A novel confocal XRF-Raman spectrometer and FPM model for analysis of solid objects and liquid substances

Szalóki, I.; Pintér, T.; Radócz, Gábor; Gerényi, Anita

doi:10.1039/c9ja00044e

Cited by 12 publications

(12 citation statements)

References 35 publications

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“…In the third strategy Equation (8c), the elementary sensitivity is applied, where g iE is the excitation functions, ε i is the efficiency of the detector at energy of the characteristic X-ray radiation of the ith element. 7 The parameter E i is the binding energy of the ith element and E max is the maximum energy of the Bremsstrahlung radiation of the primary X-ray radiation. None of the formulas (8) take into account the energydependent selective absorption of the matrix and the second-order excitation (enhancement) effects.…”

Section: Convergence Stability and Processing Time Of The Rmc-xrfmentioning

confidence: 99%

“…The stability of the whole numerical procedure was tested in such a way that in MC simulations, 10 6 source photons were generated in each iteration step and this calculation was repeated 10 times. 19 The statistical results of the RMC-XRF numerical calculations are shown in Table 1. For the comparability of the RMC results, Table 1 exhibits the measured and calculated concentrations determined by FPM-XRF and Spark Optical Emission Spectrometry (SOES) as well.…”

Section: Convergence Stability and Processing Time Of The Rmc-xrfmentioning

confidence: 99%

“…For the comparability of the RMC results, Table 1 exhibits the measured and calculated concentrations determined by FPM-XRF and Spark Optical Emission Spectrometry (SOES) as well. The FPM-XRF quantification technique in general, the algorithm of the mathematical solution, and the detailed mathematical description of the model and the numerical procedure are published in refs [5,7]. The FPM model of the XRF analysis is based on the mathematical relationships between concentrations of the sample elements and the measured characteristic X-ray intensities.…”

Section: Convergence Stability and Processing Time Of The Rmc-xrfmentioning

confidence: 99%

“…Results of SOES, 19 RMC-XRF, and FPM-XRF 7 caused by electronic noise and the signal shaping in the spectrometer electronics units; however, it can be calculated for the simulated spectra by the so called GEB parameter. 20 Quantitative evaluation of the simulated Xray spectra using dedicated software such as WinQxas 21 or bAxil 22 in every iteration step increases the necessary processing time and in some cases requires the intervention of the operator person as well.…”

Section: T a B L Ementioning

confidence: 99%

“…The FP method can be used to analyze the quantitative composition of substances over concentration range of 10 4− 10 2 m/m% for atoms 13 ≤ Z. 7 In cases where the pattern does not have a regular shape (eg. a flat surface), creation of FP equations can become rather difficult, which limits the routine applicability of the FP technique.…”

Section: Introductionmentioning

confidence: 99%

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Reverse Monte Carlo iterative algorithm for quantification of X‐ray fluorescence analysis based on MCNP6 simulation code

2020

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Reverse Monte Carlo iterative algorithm has been developed for quantification of energy‐dispersive X‐ray fluorescence analysis in order to calculate the concentrations of the elementary composition in solid substances. The core of the simulation code was the MCNP6 that is a well‐established and widely applied software package in the nuclear research and practice for simulation of nuclear systems or the full process of gamma‐ or X‐ray spectrometry. The reverse Monte Carlo algorithm and the full analytical procedure was tested by quantitative XRF analysis of reference alloy samples. The atomic compositions of the reference samples were determined by reverse Monte Carlo technique and also fundamental parameter method and by spark emission atomic spectroscopy. The agreement between the results of these three analytical methods was found within the standard deviations of the major elements of the samples. The total duration of the reverse Monte Carlo numerical computation was minimized to a few minutes using the variance reduction procedures available in the MCNP6.

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Section: Convergence Stability and Processing Time Of The Rmc-xrfmentioning

confidence: 99%

Section: Convergence Stability and Processing Time Of The Rmc-xrfmentioning

confidence: 99%

Section: Convergence Stability and Processing Time Of The Rmc-xrfmentioning

confidence: 99%

Section: T a B L Ementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reverse Monte Carlo iterative algorithm for quantification of X‐ray fluorescence analysis based on MCNP6 simulation code

2020

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Highly Tuneable Photochromic Sodalites for Dosimetry, Security Marking and Imaging

Byron,

Swain,

Paturi

et al. 2023

Adv Funct Materials

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Photochromic sodalites are considered for a plethora of possible applications, such as UV indexing and X‐ray imaging, but for many of these the materials are yet to be optimized. UV indexing can be improved through incremental adjustment of the activation energy of coloration from 300 to 410 nm through replacement of sulfur with selenium. By combining this and other methods of tuning presented in the literature, the excitation threshold and photochromism color can be tuned independently of one another. The range of possible absorption maxima is expanded to 420–680 nm, or almost the entire visible spectrum. Mixing low‐cost and easy‐to‐synthesize sodalites further broadens the possible range of colors and facilitates development of a unique sodalite mix capable of quantifying the doses of two types of UV radiation simultaneously. Finally, the response to X‐rays of these highly tuned sodalites is investigated, and it is found that they can be sensitized to produce clear, high‐contrast X‐ray images at significantly lower doses of radiation than those required by classic photochromic sodalite, Na8(AlSiO4)6(Cl,S)2.

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A background correction method for energy‐dispersive x‐ray fluorescence spectra based on morphological operation

Wang¹,

Ai²,

Xiong³

et al. 2023

X-Ray Spectrometry

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Energy‐dispersive x‐ray fluorescence spectra often suffer from background caused by primary x‐ray. Therefore, background correction is of great significance that must be performed before subsequent analysis. In this article, a background subtraction method based on morphological operation was proposed. The morphological operation and the smoothing factor could effectively reduce the possibility of background underestimation, especially for a noisy spectrum. The method was verified by simulated and experimental data, and comparisons with other peak‐clipping and penalized least square methods were made. The results showed that the method proposed in this article could get a smooth continuous estimated background with the least root mean square error compared with others. Meanwhile, it can achieve automated background correction, for there is no need to set physical parameters manually. In conclusion, those results indicated that the proposed method could be applied to the rapid in situ x‐ray fluorescence analysis to effectively correct the background.

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A novel confocal XRF-Raman spectrometer and FPM model for analysis of solid objects and liquid substances

Abstract: A new table-top combined spectrometer was designed and constructed consisting of X-ray fluorescence and Raman spectrometers for spot-analysis of elementary and chemical composition of solid and liquid substances for industrial analytical applications.

Cited by 12 publications

References 35 publications

Reverse Monte Carlo iterative algorithm for quantification of X‐ray fluorescence analysis based on MCNP6 simulation code

Reverse Monte Carlo iterative algorithm for quantification of X‐ray fluorescence analysis based on MCNP6 simulation code

Highly Tuneable Photochromic Sodalites for Dosimetry, Security Marking and Imaging

A background correction method for energy‐dispersive x‐ray fluorescence spectra based on morphological operation

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