In situ multimodal microscopic x-ray characterizations demonstrate their unique capabilities in revealing the mechanisms of material degradation and the pathways for mitigation in energy harvesting applications such as halide perovskite solar cells. Despite the excellent device performance exhibited by halide perovskites, their sensitive nature and material interfaces necessitate a precisely controlled and tunable characterization environment to identify the sources of device performance loss. In this work, we designed an in-situ sample chamber that allows the control of various environmental conditions, including heat, illumination, and bias, while simultaneously collecting chemical (X-ray fluorescence, XRF), optical (X-ray Excited Optical Luminescence, XEOL), and performance (X-ray Beam Induced Current, XBIC) measurements on functional devices. The integrated thermoelectric cooler module of the designed chamber enables controlled heating up to 100 °C and rapid cooling back down to room temperature. This allows simultaneous multimodal XRF, XEOL and XBIC signal collections on Cs0.05FA0.95PbI3 perovskite devices at various temperatures. The results show increasing homogeneity in the XBIC maps and continuous reduction in XEOL intensity, with a redshift in XEOL peak positions as sample temperatures increase. The results of the simultaneous multimodal study pave the way for improved in situ sample environments for future photovoltaic device characterizations.
We previously demonstrated a machine learning based regions-of-interest (ROI) finding tool for X-ray fluorescence microscopy, called XRF-ROI-Finder at the 9-ID beamline in Argonne National Laboratory.1 Bacterial cell treatment type prediction and recommendation for steering experiments were performed via the application of fuzzy k-means clustering algorithm. ROI-Finder takes the fluorescence microscopy images, performs segmentation and detects individual E.coli cells, extracts features for principal component analysis, and ultimately performs label-free clustering for cell treatment type prediction and recommendation for similar cells to perform automatic steering experimentation. In this paper, we assess two additional clustering method, namely hierarchical agglomerative clustering (HAC) and density based spatial clustering of applications with noise (DBSCAN) algorithm. The ROI-Finder software is hosted at https://github.com/aisteer/ROI-Finder.
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