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Full-Text Articles in Physics

Structural And Spectroscopic Analysis For Silver Bulk And Nanoparticles, Hajir M. Fadhil, Khaleel I. Hassoon, Hyder A. Salih May 2022

Structural And Spectroscopic Analysis For Silver Bulk And Nanoparticles, Hajir M. Fadhil, Khaleel I. Hassoon, Hyder A. Salih

Karbala International Journal of Modern Science

In this research work, a pulsed Nd-YْAG laser having a wavelength of 1064 nm and energy (400-700 mJ (has been utilized as a source in an induced breakdown spectroscopy (LIBS) experiment to determine the density of electron and the tem-perature of Ag-plasma. Two forms of silver (as a bulk and as a compressed nano powder) have been used as targets in the LIBSs setup. The aim of the present work is to study the impact of target properties and laser energy on the plasma fea-tures formed by the interaction between a pulsed laser and these two forms of silver. The …


Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil Patnaik Jan 2022

Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil Patnaik

Faculty Publications

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of …