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Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.
Although nanoparticles have been shown to have clear technological advantages, their use in some consumer products remains controversial, particularly where these products come in direct contact with our bodies. There has been much discussion about using metal oxide nanoparticles in sunscreens, and numerous technology assessments aimed at predicting the type, size and concentration of nanoparticles and surface treatments that will be best for consumers. Yet, the optimal configuration is ultimately the one that people actually want and are willing to pay for, but until now consumer preferences have not been included in model predictions. We describe and discuss a proof of concept study in which we design and implement a hypothetical sunscreen product configurator to predict how people tradeoff sun protection factor (SPF), product transparency and potential toxicity from reactive oxygen species (ROS) in configuring their most preferred sunscreen. We also show that preferred nanoparticle sizes and concentrations vary across demographic groups. Our results suggest that while consumers choose to reduce or eliminate potential toxicity when possible, they do not automatically sacrifice high SPF and product transparency to avoid the possibility of toxicity from ROS. We discuss some advantages of using product configurators to study potential product designs and suggest some future research possibilities.
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