X-Ray Fluorescence of Metal Halide Perovskites
(2022) FYSK02 20212Synchrotron Radiation Research
Department of Physics
- Abstract
- 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... (More)
- 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. (Less)
- Popular Abstract
- The acute problem of obtaining clean energy is getting more and more apparent each day. The need for energy-efficient renewable systems is growing but is limited by physical processes that govern the energy generation from renewable sources. A prime example is solar, which some parts of the world are having in abundance, while the solar energy capture and conversion into electricity, using solar cells, is a very inefficient process. The aim of this work would be to enable the optimization of the solar cell to be better receptive towards harvesting solar energy.
The solar cells are typically made from a single material that absorbs light, that being silicon. It is an abundant material, found mainly as sand. It absorbs light fairly well,... (More) - The acute problem of obtaining clean energy is getting more and more apparent each day. The need for energy-efficient renewable systems is growing but is limited by physical processes that govern the energy generation from renewable sources. A prime example is solar, which some parts of the world are having in abundance, while the solar energy capture and conversion into electricity, using solar cells, is a very inefficient process. The aim of this work would be to enable the optimization of the solar cell to be better receptive towards harvesting solar energy.
The solar cells are typically made from a single material that absorbs light, that being silicon. It is an abundant material, found mainly as sand. It absorbs light fairly well, but it has a limit in how efficient it can be in absorbing light. Therefore, an idea can be to add different layers that would help in absorbing light and increase efficiency. The more absorbed light in a cell, the better its efficiency is, and higher energies can be achieved at the output of a cell. It would result in more energy being available for consumption, which then reduces the number of cells which need to be installed. Such solution would lower the electricity costs, so the society would benefit from it in the end.
One important aspect of developing such materials that enhance solar sell efficiency is understanding the properties of materials and finding the optimum material that can be used. Out of many methods, this work uses a method where high-energy light is shined onto a material and the light that is returned from the material is then examined. This is typically done in big facilities which are known as synchrotrons. They can be of great help to understand the properties of the specific material, as well as differentiate how many of specific elements there is in the material. Typically, a combination of several elements would enable the improvement in the energy efficiency of the solar cell. Such compound materials are called perovskites, because of their specific properties, and materials that are used to create a compound.
This work attempts at understanding basic material properties by analyzing synchrotron imaging of perovskites, which are intended to be implemented in high-energy-efficiency solar cells. The data is stored in multidimensional arrays, so it is useful to develop an algorithm that would efficiently process and interpret this measurement data, providing relevant information and understanding what materials are present in the perovskite, as well as to what amount. Material proportions are important, in order to build a high-efficiency solar cells. Analyzing the measurement data in an algorithm also makes the process of understanding the material easier and faster, which reduces the material development time. Combining different methods may result in a desired material that can be implemented, for the benefit of all. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9078567
- author
- Narciso Silva, Samuel LU
- supervisor
- organization
- course
- FYSK02 20212
- year
- 2022
- type
- M2 - Bachelor Degree
- subject
- keywords
- Perovskite, X-ray, Fluorescence, Python, PyMCA, Synchrotron radiation
- language
- English
- id
- 9078567
- date added to LUP
- 2022-04-20 11:27:32
- date last changed
- 2022-04-20 11:27:32
@misc{9078567,
abstract = {{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.}},
author = {{Narciso Silva, Samuel}},
language = {{eng}},
note = {{Student Paper}},
title = {{X-Ray Fluorescence of Metal Halide Perovskites}},
year = {{2022}},
}