Water O–H Stretching Raman Signature for Strong Acid Monitoring via Multivariate Analysis

Coherent anti-Stokes Raman scattering spectroscopy system for observation of water molecules in anion exchange membrane

Anion exchange membrane fuel cells (AEMFCs) provide one of the most feasible remedies to fuel cells' dependency on the dwindling Pt group catalysts. Nevertheless, AEMFCs still suffer reduced durability, which requires an in-depth understanding of their membranes. The low thermal endurance of the anion exchange membranes (AEMs) usually limits the direct application of powerful techniques, such as Raman spectroscopy. We sought to establish a system for coherent anti-Stokes Raman scattering (CARS) spectroscopy capable of taking measurements inside an AEM rapidly and accurately without photodamage. A 785 nm CARS system was newly developed to study the water species in an AEM (QPAF-4) located vertically in a fuel cell. From the results of water measurement in a QPAF-4 membrane, the OH-related region was deconvoluted into nine Gaussian peaks: Five H-bonded OH peaks, non-H-bonded OH, OH-, and two CH peaks. The H-bonded species increased with increasing relative humidity, but the other species remained constant. These results open unlimited possibilities for studying and comparing different AEMFCs, enabling more rapid technology optimization.

Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel

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.

Leveraging visible and near-infrared spectroelectrochemistry to calibrate a robust model for Vanadium(IV/V) in varying nitric acid and temperature levels

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.

Partial Least Squares, Experimental Design, and Near-Infrared Spectrophotometry for the Remote Quantification of Nitric Acid Concentration and Temperature

Near-infrared spectrophotometry and partial least squares regression (PLSR) were evaluated to create a pleasantly simple yet effective approach for measuring HNO₃ concentration with varying temperature levels. A training set, which covered HNO₃ 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 HNO₃ and 4.0% for temperature. The PLSR models built from spectra collected on static samples were validated against flow tests including HNO₃ 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 HNO₃ 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.

Pursuit of the Ultimate Regression Model for Samarium(III), Europium(III), and LiCl Using Laser-Induced Fluorescence, Design of Experiments, and a Genetic Algorithm for Feature Selection

Laser-induced fluorescence spectroscopy, Raman scattering, and partial least squares regression models were optimized for the quantification of samarium (0-150 μg mL^-1), europium (0-75 μg mL^-1), and lithium chloride (0.1-12 M) with a transformational preprocessing strategy. Selecting combinations of preprocessing methods to optimize the prediction performance of regression models is frequently a major bottleneck for chemometric analysis. Here, we propose an optimization tool using an innovative combination of optimal experimental designs for selecting preprocessing transformation and a genetic algorithm (GA) for feature selection. A D-optimal design containing 26 samples (i.e., combinations of preprocessing strategies) and a user-defined design (576 samples) did not statistically lower the root mean square error of the prediction (RMSEP). The greatest improvement in prediction performance was achieved when a GA was used for feature selection. This feature selection greatly lowered RMSEP statistics by an average of 53%, resulting in the top models with percent RMSEP values of 0.91, 3.5, and 2.1% for Sm(III), Eu(III), and LiCl, respectively. These results indicate that preprocessing corrections (e.g., scatter, scaling, noise, and baseline) alone cannot realize the optimal regression model; feature selection is a more crucial aspect to consider. This unique approach provides a powerful tool for approaching the true optimum prediction performance and can be applied to numerous fields of spectroscopy and chemometrics to rapidly construct models.

Microfluidic In-Situ Spectrophotometric Approaches to Tackle Actinides Analysis in Multiple Oxidation States

The study and development of present and future processes for the treatment/recycling of spent nuclear fuels require many steps, from design in the laboratory to setting up on an industrial scale. In all of these steps, analysis and instrumentation are key points. For scientific reasons (small-scale studies, control of phenomena, etc.) but also with regard to minimizing costs, risks, and waste, such developments are increasingly carried out on milli- or microfluidic devices. The logic is the same for the chemical analyses associated with their follow-up and interpretation. Due to this, over the last few years, opto-microfluidic analysis devices adapted to the monitoring of different processes (dissolution, liquid-liquid extraction, precipitation, etc.) have been increasingly designed and developed. In this work, we prove that photonic lab-on-a-chip (PhLoC) technology is fully suitable for all actinides concentration monitoring along the plutonium uranium refining extraction (plutonium, uranium, reduction, extraction, or Purex) process. Several PhLoC microfluidic platforms were specifically designed and used in different nuclear research and development (R&D) laboratories, to tackle actinides analysis in multiple oxidation states even in mixtures. The detection limits reached (tens of µmol·L^-1) are fully compliant with on-line process monitoring, whereas a range of analyzable concentrations of three orders of magnitude can be covered with less than 150 µL of analyte. Finally, this work confirms the possibility and the potential of coupling Raman and ultraviolet-visible (UV-Vis) spectroscopies at the microfluidic scale, opening the perspective of measuring very complex mixtures.

Design of Experiments, Chemometrics, and Raman Spectroscopy for the Quantification of Hydroxylammonium, Nitrate, and Nitric Acid

Selecting optimal combinations of preprocessing methods is a major holdup for chemometric analysis. The analyst decides which method(s) to apply to the data, frequently by highly subjective or inefficient means, such as user experience or trial and error. Here, we present a user-friendly method using optimal experimental designs for selecting preprocessing transformations. We applied this strategy to optimize partial least square regression (PLSR) analysis of Stokes Raman spectra to quantify hydroxylammonium (0-0.5 M), nitric acid (0-1 M), and total nitrate (0-1.5 M) concentrations. The best PLSR model chosen by a determinant (D)-optimal design comprising 26 samples (i.e., combinations of preprocessing methods) was compared with PLSR models built with no preprocessing, a user-selected preprocessing method (i.e., trial and error), and a user-defined design strategy (576 samples). The D-optimal selection strategy improved PLSR prediction performance by more than 50% compared with the raw data and reduced the number of combinations by more than 95.5%.

Simultaneous quantification of uranium(vi), samarium, nitric acid, and temperature with combined ensemble learning, laser fluorescence, and Raman scattering for real-time monitoring

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).

Water as a probe for pH measurement in individual particles using micro-Raman spectroscopy

Atmospheric aerosol acidity impacts numerous physicochemical processes, but the determination of particle pH remains a significant challenge due to the nonconservative nature of the H⁺ concentration ([H⁺]). Traditional measurements have difficulty in describing the practical state of an aerosol because they comprise chemical components or hypotheses that change the nature of the particles. In this work, we present a direct pH measurement that uses water as a general probe to detect [H⁺] in individual particles by micro-Raman spectroscopy. Containing the vibrational bands of ions and water influenced by ions, the spectra of hydrated ion were decomposed from the solution spectra as standard spectra by multivariate curve resolution analysis. Meanwhile, ratios of hydrated ions were calculated between the Raman spectra and standard spectra to evaluate concentration profiles of each ion. It demonstrated that good quantitative models between the ratio and concentration for all ions including H⁺ can be built with correlation coefficients (R²) higher than 0.95 for the solutions. The method was further applied to individual particle pH measurement. The pH value of sulfate aerosol particles was calculated, and the standard error was 0.09 using pH values calculated from the [HSO₄^-]/[SO₄^2-] as a reference. Furthermore, the applicability of the method was proven by detecting the pH value of chloride particles. Therefore, utilizing water, the most common substance, as the spectroscopic probe to measure [H⁺] without restriction of the ion system, this method has potential to measure the pH value of atmospheric particles with various compounds, although more work needs to be done to improve the sensitivity of the method.

Quantification of Raman-Interfering Polyoxoanions for Process Analysis: Comparison of Different Chemometric Models and a Demonstration on Real Hanford Waste

The Hanford site represents a complicated environmental remediation challenge, remaining from the production of nuclear weapons. Over 100 million gallons of liquid radioactive waste of unknown composition will be chemically processed and vitrified, but the varying chemical composition and highly radioactive nature of the waste preclude the implementation of more developed, offline technologies to determine the composition. The only practical approach to waste treatment will require the significant utilization of real-time, chemometric modeling approaches. Although chemometric approaches have been applied to the analysis of Hanford waste, the models developed were highly tank-specialized, and limited discussion was provided on how models fared with interfering signals. As the tank waste is largely composed of oxoanions, which tend to have interfering Raman spectra, the general question was posed as to what chemometric approach is best suited to accurately quantify analytes in the presence of interfering signals. This was carried out by examining the ability of classical least square (CLS), principal component regression (PCR), partial least square (PLS), and locally weighted regression (LWR) to quantify NO₃^- and CO₃^2- using their bands around 1050 cm^-1. For all samples, the PLS-based model was found to be the most efficient approach from a model building and application perspective.

Monitoring the Caustic Dissolution of Aluminum Alloy in a Radiochemical Hot Cell Using Raman Spectroscopy

Chemical processing of highly radioactive materials commonly takes place in heavily shielded hot cells. The remote, real-time monitoring of chemical processing streams via optical spectroscopic techniques in hot cells may be particularly useful. Here, we describe the implementation of Raman spectroscopy and chemometric analysis to monitor the dissolution of aluminum-clad targets containing irradiated aluminum-neptunium oxide cermet pellets in caustic solutions in a hot cell environment. Partial least squares regression analysis was used to generate calibration models to quantify the concentration of dissolved aluminum, nitrate, and hydroxide in solutions within the radiochemical hot cell. This work explored a systematic approach to optimize a matrix of calibration standards using a D-optimal experimental design. The Design of Experiments-based regression model, in comparison to more traditional analytical approaches, was found to be the more practical method for building calibration models, with fewer samples, to obtain informative analytical data from Raman spectra.

Review of on-line and near real-time spectroscopic monitoring of processes relevant to nuclear material management

Spectroscopic chemometric based on-line monitoring of used nuclear fuel (UNF) reprocessing solutions and characterization of legacy nuclear waste (LNW) stored at Hanford is discussed in this manuscript. Utilizing on-line and near real-time monitoring, as opposed to traditional off-line monitoring, can significantly reduce the cost, risk and improve the efficiency of characterizing UNF and LNW processing streams. Specifically, this manuscript will highlight the benefits of spectroscopy-based monitoring approaches, which generally include the ability to collect data non-destructively. Furthermore, significant literature precedence supports the use of various real-time analysis methods, including chemometric analysis, that enable near-instantaneous conversion of spectroscopic data into information useable by process operators. This approach can accurately quantify and qualify nuclear material in near-real time enabling immediate condition characterization and potential diversion detection within UNF reprocessing streams and LNW. The ability to be applied in a real reprocessing plant and in an actual Hanford waste tank/transfer pipe has been demonstrated by applying this technique to accurately quantify analytes in real UNF streams and LNW samples. The future development of spectroscopy-based on-line monitoring is also discussed in this manuscript.

Reimagining pH Measurement: Utilizing Raman Spectroscopy for Enhanced Accuracy in Phosphoric Acid Systems

Measurement of pH is an integral component of chemical studies and process control; however, traditional pH probes are difficult to utilize in harsh or complex chemical systems. Optical spectroscopy-based online monitoring offers a powerful and novel route for characterizing system parameters, such as pH, and is well adapted to deployment in harsh environments or chemically complex systems. Specifically, Raman spectroscopy combined with chemometric analysis can provide an improved method of online p[H⁺] measurement. Multivariate curve resolution (MCR) analysis of Raman spectra can be utilized to determine speciation as a function of p[H⁺], and the MCR scores assigned to each species can be used to calculate p[H⁺]. Subsequent chemometric modeling can be used to correlate spectral response to p[H⁺]. This was demonstrated with phosphoric acid, a chemical system known to challenge traditional pH probes. Raman spectra exhibit clear changes with pH due to changing speciation, and chemometric modeling can be successfully utilized to correlate those fingerprints to p[H⁺]. With the use of this approach, p[H⁺] of the phosphoric acid system can be accurately measured without foreknowledge of system conditions such as ionic strength.

Plutonium (IV) Quantification in Technologically Relevant Media Using Potentiometric Sensor Array

The quantification of plutonium in technological streams during spent nuclear fuel (SNF) reprocessing is an important practical task that has to be solved to ensure the safety of the process. Currently applied methods are tedious, time-consuming and can hardly be implemented in on-line mode. A fast and simple quantitative plutonium (IV) analysis using a potentiometric sensor array based on extracting agents is suggested in this study. The response of the set of specially designed PVC-plasticized membrane sensors can be related to plutonium content in solutions simulating real SNF-reprocessing media through multivariate regression modeling, providing 30% higher precision of plutonium quantification than optical spectroscopy.

Online Monitoring of Solutions Within Microfluidic Chips: Simultaneous Raman and UV-Vis Absorption Spectroscopies

Microfluidics is an appealing analytical tool in the global effort to close the nuclear fuel cycle. Using a microfluidic chip permits the analysis of greatly reduced sample volumes compared to what is necessary for traditional analytical methods. There is a commensurate reduction in disposal volume and cost. The development of novel sensors is necessary to take full advantage of the microchip configuration, where optical-spectroscopy-based approaches offer a powerful route to characterize chemical composition. This study uses simultaneously applied UV-vis and micro-Raman spectroscopies adapted to function on the microscale to analyze in situ both the Nd³⁺ (UV-vis-active) and HNO₃ (Raman-active) concentrations in the same sample. An adjustable translation platform was designed to hold the micro-Raman probe above and perpendicular to the chip face and the UV-vis probe in the plane of the chip. These complimentary spectral techniques when processed through multivariate partial least-squares (PLS) models gave an accurate picture of the widely varying solution concentrations as a function of time for each solution component. Solution matrix effects can drastically alter analyte signatures as measured by both UV-vis absorbance and Raman spectroscopy. PLS methods successfully modeled these spectral changes and accurately measured concentrations of components of interest within the microfluidic chip.

Visual tool for real-time monitoring of membrane fouling via Raman spectroscopy and process model based on principal component analysis

Membrane fouling, i.e. accumulation of unwanted material on the surface of the membrane is a significant problem in filtration processes since it commonly degrades membrane performance and increases operating costs. Therefore, the advantages of early stage monitoring and control of fouling are widely recognized. In this work, the potential of using Raman spectroscopy coupled to chemometrics in order to quantify degree of membrane fouling in real-time was investigated. The Raman data set collected from adsorption experiments with varying pHs and concentrations of model compound vanillin was used to develop a predictive model based on principal component analysis (PCA) for the quantification of the vanillin adsorbed on the membrane. The correspondence between the predicted concentrations based on the PCA model and actual measured concentrations of adsorbed vanillin was moderately good. The model developed was successful in monitoring both adsorption and desorption processes. Furthermore, the model was able to detect abnormally proceeding experiment based on differentiating PCA score and loading values. The results indicated that the presented approach of using Raman spectroscopy combined with a PCA model has potential for use in monitoring and control of fouling and cleaning in membrane processes.

Development and testing of a novel micro-Raman probe and application of calibration method for the quantitative analysis of microfluidic nitric acid streams

To simplify and improve the safety of reprocessing nuclear fuel, the Micro-Raman technique was applied at the microfluidic scale with a view toward the on-line spectroscopic measurement of radioactive solutions.

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. We present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of the spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO₃^-), total acid (H⁺), neodymium (Nd³⁺), sodium (Na⁺), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.

Multivariate Analysis for Quantification of Plutonium(IV) in Nitric Acid Based on Absorption Spectra

Development of more effective, reliable, and fast methods for monitoring process streams is a growing opportunity for analytical applications. Many fields can benefit from online monitoring, including the nuclear fuel cycle where improved methods for monitoring radioactive materials will facilitate maintenance of proper safeguards and ensure safe and efficient processing of materials. Online process monitoring with a focus on optical spectroscopy can provide a fast, nondestructive method for monitoring chemical species. However, identification and quantification of species can be hindered by the complexity of the solutions if bands overlap or show condition-dependent spectral features. Plutonium(IV) is one example of a species which displays significant spectral variation with changing nitric acid concentration. Single variate analysis (i.e., Beer's Law) is difficult to apply to the quantification of Pu(IV) unless the nitric acid concentration is known and separate calibration curves have been made for all possible acid strengths. Multivariate or chemometric analysis is an approach that allows for the accurate quantification of Pu(IV) without a priori knowledge of nitric acid concentration.

Incorporating spectroscopic on-line monitoring as a method of detection for a Lewis cell setup

A Lewis cell was designed and constructed for investigating solvent extraction systems by spectrophotometrically monitoring both the organic and aqueous phases in real time. This new Lewis cell was tested and shown to perform well compared to other previously reported Lewis cell designs. The advantage of the new design is that the spectroscopic measurement allows determination of not only metal ion concentrations, but also information regarding chemical speciation - information not available with previous Lewis cell designs. For convenience, the new Lewis cell design was dubbed COSMOFLEX (COntinuous Spectroscopic MOnitoring of Forrest's Liquid-liquid EXtraction cell). After construction performance testing was done for establishing the ideal stir speed range, UV-Vis measured concentration and D value determination. Each one of these tests was satisfactorily passed.