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| Home > Publications database > Data processing of in-situ TEM toward live processing |
| Poster (After Call) | FZJ-2025-00164 |
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2024
Abstract: In-situ transmission electron microscopy (in-situ TEM) is an evolving technique because it enables the investigation of nanoscale phenomena with various stimuli such as heating, cooling, bias, gas, and liquid [1]. As external stimuli usually reduce the TEM data quality, many in-situ TEM studies have focused on improving the data quality by stabilizing the TEM from stimuli or post-processing the data with machine learning. However, an important point for in-situ TEM is the implantation of the target phenomena into the TEM reality, which is only available when the stimuli actively interact with the living processed information from the observed data. In this case, the processing time is more important than the processing quality. In this paper, we show two data processing cases about electrodeposition with liquid phase in-situ TEM (Fig. 1)[2-3]. First, by applying basic processing (Gaussian filtering, subtraction and thresholding), we could track the size and number of electrodeposited Cu particles and quantitatively compare the change between cycles and time series. Finally, by applying radial Fourier analysis method to 4D STEM data set, we could obtain pseudo-orientation map of electrodeposited Zn with electron diffraction patterns of each selected area. In both cases, valuable information can be extracted even though we did not use complicated algorithms or machine learning. As these procedures are simple enough to adapt to live processing during in-situ TEM, we can actively interact between stimulus condition and phenomena based on live processed information.
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