10 2.4.1 無極式電容 10 2.4.2 電解電容 12 2.4.3 電雙層電容 12 2.5 孔洞材材 14 第三章 實驗步驟與材料鑑定 15 3.1 化學藥品 15 3.2 使用物理混合法合成多重孔洞碳材 16 3.3 利用回收 Ca2+ 廢液合成多重孔洞碳材 16 3.4 夾具式電雙層電容之製備 17 3.4.1 電極片的準備 17 3.4.2 夾具式超級電容碳電極製備 18 3.4.3 夾具式超級電容組裝 18 3.5 對稱式電雙層電容的檢測 19 3.5.1 循環伏安法(Cyclic Voltammetry, CV) 19 3.5.2 電化學阻抗譜(Electrochemical Impedance Spectroscopy, EIS) 21 3.5.3 恆定電流充放電(Galvanostatic charge and discharge) 24 3.6 儀器設備 26 3.6.1 比表面積及孔隙分析儀(Surface Area and Porosity Analyzer) 26 3.6.2 拉曼光譜儀(Raman Spectrometer) 34 3.6.3 元素分析儀(Elemental Analyzer) 35 3.6.4 熱重分析儀(Thermogravimetric Analyzer) 35 3.6.5 電子顯微鏡(Electron microscope) 36 第四章 機械學習預測之結果 38 4.1 資料挑選 38 4.2 資料前處理 38 4.3 參數設定 40 4.4 預測結果 41 第五章 多重孔洞碳材料之合成 48 5.1 以物理混合法合成多重孔洞碳材 48 5.2 以模板沉降法合成多重孔洞碳材 51 5.3 電容行為之測試 55 第六章 結論 60 參考文獻 61

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