Data Fusion of Acoustic and Optical Emission from Laser-Induced Plasma for In Situ Measurement of Rare Earth Elements in Molten LiCl-KCl.

Molten salts have a significant potential for use as next-generation nuclear reactor coolants and in pyroprocessing for the recycling of used nuclear fuel. However, the molten salt composition needs to be known at all times, and high temperatures and intense ionizing radiation pose challenges for the monitoring instrumentation. Although the technique of laser-induced breakdown spectroscopy (LIBS) has been studied for in situ measurements of molten salts, trials to improve its monitoring accuracy using chemometrics are lacking. In this study, a data fusion technique using the LIBS optical and laser-induced acoustic (LIA) signals was investigated to enhance the measurement accuracy for molten salt monitoring. Prediction models were constructed using the partial least-squares method, and the variable importance in projection scores was analyzed to evaluate the effect of incorporating the LIA signal into the analysis. This study investigates rare earth elements Eu, Er, and Pr found not only in nuclear but also in other settings such as laser and magnetic materials. The analysis of LIBS data without data fusion resulted in a root-mean-square error of prediction (RMSEP) of 0.0774-0.0913 wt %, whereas the prediction model using data fusion led to approximately 18-40% enhanced RMSEP (0.0461-0.0679 wt %). The results suggest that fusing the LIBS data with the simultaneously recorded LIA data can improve the monitoring accuracy of rare earth element composition in molten salts.

Impact of Glass Irradiation on Laser-Induced Breakdown Spectroscopy Data Analysis.

Increased absorption of optical materials arising from exposure to ionizing radiation must be accounted for to accurately analyze laser-induced breakdown spectroscopy (LIBS) data retrieved from high-radiation environments. We evaluate this effect on two examples that mimic the diagnostics placed within novel nuclear reactor designs. The analysis is performed on LIBS data measured with 1% Xe gas in an ambient He environment and 1% Eu in a molten LiCl-KCl matrix, along with the measured optical absorption from the gamma- and neutron-irradiated low-OH fused silica and sapphire glasses. Significant changes in the number of laser shots required to reach a 3σ detection level are observed for the Eu data, increasing by two orders of magnitude after exposure to a 1.7 × 1017 n/cm2 neutron fluence. For all cases examined, the spectral dependence of absorption results in the introduction of systematic errors. Moreover, if lines from different spectral regions are used to create Boltzmann plots, this attenuation leads to statistically significant changes in the temperatures calculated from the Xe II lines and Eu II lines, lowering them from 8000 ± 610 K to 6900 ± 810 K and from 15,800 ± 400 K to 7200 ± 800 K, respectively, for exposure to the 1.7 × 1017 n/cm2 fluence. The temperature range required for a 95% confidence interval for the calculated temperature is also broadened. In the case of measuring the Xe spectrum, these effects may be mitigated using only the longer-wavelength spectral region, where radiation attenuation is relatively small, or through analysis using the iterative Saha-Boltzmann method.

Machine learning-assisted laser-induced breakdown spectroscopy for monitoring molten salt compositions of small modular reactor fuel under varying laser focus positions.

Next-generation advanced nuclear reactors based on molten salts are interested to apply machine learning (ML) technology to minimize human error and realize effective autonomous operation. Owing to harsh environments with limited access to molten salts, laser-induced breakdown spectroscopy (LIBS) has been investigated as a possible option for remote online monitoring. However, the height of molten salts is easily fluctuated by vibration. In addition, the level of molten salts could change during normal operation through the insertion of a controlling structure. While these uncertainties should be considered, their effects have not been studied yet. In this study, LIBS has been actively coupled with ML to automate the online monitoring of difficult-to-access molten salt systems. To practically apply a prediction model with ML, we intentionally defocus the measurement by manipulating the sample position. This study investigates the focusing and defocusing spectra of Sr and Mo as fission products for constructing the two prediction models using partial least squares and artificial neural network methods. For each method, the prediction models trained with focusing spectra only or focusing and defocusing spectra simultaneously are constructed and compared to each other. While the prediction model using only focusing spectra resulted in a root mean square error of prediction (RMSEP) of 0.1943-0.2175 wt%, a prediction model using both spectra led to approximately 10 times enhanced RMSEP (0.0210-0.0316 wt%). This study implies that not only focusing data but also defocusing data are needed to construct the prediction model while considering its practical usage in a real system, especially in the complex processes of the nuclear industry.

Prognostic significance of Human epidermal growth factor receptor-2 expression in patients with resectable gastric adenocarcinoma.

BACKGROUND: The purpose of this study was to investigate the correlation between human epidermal growth factor receptor 2 (HER2) overexpression and clinicopathologic factors and overall survival rate in patients who underwent curative gastrectomy for gastric adenocarcinoma. METHODS: Among patients who underwent curative gastrectomy for gastric adenocarcinoma at Inje University Paik Hospital from January 2012 to December 2015, 782 patients underwent an immunohistochemical analysis to evaluate HER2 expression levels. Clinicopathologic records that were collected from a gastric cancer database were retrospectively reviewed to identify clinicopathologic factors and survival rates of the patients. RESULTS: HER2 overexpression was detected in 166 patients (21.2%). There was a statistically significant correlation between HER2 expression level and sex (p = 0.013), histologic differentiation (p < 0.001), Lauren classification (p < 0.001), and T pathologic stage (p = 0.022). There were no statistically significant relationships between HER2 expression level and overall 5-year survival rate (p = 0.775) and overall 5-year survival rate of gastric adenocarcinoma classified according to the TNM stage (stage I: p = 0.756, stage II: p = 0.571, stage III: p = 0.704). The HER2 expression level was not affected by the overall 5-year survival rate in the uni- and multivariate analyses. CONCLUSIONS: In this study, the HER2 overexpression rate in gastric adenocarcinoma was 21.2% and was observed in well- and moderately differentiated types according to histologic differentiation, intestinal type according to the Lauren classification, male, and low T stage. There was no correlation between HER2 expression level and overall 5-year survival rate, and HER2 expression level was not associated with independent prognostic factors.