金属表面二维离子水合层的微观结构和动力学性质研究，有助于理解电化学界面的双电层结构及其对反应效率的影响。本论文在课题组的钠离子水合团簇研究的基础上，将实验延伸到二维钾离子水合层的生长制备和结构表征，以期发现离子浓度的变化对二维钾离子水合层的结构和物性产生的影响。实验利用高分辨率的扫描隧道显微镜和qPlus型原子力显微镜观测到二维钾离子水合层的不同相，而且这些相的出现与水和钾的配比相关。通过与理论计算的模拟图像的比对分析获得了二维钾离子水合层的原子结构。

准确判断二维离子水合层晶胞结构中的原子或分子种类，以及对扫描探针显微镜针尖的质量好坏进行自动化判断和修复，是课题组在二维离子水合层表征分析中的两大需求。本论文利用机器学习方法和TensorFlow 神经网络库，针对二维钾离子水合层的结构和扫描探针的针尖状态两类问题分别设计了具有不同网络层结构的卷积神经网络模型。通过尝试多种机器学习的优化方法以及对网络模型层结构和参数的持续调试，在二维钾离子水合层的结构分类问题和扫描探针针尖状态的识别问题上均达到了100%的准确率。本论文的工作为进一步研究三维离子水合层以及未来实现扫描自动化控制和识别做出了初步的尝试。

The study of the microstructure and kinetic properties of two-dimensional (2D) ion hydrates on metal surface is helpful for understanding the double layer structure of the electrochemical interface and its effect on the reaction efficiency. Based on previous research work on 2D sodium ion hydrated clusters, this thesis extends the investigation to the growth and characterization of 2D potassium ion hydrates and aims to find out how different hydration numbers influence the structure and physical properties of 2D potassium hydrates. In this study, different phases of 2D potassium hydrates are observed by using STM and qPlus AFM. The different phases are found to be related to the water to potassium ratio. The actual structures of different phases of 2D potassium hydrates are identified by compared with the simulated results.

Accurate determination of the atom or molecule types in the 2D ion hydrate crystal cell structure and automatic detection of SPM tip quality are two major requirements of our research group in 2D ion hydrates characterization. In this study, Convolutional Neural Networks are created by using the TensorFlow libraries. Training models with different numbers of layers are designed for 2D potassium hydrates structure classification and probe tip status recognition, respectively. With various optimization methods and longtime tuning of the network parameters, the accuracies of both structure classification and probe tip status recognition reach 100% finally. This work can be seen as a preliminary step for further exploration on 3D ion hydrates structure recognition and for realization of SPM scan automation.

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