Modeling Nanoscale Imaging in Electron Microscopy
Imaging with electrons, in particular using scanning transmission electron microscopy (STEM), will become increasingly important in the near future, especially in the materials and life sciences. Understanding cellular interaction networks will enable transformative research such as “visual proteomics,” where spatial arrangements of the proteome or particular subsets of proteins will be mapped out. In the area of heterogeneous catalysis, which in many cases relies on nanoparticles deposited onto supports recently, achieved advances in imaging and characterization of catalysts and precatalysts are transforming the field and
allowing more and more rational design of multifunctional catalysts. Advances in nanoscale manufacturing will require picometer resolution and control as well as the elimination of routine visual inspection by humans to become viable and implemented in “real” manufacturing environments. There are (at least) two major obstructions to fully exploit the information provided by electron microscopy.
On the one hand, a major bottleneck in all these applications is currently the “human-in-the-loop” resulting in slow and labor-intensive selection and accumulation of images. A “smart” microscope in which instrument control, image
prescreening, image recognition, and machine learning techniques are integrated would transform the use of electron imaging in materials science, biology, and other fields of research by combining fast and reliable imaging with automated highthroughput analysis such as combinatorial chemical synthesis in catalysis or the multiple “omics” in biology.
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