| Figure 0042a shows that each diffraction pattern represents an average of 7 × 7 experimental images because this approach reduces noise and enhances the signal quality by averaging adjacent probe positions. This technique effectively increases the dataset's robustness by combining multiple images, helping to capture finer details in the diffraction patterns while minimizing the impact of random variations that could arise from individual measurements. This averaging process, over a 7 × 7 grid, is beneficial in obtaining clearer and more reliable structural information about the sample, especially in complex 4D-STEM experiments where high precision is essential.
Figure 0042a. Experimental 4D-STEM measurement of a dichalcogenide 2D material. Atomic map is inferred from the data, where each diffraction pattern represents an average of 7 × 7 experimental images, enhancing signal quality. Green-labeled STEM probes indicate sample regions with one layer, vacuum, and two layers. [1] |
Figure 0042b demonstrates the methodology and components involved in using 4D scanning transmission electron microscopy (4DSTEM) to analyze lattice structures within peptide nanocrystals. The figure illustrates key aspects of the experimental setup, including the nanobeam's raster scanning across the crystal, producing diffraction patterns at each scan position. The main diagram shows a high-angle annular dark field (HAADF) STEM image highlighting the crystal's structure, while subsequent panels display a montage of diffraction patterns obtained from the scan, with specific Bragg reflections indicated. This setup enables the precise mapping of nanoscale lattice orientations, revealing details of the crystal's internal structure and orientation variations at the sub-100 nm scale. The grain orientations can be mapped, e.g. with MapViewer (e.g.page1175).
Figure 0042b. Measurement of lattice structure in peptide nanocrystals using 4DSTEM. (a) Schematic of the 4DSTEM setup, highlighting the essential components; inset displays a low-dose, low-magnification HAADF STEM image. (b) Higher resolution STEM image of the crystal shown in the inset of (a), revealing finer details. (c) Array of diffraction patterns from a 4DSTEM scan corresponding to (b); (d) A single diffraction pattern, and (e) The averaged diffraction pattern from the region marked in red in (c). The primary beam is masked with red circles, and blue arrows indicate select Bragg reflections. [2] |
The workflow of 4D-STEM diffraction involves background subtraction, normalization, thresholding, and pixel-level stepwise counting [2]:
- Background subtraction is used to enhance the signal quality in diffraction patterns. That is, after raw diffraction patterns are collected, any unwanted background noise is removed. This involves estimating a baseline level for background noise from pixel intensity distributions and then subtracting this background level from the diffraction images. This technique helps isolate the actual diffraction signals (such as Bragg reflections) by reducing the influence of low-intensity, non-signal elements in the images.
- By normalizing, we can control the variations in pixel intensities, facilitating clearer interpretation of Bragg reflections and central beam intensities, which ultimately helps estimate crystal properties such as thickness more accurately.
- Thresholding is used to filter out background noise and enhance the clarity of important features, such as Bragg reflections, in processing electron diffraction images. This is achieved by setting a threshold value so that pixel intensities below this value, which are considered noise or background, are removed from the data. By focusing on pixels above this threshold, the signal-to-noise ratio improves, allowing more accurate detection and counting of significant electron events, such as those corresponding to diffraction peaks.
- Pixel-level stepwise counting refers to a technique for processing electron diffraction patterns by assigning counts to each pixel based on detected electron events. This method involves dividing pixel intensities into counting bins, where each bin represents a specific number of electron counts. Pixels are categorized as having either zero counts (background), single counts, or multiple counts if they register coincident electrons. This stepwise counting enhances the dynamic range and accuracy of diffraction data by reducing signal loss due to electron coincidences, especially around Bragg reflection regions where signals are concentrated. Note the diffraction patterns can be indexed, e.g. with DiffGen 2 (see page1175).
[1] Ophus, C., Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM): From Scanning Nanodiffraction to Ptychography and Beyond. Microscopy and Microanalysis, 25(3), 563–582. doi:10.1017/S1431927619000497, 2019.
[2] Gallagher-Jones M, Ophus C, Bustillo KC, Boyer DR, Panova O, Glynn C,
Zee C-T, Ciston J, Mancia KC, Minor AM & Rodriguez JA (2019).
Nanoscale mosaicity revealed in peptide microcrystals by scanning electron
nanodiffraction. Commun Biol 2, 26.
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