Online monitoring is a key challenge for the continued development of molten salt reactor (MSR) technology. Laser-induced breakdown spectroscopy (LIBS) has previously been demonstrated to be a viable ...tool for monitoring aerosolized species and noble gases in real time, but the ability to discern varying isotopes in these streams has not yet been investigated for MSR applications. Tritium will form in MSRs from ternary fission and from (n,α)-reactions occurring in lithium-containing salts. This study compares three spectrometers of varying resolutions and types for measuring hydrogen isotope shifts in LIBS spectra of wetted filter paper. For each spectrometer, multivariate models were built (i.e., principal component regression, partial least squares regression, and multivariate curve resolution) to quantify the isotope ratio. The top models were then modified and corrected to apply the models to aerosol samples with varying isotope ratios. This novel calibration strategy offers an 82% reduction in volume of the calibration samples needed and is a more viable pathway for calibrating deployable LIBS systems. Lastly, this calibration model was compared with an all-aerosol trained model for monitoring hydrogen isotopes during a real-time test where the protium/deuterium ratio, along with representative salt species (i.e., lithium, sodium, and potassium) were adjusted dynamically. Results of this test validated the predictive capabilities of the transferred model and highlighted the capabilities of LIBS for real-time monitoring of MSR effluent streams.
Near-infrared spectrophotometry and partial least squares regression (PLSR) were evaluated to create a pleasantly simple yet effective approach for measuring HNO
concentration with varying ...temperature levels. A training set, which covered HNO
concentrations (0.1-8 M) and temperature (10-40 °C), was selected using a D-optimal design to minimize the number of samples required in the calibration set for PLSR analysis. The top D-optimal-selected PLSR models had root mean squared error of prediction values of 1.4% for HNO
and 4.0% for temperature. The PLSR models built from spectra collected on static samples were validated against flow tests including HNO
concentration and temperature gradients to test abnormal conditions (e.g., bubbles) and the model performance between sample points in the factor space. Based on cross-validation and prediction modeling statistics, the designed near-infrared absorption approach can provide remote, quantitative analysis of HNO
concentration and temperature for production-oriented applications in facilities where laser safety challenges would inhibit the implementation of other optical techniques (e.g., Raman spectroscopy) and in which space, time, and/or resources are constrained. The experimental design approach effectively minimized the number of samples in the training set and maintained or improved PLSR model performance, which makes the described chemometric approach more amenable to nuclear field applications.
This study with surrogate materials shows that laser-induced breakdown spectroscopy (LIBS) is a robust tool with promising capability toward monitoring gaseous (Xe and Kr) and aerosol (Cs and Rb) ...species in an off-gas stream from a molten salt reactor (MSR). MSRs will continually evolve fission products into the cover gas flowing across the reactor headspace. The cover gas entrains Xe and Kr gases, along with aerosol particles, before passing into an off-gas treatment system. Univariate models of Xe and Kr peaks showed a strong correlation to concentration indicated by their coefficients of determination of 0.983 and 0.997, respectively. Multivariate models were built for all four analytes using partial least squares regression coupled with preprocessing steps including normalization, trimming, and/or genetic algorithm derived filters. The models were evaluated by predicting the concentrations of the analytes in four validation samples, in which all calibration models were successfully validated at a confidence interval of 99.9%. Lastly, pressure controllers were used to regulate the mass flow rate of Kr flowing into the measurement cell in sinusoidal and stepwise waveforms to test the real-time monitoring capabilities of the regression models. Both univariate and partial least squares Kr models were able to successfully quantify the gas concentration in the real-time evaluation. The root mean squared error of prediction (RMSEP) values for these real-time tests were calculated to be 0.051, 0.060, and 0.121 mol% demonstrating the measurement systems’ capability to perform online monitoring with acceptable accuracy.
Graphical Abstract
Spectroelectrochemistry and optimal design of experiments can be used to rapidly build accurate models for species quantification and enable a greater level of process awareness. Optical spectroscopy ...can provide vital elemental and molecular information, but several hurdles must be overcome before it can become a widely adopted analytical method for remote analysis in the nuclear field. Analytes with varying oxidation state, acid concentration, and fluctuating temperature must be efficiently accounted for to minimize time and resources in restrictive hot cell environments. The classic one-factor-at-a-time approach is not suitable for frequent calibration/maintenance operations in this setting. Therefore, a novel alternative was developed to characterize a system containing vanadium(IV/V) (0.01–0.1 M), nitric acid (0.1–4 M), and varying temperatures (20–45 °C). Spectroelectrochemistry methods were used to acquire a sample set selected by optimal design of experiments. This new approach allows for the accurate analysis of vanadium and HNO3 concentration by leveraging UV–Vis–NIR absorption spectroscopy with robust and accurate chemometric models. The top model's root mean squared error of prediction percent values were 3.47%, 4.06%, 3.40%, and 10.9% for V(IV), V(V), HNO3, and temperature, respectively. These models, efficiently developed using the designed approach, exhibited strong predictive accuracy for vanadium and acid with varying oxidation states and temperature using only spectrophotometry, which advances current technology for real-world hot cell applications. Additionally, Nernstian analysis of the V(IV/V) standard potential was performed using traditional absorbance methods and multivariate curve resolution (MCR). The successful tests demonstrated that MCR Nernst tests may be valuable in highly convoluted spectral systems to better understand the redox processes' behavior.
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•Utilized spectroelectrochemistry for training models with multiple oxidation states of vanadium.•Multivariate models directly quantify vanadium oxidation states, nitric acid, and temperatures in real-time.•A multivariate curve resolution (MCR) approach was demonstrated for Nernst analysis.•These methods are amenable to applications in radiological gloveboxes for monitoring and characterizing nuclear material.
Laser-induced fluorescence spectroscopy (LIFS), Raman spectroscopy, and a stacked regression ensemble was developed for near real-time quantification of uranium(
vi
) (1-100 μg mL
−1
), samarium ...(0-200 μg mL
−1
) and nitric acid (0.1-4 M) with varying temperature (20 °C-45 °C). LIFS applications range from fundamental lab-scale studies to real-time process monitoring at industrial levels, such as nuclear reprocessing applications, provided the phenomena affecting the fluorescence spectrum are accounted for (
e.g.
, absorption, quenching, complexation). Multiple chemometric models were examined and compared to a more traditional multivariate regression approach called partial least squares (PLS). Results obtained on synthetic samples selected using D-optimal experimental design indicated that a stacked regression method, which included ridge regression, random forest, PLS, and an eXtreme gradient boost algorithm, successfully measured uranium(
vi
) concentrations directly in nitric acid without measuring luminescence lifetimes or standard addition. The top model resulted in percent root-mean-square error of prediction values of 5.2, 1.9, 3.0, and 2.3% for U(
vi
), Sm
3+
, HNO
3
, and temperature, respectively. The approach may be useful for quantifying fluorescent fission products (
e.g.
, Sm
3+
) to provide information on burnup of irradiated nuclear fuel. This novel framework reinforces the applicability of LIFS for real-time applications in nuclear fuel cycle applications.
Laser-induced fluorescence spectroscopy, Raman spectroscopy, and a stacked regression was developed for rapid quantification of uranium(
vi
) (1-100 μg mL
−1
), samarium (0-200 μg mL
−1
) and nitric acid (0.1-4 M) with varying temperature (20 °C-45 °C).
To enable the deployment of molten salt reactor technology, the development of off-gas treatment systems and advanced monitoring tools capable of operating with high temperatures and radiation fields ...while delivering near real-time information is necessary. This study aims to fulfill this requirement and proposes laser-induced breakdown spectroscopy (LIBS) for monitoring molten salt aerosol streams. A sheath gas measuring method was developed to protect optical elements from aerosol particles and to ensure a relatively constant aerosol stream for measurement. An aqueous system was studied to demonstrate the utility of LIBS for monitoring possible fission products in an aerosol stream: Gd, Nd, and Sm up to 2000 parts per million (ppm). A calibration model was built using partial least squares (PLS) regression with five, six, and nine latent variables for Gd, Nd, and Sm, respectively. This calibration model successfully estimated the concentrations of three test samples, which were validated with inductively charged plasma optical emission spectroscopy measurements at a 99.9% confidence interval. To enhance these models, a genetic algorithm was used to filter the spectra before entering the PLS model, thereby limiting the spectral features being regressed to those with greater correlations to concentration. This allowed for the number of latent variables used in the PLS models to be reduced to four, three, and three for Gd, Nd, and Sm, respectively. Lastly, the genetic algorithm-filtered PLS models were used to predict the concentrations of the aerosol stream on a real-time dataset and resulted in a 73%, 18%, and 25% improvement in root mean squared error of prediction compared to the original PLS models developed.
Graphical Abstract
Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0–100 μg mL–1), europium (0–150 μg mL–1), samarium (0–250 μg mL–1), praseodymium (0–350 μg mL–1), ...neodymium (0–1000 μg mL–1), and HNO3 (2–4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet–visible–near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9–5.7% for the seven factors. The optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.
Partial least squares regression models were optimized for the quantification of trace elements including lanthanides (
e.g.
, Ce, Nd), transition metals (
e.g.
, Fe, Cr, Ni, Zr), post-transition ...metals (
e.g.
, Al, Pb), alkali/alkaline earth metals (
e.g.
, Na, Mg), metalloids (
e.g.
, Si, As) and nonmetals (
e.g.
, P) in uranium (U) by analyzing inductively coupled plasma-optical emission spectra. Chemical separations are commonly used to separate U from trace elements to enable highly reliable measurements by removing low lying spectral interferences from U in optical emission spectra. Here, an innovative multivariate regression approach was tested to circumvent the need for separations under relevant trace concentration ranges (20-5000 μg per g U). An I-optimal design was used to efficiently select training set samples, which were validated against several quality control samples with root mean square error of the prediction values ranging from 1% to 3% for 30 elements. The methodology was validated by the analysis of reference materials CRM 124-1 and CUP-2 and compared to partial least squares regression predictions from experimental values. The exemplar results indicate that the multivariate regression approach can account for covarying and overlapping spectral features better than standard software protocols. This unique approach provides a powerful tool for measuring trace elements in U without the time and waste associated with separations or matrix matched calibration standards and may be adapted to other systems.
Inductively coupled plasma-optical emission spectra were modeled with partial least squares regression models to quantify trace elements in uranium (20-5000 μg per g U) without chemical separations or matrix matched calibration standards.
Laser-induced breakdown spectroscopy (LIBS) was employed to characterize coatings on surrogate fuel particles. Tri-structural isotropic (TRISO) particles are a proposed nuclear fuel alternative for ...high temperature reactors. These particles are constructed of a ZrO
2
kernel (as a surrogate to uranium), surrounded by an inner pyrolytic carbon layer and are surrounded by an outer carbide layer (ZrC, presented here) to act as a barrier to fission products generated during nuclear reactions. These particles are embedded within a graphite compact and housed within the reactor core. Simply put, due to their robust nature, performing elemental analysis of these particles poses a challenge. Presented here, LIBS is explored as a method for characterizing elemental constituents of these particles, with the focus being on rapid elemental mapping and depth profiling. Different from traditional elemental analysis techniques (
e.g.
, inductively coupled plasma - based methods), LIBS is advantageous because it can directly analyze the sample surface and can detect light elements such as C and O, making it a viable technique for the analysis of small, multilayered particles as spatial elemental information is warranted in the production of these particles. In the work presented here, LIBS was successfully used for discerning small layers (30-50 μm), detecting the location of carbon and oxygen layers, providing fast 2-D mapping (<5 min per particle) and rapid depth profiling (10 s per particle).
Depiction of the analysis of a surrogate tri-structural isotropic (TRISO) particle
via
laser-induced breakdown spectroscopy (LIBS).
Aims
The aim of this study is to develop and test the applicability of a rapid in situ plant chemistry profiling technique to determine elemental composition of small-volume plant and soil samples ...obtained from a woody bioenergy crop species,
Populus trichocarpa
. Expanding the research tools available to characterize the nutrient element correlations among plant tissue types and soil depths is a critical need in the path of understanding productivity and adaptation of plants to variations in external abiotic and biotic factors and developing sustainable perennial bioenergy crops that are co-optimized for biomass valorization aboveground and carbon sequestration belowground.
Methods
Several plant root, stem, and soil samples were tested using laser-induced breakdown spectroscopy (LIBS) to evaluate the presence and distribution of nutrient elements. Samples were tested as collected and after being dried and cross sectioned to evaluate the effectiveness of using LIBS for in situ analysis on plant samples.
Results
The collected LIBS spectra show the elemental peaks were the same in both the as collected and prepared samples for roots and stems. Qualitative amounts of elements such as H, C, N, O, Li, Na, Mg, K, Ca, Fe, Al, and Si were able to be identified rapidly in raw samples.
Conclusion
Here we demonstrate suitability of LIBS in obtaining rapid, in situ, elemental distribution in plant and soil samples, utilizing only small sample volumes and minimal sample preparation. This demonstration opens up a new rapid phenotyping avenue necessary to fill the asymmetrical knowledge gaps in belowground performance of plant systems.
