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Artificial neural networks for prediction of laser-induced plasma parameters from emission spectra
Grünwald, Martin ; Képeš, Erik (referee) ; Vrábel, Jakub (advisor)
This bachelor thesis deals with predictions of plasma parameters using an artificial neural network and construction of datasets designed for training artificial neural networks on spectroscopic data. The basic theory of laser-induced breakdown spectroscopy (LIBS), basic aspects of plasma physics and an introduction to artificial neural network theory are all included in this thesis. Furthermore, the thesis focuses on the proceduce of generation, augmentation and preprocessing of a dataset. Along with that, the method for generation of various spectroscopic data and subsequent artificial neural network training is described. The outcome of the thesis is a series of scripts made for executing individual steps and also a proof of concept for plasma parameter prediction from LIBS spectra using an artificial neural network model.
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"Atoms in molecules" (AIM) analysis of the Pt(II)-complexes
ŠVECOVÁ, Kristýna
AIM (Atoms in molecules) analysis of square planar trans-Pt[(NH3)2T(H2O)]n+ complexes (T = H2O, NH3, NO2-, OH-, F-, Cl-, Br-, SCN-, CN-, PH3, CO, CH3-, H-, C2H4) were performed. Electron density in critical points of the Pt-H2O bond correlates inversely proportionally with the length of this bond and with the occupation of 5d orbitals of the central platinum atom and proportionally with a total charge of the H2O ligand. The Pt-H2O bond can be supposed as a typical donor-acceptor bond. AIM analysis is a very useful method for a quantitative description of the strength of the trans-influence.
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