Andrey Orekhov (Antwerp / BE), Nikita Denisov (Antwerp / BE), Daen Jannis (Antwerp / BE), Nicolas Gauquelin (Antwerp / BE), Johan Verbeeck (Antwerp / BE)
Abstract text (incl. figure legends and references)
The study of the structure of nanocrystalline materials is often difficult as standard X-ray diffraction techniques break down for sub micrometer particles, especially when occurring in a mixture. Here, we present a tool for structural analysis of a wide range of layered materials and nanosized powder samples based on a conventional scanning electron microscope. An hybrid pixel electron detector below the sample makes it possible to record two dimensional diffraction patterns for every probe position on the sample surface, in transmission mode, thus performing a 4d-STEM (scanning transmission electron microscopy) analysis. This offers a field of view up to 2 mm2, while providing spatial resolution in the nm range, enabling the collection of statistical data on grain size, relative orientation angle, bilayer stacking, strain, etc. which can be mined through a custom-made open-source Python based code. Diffraction pattern acquisition is performed using an Advacam®MiniPix detector with a 300 ?m Si active layer and a 13bit depth dynamic range. We use a standard JEOL JSM-5510 SEM with a W electron source, enabling wide adoption of our proposed setup. Samples are mounted on a Thorlabs 30 mm optical cage system, giving an adjustable sample to detector distance (camera length) ranging from 5 to 50 mm (Fig. 1) [1]. The direct geometrical projection of the diffraction pattern onto the hybrid pixel detector provides a robust calibration of the scattering angles not influenced by lens drift and optical distortions.
Fig. 1. (a) Schematic drawing of the experiment, consisting of a layered material and a hybrid pixel detector at the bottom, inside an SEM sample chamber. (b) Picture of the setup. The Thorlabs optical cage system allows camera length adjustment.
The authors acknowledge the financial support from EU FLAG‐ERA JTC 2017 GRAPH‐EYE, FWO SBO AutomatED S000121N from the Research Foundation Flanders.
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