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Application of Single-Layer Graphene as a Protective Coating on Single Crystal Copper (111) and Machine Learning Assisted Discovery of 2D Materials
註釋This thesis focuses on two different areas of materials science research. The first part explores the effectiveness of single-layer graphene (SLG) coating on single-crystal copper (Cu (111)) exposed to Desulfovibrio alaskensis G20 (DAG-20) sulfate-reducing bacteria known for its significant role in microbially induced corrosion (MIC). The study compares the corrosion rates of bare SC-Cu and single-layer graphene-coated SC-Cu (SLG/SC-Cu) using open circuit potential, electrochemical impedance, and polarization resistance measurements. The results show that SLG/SC-Cu exhibits higher open circuit potential, higher impedance, and lower corrosion rates compared to bare SC-Cu. The graphene coating acts as a barrier to corrosion, and the results demonstrate the potential of graphene-coated SC-Cu for developing advanced corrosion-resistant materials. The second chapter investigates the application of machine learning techniques to accelerate the discovery process of two-dimensional (2D) materials. The study explores a variety of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning, to identify promising 2D materials. The results demonstrate that machine learning can effectively screen and predict new 2D materials with desirable properties such as superior barrier property, high thermal conductivity, mechanical strength, and electronic conductivity. The study highlights the potential of machine learning in materials science research and its application to the discovery of 2D materials while also discussing the challenges and potential directions for future research. Overall, this thesis provides valuable insights into the development of advanced materials for a wide range of applications, from corrosion-resistant coatings to high-performance 2D materials.