The surface immobilization of molecular catalysts is attractive because it combines the benefits of homogeneous and heterogeneous catalysis. However, determining the surface coverage and distribution of a molecular catalyst on a solid support is often challenging, inhibiting our ability to design improved catalytic systems. Here, we demonstrate that the combination of scanning transmission electron microscopy (STEM) and image analysis of the individual positions of heavy atoms in transition metal complexes via a convolutional neural network (CNN) allows statistically robust determination of the surface coverage and distribution of immobilized molecular catalysts. These observations provide information about how changes in the functionalization conditions, attachment group, and structure of the molecular catalyst affect the surface coverage and distribution, providing insight into the chemical mechanism of surface immobilization. The method could be generally valuable for correlating the surface coverage and distribution to the activity, selectivity, and stability of a catalytic system.

中文翻译：

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HAADF-STEM 图像的统计分析，以确定固定化分子复合物的表面覆盖率和分布


分子催化剂的表面固定化很有吸引力，因为它结合了均相和非均相催化的优点。然而，确定分子催化剂在固体载体上的表面覆盖率和分布通常具有挑战性，这抑制了我们设计改进的催化系统的能力。在这里，我们证明了扫描透射电子显微镜 （STEM） 和通过卷积神经网络 （CNN） 对过渡金属配合物中重原子的各个位置进行图像分析的结合，可以在统计上稳健地确定固定化分子催化剂的表面覆盖率和分布。这些观察结果提供了有关分子催化剂的功能化条件、附着基团和结构的变化如何影响表面覆盖和分布的信息，从而为了解表面固定化的化学机制提供了见解。该方法通常对于将表面覆盖率和分布与催化系统的活性、选择性和稳定性相关联很有价值。