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研究生： 邱創斌
Chiu, Chuang-Pin
論文名稱： 量子力學與分子動力分析酵素生物燃料電池性能影響因子
Quantum Mechanics and Molecular Dynamics Analysis of Influencing Factors on the Performance of Enzymatic Biofuel Cells
指導教授： 洪哲文
Hong, Che-Wun
口試委員:
學位類別： 博士
Doctor
系所名稱： 工學院 - 動力機械工程學系
Department of Power Mechanical Engineering
論文出版年： 2010
畢業學年度： 98
語文別： 英文
論文頁數： 90
中文關鍵詞： 酵素型生物燃料電池 、分子動力學 、密度泛函理論 、量子力學
外文關鍵詞： enzymatic biofuel cell, molecular dynamics simulation, density functional theory, quantum Mechanics
相關次數： 點閱：3  下載：0 
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    • An enzymatic biofuel cell (BioFC) is scientifically important because the biofuel can be the glucose solution in a human body and the catalyst uses some kind of organic enzymes instead of the traditional noble metal. Electricity is produced from the bio-electrochemical reaction in which the glucose is oxidized at the anode and oxygen molecules are reduced at the cathode. Products are only the gluconic acid and a water molecule in the overall reaction. Two major factors that influence the cell performance are identified. The first one is the proton (positive ion) diffusion rate in the electrolytic solution. The second one is the oxygen reduction rate at the cathode. This thesis investigates both the proton transport phenomenon near the anode and the catalytic mechanism of oxygen reduction at the cathode.
    In order to investigate the diffusion process of the protons in a nano-scale environment, molecular dynamics (MD) simulation techniques were employed. All the complex biological molecules involved in the transport were structured using a semi-empirical quantum mechanics (QM) method. Hydronium ions (a proton bond to a water molecule) are the major observation target to trace the trajectory. The diffusion coefficient and the ionic conductivity can be evaluated from the MD simulation results. The first conclusion can be drawn here that the greater the glucose concentration, the better the hydronium diffusivity. In the nano-scale environment, the enzyme promotes the production of protons, but it also plays an obstacle in the hydronium diffusion path.
    In order to improve the low diffusion mechanism and to increase the cell performance, an external magnetic field is applied to the simulation. The simulation model comprised an Au electrode, PQQ (pyrrolo quinoline quinine), FAD (flavin adenine dinucleotide), and glucose molecules with prescribed hydronium ions in the aqueous solution. A constant magnetic field is applied perpendicularly to the major direction of the diffusion path. It is found that the magnetic field strength is able to enhance the hydronium diffusivity in the solution and the rate of the biochemical reaction is increased. An example of simulation results reveals that the hydronium diffusivity can be increased from m2/s to a maximum m2/s at the glucose concentration 27 mM. The external magnetic field is an easy and feasible technique to improve the BioFC performance significantly.
    In the last part, a density functional theory (DFT) was introduced to investigate a simplified catalytic mechanism of the oxygen reduction at the cathode. The adsorption process of an oxygen molecule on the metal surface (of the enzymatic reaction center) is analyzed. The rate-limited first proton transfer is also evaluated. The catalytic process plays the key role for further improvement of the BioFC performance.

    Abstract Ⅰ Table of Content VI List of Figures IX List of Tables XII Nomenclature XIII Chapter 1 Introduction 1 1.1 Developmen of Biofuel cell 3 1.1.1 Microbial Fuel Cells and Enzymatic Biofuel Cells 3 1.1.2 The Enzymatic Biofuel Cell 6 1.2 Literature Survey 9 1.2.1 Enzymatic Biofuel Cells 9 1.2.2 Proton Diffusion in the Water 10 1.2.4 Oxygen Reduction Reaction and Density Function Theory 11 1.3 Motivation and Objectives 12 Chapter 2 Fundamental Theories of Molecular Dynamics and Computational Quantum Mechanics 14 2.1 Molecular Dynamics Simulation 14 2.1.1 Potential Functions and Interaction Forces 15 2.1.2 Numerical Methods 21 2.1.3 Mean Square Displacement, Diffusion Coefficient, Ionic Conductivity and Radial Distribution Function 22 2.2 Density Functional Theory 24 2.2.1 The Schrodinger Equation 24 2.2.2 The Hohenberg-Kohn Theorem 26 2.2.3 The Basic Kohn-Sham Equation 27 2.2.4 The Hartree-Fock Self-Consistent Field (SCF) Approximation 30 Chapter 3 Modeling and Simulation Results 33 3.1 Molecular Modeling of Hydronium Diffusivity in an Enzymatic Biofuel Cell 33 3.1.1 Molecular Models 35 3.1.2 Simulation Results 43 3.2 Magnetic Field Effect on the Hydronium Diffusivity at an Enzymatic Biofuel Cell Anode via Atomistic Analysis 51 3.2.1 Molecular Models 52 3.2.2 Simulation Results 54 3.3 Quantum Simulation of the Catalytic Mechanism of Oxygen Reduction at Cathode Using Density Functional Theory 61 3.3.1 Molecular model and computational method 63 3.3.2 Oxygn Adsorption 64 3.3.3 Scan of Total energy 68 3.3.4 First Proton Transfer 71 Chapter 4 Conclusion 79 4.1 Molecular Modeling of Hydronium Diffusion 75 4.2 Magnetic Field Effect on the Enzymatic Biofuel Cell Performance 76 4.3 Quantum Simulation of the Catalytic Mechanism 77 4.4 Future Work Suggestion 79 References 81 Appendix 89 LIST OF FIGURES Fig. 1.1 Working principle of general fuel cell 3 Fig. 1.2 The basic configuration of an enzymatic biofuel cell 7 Fig. 2.1 The interatomic bond vector 19 Fig. 2.2 The valence angle and bond vector 20 Fig. 2.3 The dihedral angle and associated bond vector 20 Fig. 2.4 The inversion angle and associated bond vector 22 Fig. 3.1 The process of constructing enzymatic anode 34 Fig. 3.2 Molecular structure of the example hydroxonium 36 Fig. 3.3 The chemical composition and molecular structure of the PQQ molecule. White balls represent the hydrogen atoms; deep blue balls represent the nitrogen atoms; light blue balls represent the carbon atoms; and red balls are the oxygen atoms respectively 38 Fig. 3.4 The chemical composition and molecular structure of the FAD molecule. Yellow balls represent the phosphorus atoms; the others are same as those indicated in Fig. 3 39 Fig. 3.5 The configuration of the system model at the anode. It consists of the PQQ, FAD molecules with an Au electrode in the glucose solution. The enzyme set is adhered to the Au electrode by a sulfur atom 39 Fig. 3.6 The initial molecular model of the simulation system with water molecules added according to the glucose concentration.. No. 1 and No. 2 hydroniums (represented by black-and-white balls to make them clearly) will be used to trace the trajectory as examples 40 Fig. 3.7 Variation of system energies, including the VDW (van der Waals) energy, the electrostatic energy, the configuration energy (intra-molecular potential) and the total energy during the molecular simulation. The simulation results start to converge at about 30 ps 44 Fig. 3.8 Motion pictures at (a) 4.5ps; (b) 14ps; (c) 30ps; and (d) 300ps, black-and-white balls represent the molecules. No. 1 is the one trapped in the enzyme, and No. 2 is free to travel 45 Fig. 3.9 The radial distribution function g(r) of - molecules 46 Fig. 3.10 The radial distribution function g(r) of - pairs 47 Fig. 3.11 The MSD diagram of at three glucose concentrations. The slope of each curve indicates the mobility of the ions. This figure shows that the higher the glucose concentration, the better the mobility of 49 Fig. 3.12 The trajectory of hydronium ions in the simulation system: green ones represent the lower activity hydronium (No. 1 in Figs 3.6 and 3.8), and red color ones indicate the higher activity ion (No. 2 in Figs 3.6 and 3.8) 50 Fig. 3.13 Initial molecular system model with water molecules 52 Fig. 3.14 Temperature variation record of the simulation system in the equilibrium stage for 300ps. The fluctuation is within 5% 55 Fig. 3.15 Snapshots of simulation result at the elapsed time of (a) 15 ps, (b) 30 ps, (c) 100 ps, and (d) 300 ps 56 Fig. 3.16 Radial distribution function (RDF) between hydroniums and water molecules 58 Fig. 3.17 Mean squared displacement (MSD) of the hydroniums under constant magnetic fields of B=0.92T and B=0 T 58 Fig. 3.18 Diffusion coefficient of hydronium ions as a function of magnetic flux density 60 Fig. 3.19 Copper centers of the laccase [40, 41] 62 Fig. 3.20 The optimized structure of oxygen adsorption on the two Cu atoms and Pt atoms 65 Fig. 3.21 The optimize geometry of oxygen adsorption on the Cu_9 (100), Cu_18 (100), Pt_9 (100), and Pt_18 (100) surfaces 67 Fig. 3.22 The process of how the oxygen molecule is adsorbed on two Cu atoms according to the scan of the total energy variation 69 Fig. 3.23 The scan of the total energy variation due to the adsorption of oxygen molecule on two Pt atoms 70 Fig. 3.24 The scan of the total energy variation due to the adsorption of oxygen molecule on the Cu_18 surface 70 Fig. 3.25 The scan of the total energy variation due to the adsorption of oxygen molecule on the Pt_18 surface 71 Fig. 3.26 The transfer processes of the first proton on the Cu_9 (100) and Cu_18 (100) surfaces 73 Fig. 3.27 The transfer processes of the first proton on the Pt_9 (100) and Pt_18 (100) surfaces 74

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