Figure 8 - uploaded by Ashwin Rao
Content may be subject to copyright.
Source publication
![Figure 1. Schematic diagram of LIBS setup [16]. A pulsed laser ablates...](profile/Ashwin-Rao-4/publication/358437603/figure/fig1/AS:1121239397609474@1644335712599/Schematic-diagram-of-LIBS-setup-16-A-pulsed-laser-ablates-the-sample-surface-the_Q320.jpg)
This work investigated the capability of a portable LIBS device to detect and quantify dopants in plutonium surrogate alloys, specifically gallium, which is a common stabilizer used in plutonium alloys. The SciAps Z500-ER was utilized to collect spectral data from cerium-gallium alloys of varying gallium concentrations. Calibration models were buil...
Citations
... The limit of detection (LoD) based on a univariate calibration is directly dependent on the sensitivity (slope) of the calibration; this often results in univariate calibrations producing unreliable and less accurate regression models from complex spectral data. This is evident in Fig. 1, produced during the analysis of cerium-gallium alloy spectra in an initial study applying LIBS for Pu analysis [45]. ...
... This allows it to fit trends in the input to the target output more accurately using the same number of components. This improved fit is demonstrated in Fig. 4, which shows both regression methods fitting LIBS data corresponding to known Ga content levels in Ce alloys (observed response) to predicted concentrations of gallium determined by the models (fitted response) [45]. The corresponding fit metrics are shown in Table 1. ...
... This study indicated that the handheld LIBS device could capture the minor Ga I emissions at 287 and 294 nm among the bulk Ce emissions, and that these emission peaks could be used to generate univariate calibrations with detection limits as low as 0.3 wt% Ga. A subsequent investigation using multivariate analysis methods to analyze the same Ce-Ga spectra found that implementing PLSR generated a calibration curve for quantifying Ga with RMSE as low as 0.2% [45]. The results of these initial experiments opened up the question of how detection limits and prediction errors for calibration models quantifying Ga could be reduced, given the limited resolving power of handheld LIBS devices. ...
Analytical spectroscopy methods have shown many possible uses for nuclear material diagnostics and measurements in recent studies. In particular, the application potential for various atomic spectroscopy techniques is uniquely diverse and generates interest across a wide range of nuclear science areas. Over the last decade, techniques such as laser-induced breakdown spectroscopy, Raman spectroscopy, and x-ray fluorescence spectroscopy have yielded considerable improvements in the diagnostic analysis of nuclear materials, especially with machine learning implementations. These techniques have been applied for analytical solutions to problems concerning nuclear forensics, nuclear fuel manufacturing, nuclear fuel quality control, and general diagnostic analysis of nuclear materials. The data yielded from atomic spectroscopy methods provide innovative solutions to problems surrounding the characterization of nuclear materials, particularly for compounds with complex chemistry. Implementing these optical spectroscopy techniques can provide comprehensive new insights into the chemical analysis of nuclear materials. In particular, recent advances coupling machine learning methods to the processing of atomic emission spectra have yielded novel, robust solutions for nuclear material characterization. This review paper will provide a summation of several of these recent advances and will discuss key experimental studies that have advanced the use of analytical atomic spectroscopy techniques as active tools for nuclear diagnostic measurements.
... The SciAps corporation produces handheld Z-series LIBS analyzers, which weigh only a few pounds, cost approximately $40,000 (USD), and are widely used for elemental analysis of scrap and industrial metals [15,16]. A recent study using cerium, a common chemical surrogate for plutonium, proved that the Z500 could effectively quantify the presence of gallium in Ce-Ga alloys [17,18]. This has paved the way for applying portable LIBS systems for elemental analysis of plutonium samples. ...
... Finally, recent work conducted by the author using the Z500 developed the groundwork for analysis of Pu by conducting experiments on cerium alloys [18]. This study examined the capabilities of the portable LIBS system for quantifying gallium present in Ce-Ga alloys. ...
... This allows it to fit trends in the input to the target output more accurately using the same number of components. This improved fit is demonstrated in Fig. 25, which shows both regression methods fitting LIBS data to predicted concentrations of gallium in cerium alloys [18]. The corresponding fit metrics are shown in Table 1. ...
Analytical atomic spectroscopy methods have the potential to provide solutions for rapid, high fidelity chemical analysis of plutonium alloys. Implementing these methods with advanced analytical techniques can help reduce the chemical analysis time needed for plutonium pit production, directly enabling the 80 pit-per-year by 2030 manufacturing goal outlined in the 2018 Nuclear Posture Review. Two commercial, handheld elemental analyzers were validated for potential in situ analysis of Pu. A handheld XRF device was able to detect gallium in a Pu surrogate matrix with a detection limit of 0.002 wt% and a mean error of 8%. A handheld LIBS device was able to yield univariate detection limits as low as 0.1 wt% Ga with mean error of 3%. Implementing machine learning methods for spectral analysis with the handheld LIBS device reduced error to 0.27%, but the limited device resolution impedes improvements in sensitivity. A compact Echelle spectrometer was implemented with a laboratory LIBS setup to reach a detection limit of 0.006 wt% Ga when coupled with an optimized extra trees regression. A Gaussian kernel regression trained on this high resolution data set yielded the most accurate predictive model with 0.33% error. Lastly, the phenomenon of self-absorption was quantified and corrected for in Ce-Ga LIBS spectra. By implementing a Stark broadening based correction, the univariate detection limit for Ga from LIBS spectra was reduced to 0.008%. Overall, this research indicates that implementing a compact, high resolving power spectrograph for recording Pu alloy spectra and developing optimized machine learning models for spectral analysis can yield high fidelity solutions for Pu alloy chemical analysis and quality control.
... Additionally, both regressions yielded nearly indentical LoD values of 0.015% and 0.017% for the GKR and ANN, respectively. These translate to detection limits in the low 100s of parts-per-million (ppm) for Ga, which is a significant improvement over univariate LoDs previously calculated with a handheld LIBS device, which failed to reach lower than 1000s of ppm Ga [52]. Additonally, using a trilayer ANN with regularization overcame previous issues with overfitting data, which had been observed in single layer feedforward neural networks used to analyze lathanide and actinide spectra. ...
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 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys.
