X-Ray Fluorescence of Metal Halide Perovskites

Sammanfattning: This thesis has the aim to provide a framework for analyzing X-ray fluorescence (XRF) data obtained from German Electron-Synchrotron Group (DESY). The theoretical analysis shows that XRF competes with Auger emission, and works mainly for elements with high atomic number. Here, incident X-rays of 13.7 keV, generated by the synchrotron radiation, impinges the material, and fluorescent X-ray spectra are recorded. For the analysis, custom Python scripts have been written to manage large datasets and plot results. They are the major contribution to this work, as they reveal how data is organized and how it should be interpolated, for the XRF maps to be plotted. For the fitting of the individual element data, PyMCA, a Python-based XRF analysis software, is used. The software is able to accurately fit the counts as they are measured, and to assign them to specific elements. From this, an elemental map is provided. The maps shown include K, Br, I, and Pb, while in spectra, Cs is considered as well. The material investigated is a perovskite, CsPbBr$_3$, which can be used to enhance efficiency of solar cells. This material is combined with a KI solutions in different ways, where K is used to improve the perovskite light absorption, and is detected through XRF. The obtained data indeed show presence of K, where counts depend on how K is deposited. Furthermore, individual pixel fit reveals that Br and I tend to segregate when material is exposed to light, as I counts drop. Lastly, PyMCA is able to obtain element counts more accurately that region-of-interest plot. All these effects and similar, can be examined using PyMCA, and custom Python scripts that organize, and interpolate data. Each pixel can be analyzed separately, showing that big data analysis can be a useful tool in science.