![]() ![]() ![]() With the discovery of new materials and new applications for those known, the need for surface and interface analysis has notably increased. Furthermore, a series of results for molecules, containing elements of the second and the third row of the periodic table, are presented and compared with experimental results, in order to establish the quality and fitness-for-purpose of the quantum chemical-based predictions. In this work, we present a general summary of the methods for the calculation of the core electron binding energies and compare the use of 2 of these methods using the popular “GAUSSIAN” software package. In practice, though, care needs to be taken in the approximations, assumptions, and settings used in applying such software to calculate binding energies. In principle, such calculations have become much easier than in the past, due to the availability of powerful personal computers and excellent software. However, reference materials are not always available, so that it becomes necessary to estimate the binding energies of likely components through quantum chemical calculations. In many cases, reference spectra taken from pure reference samples of the chemical components can aid the peak fitting procedure. Often, these shifts are small, or an element is present in several oxidation states in the same sample, so that interpretation of the spectra is difficult without good reference data on binding energies of the likely constituents. We can do this by masking and selecting the data before averaging.Chemical shifts observed in high-resolution X-ray photoelectron spectroscopy (XPS) spectra are normally used to determine the chemical state of the elements of interest. The first component corresponds to a \(\text_2\) region, as identified by Component 0. Very intense locations in the spatial maps correspond to places where the spectrum on the right is a good representation to the data (red) or to its negative (blue).īased on the images, we can see that the first four components (0 through 3) explain almost all the variation in the data. Each row above has a spatial map of the coefficient in the decomposition (left) and the XPS spectrum corresponding to that component (right). A Comprehensive Software Stack for Condensed Matter Physics.Converting Time-of-Flight Data to Kinetic Energy.Understanding ARPES: the Single Particle Spectral Function.Understanding ARPES: Momentum Conversion.Adding Support for Beamlines or Lab Facilities.More advanced plotting techniques in PyARPES.Looking at the wider vs narrower peak regions. ![]() Selecting data using the PCA decomposition. ![]()
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January 2023
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