Eliasson Henrik, Lothian Angus, Surin Ivan, Mitchell Sharon, Pérez-Ramírez Javier, Erni Rolf
Electron Microscopy Center, Empa - Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, Dübendorf, 8600, Switzerland.
Computer Vision Laboratory, Department of Electrical Engineering, Linköping University, Linköping, 581 83, Sweden.
Small Methods. 2025 Mar;9(3):e2401108. doi: 10.1002/smtd.202401108. Epub 2024 Oct 2.
Transmission electron microscopy (TEM) plays a crucial role in heterogeneous catalysis for assessing the size distribution of supported metal nanoparticles. Typically, nanoparticle size is quantified by measuring the diameter under the assumption of spherical geometry, a simplification that limits the precision needed for advancing synthesis-structure-performance relationships. Currently, there is a lack of techniques that can reliably extract more meaningful information from atomically resolved TEM images, like nuclearity or geometry. Here, cycle-consistent generative adversarial networks (CycleGANs) are explored to bridge experimental and simulated images, directly linking experimental observations with information from their underlying atomic structure. Using the versatile Pt/CeO (Pt particles centered ≈2 nm) catalyst synthesized by impregnation, large datasets of experimental scanning transmission electron micrographs and physical image simulations are created to train a CycleGAN. A subsequent size-estimation network is developed to determine the nuclearity of imaged nanoparticles, providing plausible estimates for ≈70% of experimentally observed particles. This automatic approach enables precise size determination of supported nanoparticle-based catalysts overcoming crystal orientation limitations of conventional techniques, promising high accuracy with sufficient training data. Tools like this are envisioned to be of great use in designing and characterizing catalytic materials with improved atomic precision.
透射电子显微镜（TEM）在多相催化中对于评估负载型金属纳米颗粒的尺寸分布起着至关重要的作用。通常，纳米颗粒的尺寸是在假设为球形几何形状的情况下通过测量直径来量化的，这种简化限制了推进合成-结构-性能关系所需的精度。目前，缺乏能够从原子分辨的TEM图像中可靠地提取更有意义信息（如核数或几何形状）的技术。在此，探索了循环一致生成对抗网络（CycleGANs）来弥合实验图像和模拟图像之间的差距，将实验观察结果与来自其潜在原子结构的信息直接联系起来。使用通过浸渍法合成的通用Pt/CeO（Pt颗粒中心约为2纳米）催化剂，创建了大量的实验扫描透射电子显微照片和物理图像模拟数据集来训练一个CycleGAN。随后开发了一个尺寸估计网络来确定成像纳米颗粒的核数，为约70%的实验观察到的颗粒提供合理的估计。这种自动方法能够精确确定负载型纳米颗粒基催化剂的尺寸，克服了传统技术的晶体取向限制，在有足够训练数据的情况下有望实现高精度。设想这样的工具在设计和表征具有更高原子精度的催化材料方面将有很大用途。
