Nanostructured materials continue to find applications in various electronic and sensing devices, chromatography, separations, drug delivery, renewable energy, and catalysis. While major advancements on the synthesis and characterization of these materials have already been made, getting information about their structures at sub-nanometer resolution remains challenging. It is also unfortunate to find that many emerging or already available powerful analytical methods take time to be fully adopted for characterization of various nanomaterials. The scanning low energy electron microscopy (SLEEM) is a good example to this. In this report, we show how clearer structural and surface information at nanoscale can be obtained by SLEEM, coupled with deep learning. The method is demonstrated using Au nanoparticles-loaded mesoporous silica as a model system. Moreover, unlike conventional scanning electron microscopy (SEM), SLEEM does not require the samples to be coated with conductive films for analysis; thus, not only it is convenient to use but it also does not give artifacts. The results further reveal that SLEEM and deep learning can serve as great tools to analyze materials at nanoscale well. The biggest advantage of the presented method is its availability, as most modern SEMs are able to operate at low energies and deep learning methods are already being widely used in many fields.
Keywords: Deep learning; Mesoporous silica; Scanning low energy electron microscopy.
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