A novel approach towards experimental parameters optimization in Laser-induced breakdown spectroscopy

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F20%3APU137470" target="_blank" >RIV/00216305:26620/20:PU137470 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
A novel approach towards experimental parameters optimization in Laser-induced breakdown spectroscopy
Original language description
Here we propose a novel and universal method of Laser-Induced breakdown spectroscopy (LIBS) experimental conditions optimization based on machine learning. The simple feedforward neural network (FNN) was trained by empirically measured data. The design of FNN was optimized using a genetic algorithm (GA). As the figure of merit of GA was utilized the signal to noise ratio of selected spectral lines. The input data for FNN can be divided in two groups, one group describing the sample and spectral lines of respective elements (e.g. sample density and hardness, content of selected element, energy levels of selected transitions etc.), and the other group describing the experimental conditions (e.g. laser wavelength and energy, gate delay, gate width etc.). The method is demonstrated and explained in a simple case of single pulse LIBS and two basal parameters – gate delay and laser pulse fluence. Afterwards, we present the optimization for more complex measurement three orthogonal laser pulse (3P LIBS), whe
Czech name
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Czech description
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Project
<a href="/en/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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