Simulation of Optical Emission Spectra of Cu-Sn-Zn Alloy Plasmas for Laser-Induced Breakdown Spectroscopy Applications

Sivadas, M S Suryan; John, Lekha Mary; Anoop, K. K.

doi:10.1088/1757-899x/1221/1/012027

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…The visible portion of the spectrum also contains emission lines; however, they are less intense when compared to the UV region. As the temperature decreases (0.7 eV), it is noted that the intensity of UV emission lines decreases, and visible emission dominates over UV and NIR emissions [24]. This is due to the fact that when the plasma temperature decreases, the degree of ionization reduces, and as a result, the intensity of emission lines from ionic species also decreases.…”

Section: Resultsmentioning

confidence: 99%

“…David J. Flannigan used an excel spreadsheet-based program to simulate the atomic emission spectra of neutral atoms [23]. Recently, our team published a study simulating optical emission spectra under typical LIBS conditions to simulate neutral and singly ionized species [24]. Simulation of optical emission spectra (OES) at typical LPP conditions is a convenient tool for understanding the characteristic emission of lines of multielement samples in LIBS experiments [25,26].…”

Section: Simulations Of Optical Emission Spectramentioning

confidence: 99%

“…Additionally, we consider that the plasma is optically thin and that there is no self-absorption in the emission lines. Simulation of OES at typical LPP conditions involves four steps [24]. The essential equations associated with each step are given below,…”

Section: Simulations Of Optical Emission Spectramentioning

confidence: 99%

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Impact of intensity error on temperature estimation in laser-induced breakdown spectroscopy: a numerical study

John,

Anoop

2023

Laser Phys.

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Laser-induced breakdown spectroscopy (LIBS) is a cutting-edge technique for the compositional analysis of multi-element materials. Under standard circumstances for laser-induced plasma (T e = 1 eV and N e = 1016 cm−3), we simulated the emission spectrum of a binary alloy (with 70 wt.% Cu–30 wt.% Al). We used the Saha ionization equilibrium formulas to calculate the population of neutral and ionized species of each constituent element, and the Boltzmann distribution to estimate the intensities of emission lines with radiative transition probabilities. The Stark broadening equation is then used to determine the line broadening, yielding a Lorentzian profile for each line. The sum of line emissions of all constituent species will approximate the alloy’s LIBS spectra in an assumption of ideal analytical plasma. Then, we generated random errors in the intensities of spectral lines ranging from 5% to 35%. To investigate temperature estimation accuracy, we utilized three well-established approaches: the Boltzmann plot (BP) method, the Saha–Boltzmann plot (SBP) method, and the Multi-elemental SBP (MESBP) method. As intensity error increases from 5% to 35%, the estimated temperature in the BP method deviates from 0.25% to 18.3%. Whereas the intensity error is almost unaffected using the SBP method and the MESBP method. The temperature deviation is less than 2% in both situations. This study is relevant to calibration-free LIBS, in which the exact temperature determination is crucial for the abundance estimation of trace, major, and minor elements.

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Section: Resultsmentioning

confidence: 99%

Section: Simulations Of Optical Emission Spectramentioning

confidence: 99%

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Impact of intensity error on temperature estimation in laser-induced breakdown spectroscopy: a numerical study

John,

Anoop

2023

Laser Phys.

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“…Following this, we gave gaussian broadening to each line, and finally, graphed the spectrum based on this broadening [10]. A comprehensive explanation of these various stages in the simulation can be found in our prior research work [11].…”

Section: Deep Learning Neural Network Approachmentioning

confidence: 99%

Deep Learning Neural Network (DNN) Coupled Laser Induced Breakdown Spectroscopy for Quantitative Analysis of Copper Alloys

Pauly,

Mary John,

Suryan Sivadas

et al. 2024

IOP Conf. Ser.: Mater. Sci. Eng.

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Conventional Laser Induced Breakdown Spectroscopy (LIBS) quantitative analysis, employing both calibration and calibration-free techniques, is challenged by spectral overlapping, self-absorption, and spectral broadening effects, leading to decreased accuracy. Recently, integration of machine learning (ML) algorithms with LIBS has been increasingly employed to tackle these challenges. This article explores the augmentation of LIBS with deep learning neural networks (DNN) to enhance accuracy of quantitative analysis of multi-elemental copper alloys. Sufficient training data was acquired by simulating optical emission spectra for bronze (Cu-Sn) and admiralty brass (Cu-Zn-Sn) alloys under standard laser produced plasmas conditions, encompassing different alloy concentrations, electron temperatures, and densities. We designed a regularized DNN structure, fine-tuned using a validation dataset to optimize quantitative results. The model’s accuracy was assessed with test dataset. The quantitative results demonstrated reduced loss as training spectra increased from 500 to 5000 for both alloys. The decline in mean squared error, from 2.793×10−3 to 4.283×10−5 for bronze and from 3.245×10−2 to 5.598×10−4 for admiralty brass alloys, as training data increased from 500 to 5000, underscores the proposed DNN model’s potential for metallurgical alloy quantification.

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“…This section discusses the theory and different stages in the simulation of optical emission spectra. 18 We require plasma characteristics for simulation, including the fractional composition of the elements in the plasma as well as the plasma temperature ( T ) and electron number density ( N e ). Spectroscopic data such as wavelengths, upper and lower-level energies, degeneracy, and transition probability can be retrieved from standard databases like NIST or Kurucz.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A numerical procedure for understanding the self-absorption effects in laser induced breakdown spectroscopy

John,

Anoop

2023

RSC Adv.

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Simulation of Optical Emission Spectra of Cu-Sn-Zn Alloy Plasmas for Laser-Induced Breakdown Spectroscopy Applications

Cited by 4 publications

References 6 publications

Impact of intensity error on temperature estimation in laser-induced breakdown spectroscopy: a numerical study

Impact of intensity error on temperature estimation in laser-induced breakdown spectroscopy: a numerical study

Deep Learning Neural Network (DNN) Coupled Laser Induced Breakdown Spectroscopy for Quantitative Analysis of Copper Alloys

A numerical procedure for understanding the self-absorption effects in laser induced breakdown spectroscopy

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