Maintaining pH in an operable range is key in many industrial processes, where automating pH correction and control can enable significant advances in process efficiency. One such process is solvent extraction. In solvent extraction, the pH can directly influence separation by altering the extraction of different species. Solvent extraction procedures for separating components of used nuclear fuel, such as TALSPEAK and Advanced TALSPEAK, are examples of this. In these processes, the extraction of trivalent lanthanides and actinides is pH-dependent, with more acidic conditions causing greater metal extraction. However, the pH can be challenging to control. Here, automated pH control has been demonstrated using robust, Raman-based pH measurement that informed the control software connected to programmable logic controllers (PLCs). The use of Raman spectroscopy paired with a chemometric model has been applied to the feed solutions for TALSPEAK and Advanced TALSPEAK processes and is used to demonstrate effective, robust, and near instantaneous pH measurement. The result of this measurement was then fed into a PLC instrument that had been programmed to maintain a constant pH. When the pH was purposely perturbed by adding an acid or base, the PLC was able to automatically correct the pH and, consequently, control the desired extraction of metal ions. The analytical approach and performance to automated, real-time process control are described here.

Nuclear energy is a promising low-carbon energy candidate to meet the increased demand for green energy, where the integration of fuel recycling can have significant benefits for material usage and waste reduction. Utilizing in situ monitoring tools can provide ample opportunities to better control and safeguard nuclear material recycle processes while also offering knowledge and insight into real-time solution properties. The simultaneous measurement of analytical targets in multiple process locations can enable real-time mass balance and material accountancy calculations. This is demonstrated here with a mass balance study of Nd3+ on countercurrent aqueous/organic metal extraction within a single centrifugal contactor. The Nd3+ concentration was simultaneously monitored at the inlets and outlets of both aqueous and organic phases using a visible absorbance detector that allowed for the simultaneous measurement of up to six locations. The Nd3+ concentration was calculated by using chemical data science algorithms, where model training sets were collected on a single track of the detector. The discussion includes addressing the challenges of using a model collected on a single track and applying it as a model across the other tracks on the detector. Each track of the detector corresponds to one measurement location on the contactor. The difference in the integrated moles of Nd3+ between the inlet and outlet at the end of the experiment was near zero, indicating that the mass balance of this experiment was maintained. Overall, the online spectroscopic monitoring was able to follow changing solution conditions and accurately measure the concentration of Nd3+ in different locations within the contactor system.

Rare earth elements (REEs) are essential to most renewable energy technologies. Unfortunately, as we transition to sustainable energy production, the demand for REEs rapidly grows well beyond the current rates of production. As a result, novel means of efficient, scalable, and easily adaptable methods for processing primary and recycled feedstocks are needed. Development and integration of sensors for highly selective in-line monitoring can support more efficient design and testing of such novel separation processes as well as more cost-effective deployment of those separation flowsheets. Work here will explore the application of fluorescence spectroscopy, a highly sensitive and selective technique, to quantify multiple lanthanides in complex mixtures, including known interferents or quenching agents. Results include identification of the optimal excitation wavelength and the limit of detection of various rare earth elements, as well as the performance of data-science-based quantification approaches in streams where "unknowns" are present. Overall, the data science tools in conjunction with optical sensor data were able to quantify analytes in the presence of other lanthanides anticipated to be in the actual industrial stream. Here, we include the characterization of lanthanides in a microfluidic device similar to those used in new process development. This study demonstrates the capability of utilizing fluorescence spectroscopy to quantify analytes in a complicated solution matrix, suggesting this as a successful approach for in-line monitoring to optimize the separation efficiency in an industrial stream.

Nuclear fission is an energy source that can provide consistent power with very low associated carbon emissions. However, management of the used nuclear fuel is an important aspect of the application of nuclear power. Recycling of useful components from used fuel is an attractive option, but this involves chemical processing of the fuel. Possible chemical separation technologies that might be used in this regard are sensitive to solution pH. Raman spectroscopy is a promising technique for monitoring the pH of solutions in real time. Classical pH probes are too fragile to be used in the harsh environments encountered in nuclear fuel processing. Raman probes are robust and can withstand these harsh environments to track pH. Coupled with chemometric analysis, the demonstration of the use of Raman spectroscopy to track and predict the pH in carboxylate-buffered systems is made possible. Utilizing this spectroscopy in conjunction with Programmable Logic Controllers mimics industrial control systems used in many modern industrial settings. This showcases a pragmatic approach toward leveraging Raman spectroscopy and chemometric model outputs as inputs for a real-time control system. The model to predict pH created by chemometrics proved to be successful in tracking pH. The optimal pH for TALSPEAK extraction of lanthanides and actinides from aqueous solution is known to proceed in a narrow pH range of around pH = 2.8 ± 0.1. This study uses Raman optical monitoring and automated control to return and maintain solution pH within this range after acid or base perturbations move the solution pH well outside this region. Root-mean-square errors show that pH changes measured using Raman spectroscopy on the batch process solution are reliably measured and used to automatically correct and maintain solution pH. Measurement of solution pH tracks favorably with electrochemical pH probe comparison measurements. As a result, the ability to showcase Raman spectroscopy paired with chemometrics analysis acts as a durable, better alternative data source compared to traditional pH probes to optimize the separation efficiency in the used nuclear fuel processing.

This Review summarizes a range of analytical techniques that can be used to detect, quantify, and/or distinguish between isotopes of iodine (e.g., long-lived 129I, short-lived 131I, stable 127I). One reason this is of interest is that understanding potential radioiodine release from nuclear processes is crucial to prevent environmental contamination and to protect human health as it can incorporate into the thyroid leading to cancer. It is also of interest for evaluating iodine retention performances of next-generation iodine off-gas capture materials and long-term waste forms for immobilizing radioiodine for disposal in geologic repositories. Depending upon the form of iodine (e.g., molecules, elemental, and ionic) and the matter state (i.e., solid, liquid, and gaseous), the available options can vary. In addition, several other key parameters vary between the methods discussed herein, including the destructive vs nondestructive nature of the measurement process (including in situ vs ex situ measurement options), the analytical data collection times, and the amount of sample required for analysis.

Optical spectroscopy is a valuable tool for online monitoring of a variety of processes. Ultraviolet-visible (UV-vis) spectroscopy can monitor the concentration of analytes as well as identify the speciation and oxidation state. However, it can be difficult or impossible to employ UV-vis-based sensors in chemical systems that are very dark (i.e., have a high optical density), requiring exceedingly short path lengths (for transmission approaches) or an effective means of backscattering (for reflectance approaches). Examples of processes that produce highly absorbing solutions and that would benefit significantly from the diagnostic potential of optical sensors include used nuclear fuel recycling and molten salt systems with high concentrations of dissolved uranium. Utilizing an attenuated total reflectance (ATR) UV-vis approach can overcome these challenges and allow for the measurement of solutions orders of magnitude more concentrated than transmission UV-vis. However, determining ideal sensor specifications for various processes can be time-consuming and expensive. Here, we evaluate the ability of a novel ATR-UV-vis probe to measure very concentrated solutions of Co(II) and Ni(II) nitrate as well as organic dyes (methylene blue, acid red 1, and crystal violet). This sensor design provides a modular method for exploring possible "path lengths" by altering the length of the ATR fiber that was submerged within solution during spectral measurements. Measurements within the ATR sensor cell were compared to measurements gathered by transmission UV-vis of samples within a commercially available 1 cm optical cuvette. The ATR-UV-vis probe was capable of measuring absorbance of solutions with a chromophore concentration 600 times greater than that in the 1 cm cuvette. Advanced data analysis in the form of multivariate curve resolution (MCR) was used to analyze the speciation of methylene blue over a large concentration range. The application of this novel ATR-UV-vis probe to the investigation of dark solutions is a promising avenue for use in online monitoring of nuclear processes.

Microfluidic devices (MFDs) offer customizable, low-cost, and low-waste platforms for performing chemical analyses. Optical spectroscopy techniques provide nondestructive monitoring of small sample volumes within microfluidic channels. Optical spectroscopy can probe speciation, oxidation state, and concentration of analytes as well as detect counterions and provide information about matrix composition. Here, ultraviolet-visible (UV-vis) absorbance, near-infrared (NIR) absorbance, and Raman spectroscopy are utilized on a custom poly(methyl methacrylate) (PMMA) MFD for the detection of three lanthanide nitrates in solution. Absorbance spectroscopies are conducted across three pathlengths using three portions of a contiguous channel within the MFD. Univariate and chemometric multivariate modeling, specifically Beer's law regression and principal component regression (PCR), respectively, are utilized to quantify the three lanthanides and the nitrate counterion. Models are composed of spectra from one or multiple pathlengths. Models are also constructed from multiblock spectra composed of UV-vis, NIR, and Raman spectra at one or multiple pathlengths. Root-mean-square errors (RMSE), limit of detection (LOD), and residual predictive deviation (RPD) values are compared for univariate, multivariate, multi-pathlength, and multiblock models. Univariate modeling produces acceptable results for analytes with a simple signal, such as samarium cations, producing an LOD of 5.49 mM. Multivariate and multiblock models produce enhanced quantification for analytes that experience spectral overlap and interfering nonanalyte signals, such as holmium, which had an LOD reduction from 7.21 mM for the univariate model down to 3.96 mM for the multiblock model. Multi-pathlength models are developed that maintain model errors in line with single-pathlength models. Multi-pathlength models have RPDs from 9.18 to 46.4, while incorporating absorbance spectra collected at optical paths of up to 10-fold difference in length.

Spectroelectrochemistry (SEC) is a powerful technique that enables a variety of redox properties to be studied, including formal potential (Eo ), thermodynamic values (Delta G, Delta H, Delta S), diffusion coefficient (D), electron transfer stoichiometry (n), and others. SEC requires an electrode which light can pass through while maintaining sufficient electrical conductivity. This has been traditionally composed of metal or metal oxide films atop transparent substrates like glass, quartz, or metallic mesh. Robust electrode materials like boron-doped diamond (BDD) could help expand the environments in which SEC can be performed, but most designs are limited to thin films (similar to 100-200 nm) on transparent substrates less resilient than free-standing BDD. This work presents a free-standing BDD grid electrode (G-BDD) for fundamental SEC measurements, using the well-characterized Fe(CN)6 3-/4- redox couple as proof-of-concept. With a combination of cyclic voltammetry (CV), thin-layer SEC, and chronoabsorptometry, several of the redox properties mentioned above were calculated and compared. For Eo ', n, and D, similar results were obtained when comparing the CV [Eo ' = +0.279 (+/- 0.002) V vs Ag/AgCl; n = 0.97; D = 4.1 x 10-6 cm2s-1] and SEC [Eo ' = +0.278 (+/- 0.001) V vs Ag/AgCl; n = 0.91; D = 5.2 x 10-6 cm2s-1] techniques. Both values align with what has been previously reported. To calculate D from the SEC data, modification of the classical equation used in chronoabsorptometry was required to accommodate the G-BDD electrode geometry. Overall, this work expands on the applicability of SEC techniques and BDD as a versatile electrode material.

Online and real-time analysis of a chemical process is a major analytical challenge that can drastically change the way the chemical industry or chemical research operates. With in situ analyses, a new and powerful understanding of chemistry can be gained; however, building robust tools for long-term monitoring faces many challenges, including compensating for instrument drift, instrument replacement, and sensor or probe replacement. Accounting for these changes by recollecting calibration data and rebuilding quantification models can be costly and time-consuming. Here, methods to overcome these challenges are demonstrated with an application of Raman spectroscopy to monitoring hydrogen isotopes with varied speciation within dynamic gas streams. Specifically, chemical data science tools such as chemometric modeling are leveraged along with several examples of calibration transfer approaches. Furthermore, the optimization of instrument and sensor cell parameters for targeted gas-phase analyses is discussed. While the particular focus on hydrogen is highly beneficial within the nuclear energy sector, mechanisms built and demonstrated here are widely applicable to optical spectroscopy monitoring in numerous other chemical systems that can be leveraged in other processes.

During process monitoring applications, referenced optical spectroscopy, such as absorbance spectroscopy, can suffer from environmental and instrumental fluctuations that alter the intensity of irradiance reaching the spectrometer's detector at each detected frequency. Temperature, vibration, light source aging, instrument damage, detector aging, detector registry shifts, sampling cell degradation, and similar perturbations create situations in which a previously collected reference spectrum may no longer be valid for the current state of the system. This can lead to the calculation of poor-quality absorbance spectra that are unsuitable for qualitative or quantitative analysis based on prior calibration models. The use of single-beam spectra in the creation of multivariate calibration models circumvents the need for collecting and maintaining a stable reference spectrum throughout an ongoing chemical process. However, unlike absorbance spectra, which typically have a zero baseline, single-beam spectra contain a high background signal relative to an analyte signal, and they may also contain intense peaks from the light source. Here, multivariate principal component analysis (PCA) and partial least squares (PLS) regression models are built using single-beam and absorbance spectra to compare the efficacy of both types of spectra for qualitative and quantitative analyses of lanthanide solutions. A multileg fiber optic UV-visible spectrometer is utilized to collect samples under three distinct wavelength registries in three unique sampling cells and under lighting conditions spanning 0.2 to 2.0 relative transmittance. Under these conditions, single-beam spectral PCA models produced enhanced discrimination between sampling conditions, allowing spectra to be grouped by the instrumental conditions under which they were collected. Absorbance and single-beam PLS models produced equivalent quantitations of the lanthanide concentrations.

The methodology and mathematical treatment of several classic multivariate methods for the analysis of spectroscopic data is demonstrated in a straightforward way that can be used as a basis for teaching an undergraduate introductory course on chemometric analysis. The multivariate techniques of classical least-squares (CLS), principal component regression (PCR), and partial least-squares (PLS), as well as the univariate Beer's law method have been described and compared, building students' understanding by starting with the univariate method and progressing step by step into the multivariate methods. Equations for the production of regression vectors from training set spectral data are described and their use demonstrated for the prediction of constituent concentrations on a separate validation set of spectra. Extreme care is taken to ensure consistency in variable formatting of data matrices. This provides a key foundation to understand how spectral data are manipulated using these different mathematical approaches for building quantitative regression models. Each method is applied to a real-world data set, and the results are discussed to show students the types of information that can be gleaned from each method. A training set comprising 20 infrared absorbance spectra containing 3 constituents (benzene, polystyrene, and gasoline) of known composition are used to demonstrate the matrix operations for each regression method. A separate set of 12 real-world napalm samples (containing benzene, polystyrene, and gasoline) are used as a validation set to demonstrate the ability to utilize the regression models on an unknown data set. A toolbox (PNNL Chemometric Toolbox) written in MATLAB language is supplied in the Supporting Information and can be used as a companion for understanding the development and deployment of the chemometric algorithms described in this paper. The data sets of the infrared spectra are also supplied, allowing users to build and inspect the chemometric models on their own. Finally, the Toolbox includes scripts to assist users in loading their own data sets into MATLAB and performing CLS, PCR, and PLS on their data.

Harsh environments represent a unique opportunity to explore new frontiers in chemistry while developing novel tools to meet global needs. Exploring the chemistry of uranium within molten salts is a key example. Actinide chemistry within the highly ionic environment of a molten salt is poorly understood, particularly in the presence of common salt impurities or without active oxidation state control. Delving into this chemistry can provide new insight into actinide and f-electron interactions. Furthermore, expanding our chemical knowledge can also enable advances in the deployment of molten salt reactors or molten salt recycle schemes. Both molten salt applications aim toward providing green and reliable energy as well as critical materials for the world. Here, the utilization of visible absorbance and Raman spectroscopies to understand and quantify U within chloride-based salt eutectics is discussed. Furthermore, machine learning techniques in the form of chemometric modeling are developed and described, providing advanced analytical tools to quantify and characterize the U present. These tools are then leveraged to monitor and explore the dynamic fundamental chemistry of U within chloride-based salt melts without active oxidation state control.

In situ and real-time analysis of chemical systems, or online monitoring, has numerous benefits in all fields of chemistry. A common challenge can be found in matrix effects, where the addition of a new chemical species causes chemical interactions and changes the fingerprints of other chemical species in the system. This is demonstrated here by looking at the Raman and visible spectra of the uranyl ion within combined nitric acid and hydrofluoric acid media. This system is not only highly important to nuclear energy, a green and reliable option for energy portfolios, but also provides a clear chemistry example that can be applied to other chemical systems. The application of optical spectroscopy is discussed, along with the application and comparison of both multivariate curve resolution and HypSpec to deconvolute and understand speciation. Finally, the use of chemical data science in the form of chemometric modeling is used to demonstrate robust quantification of uranium within a complex chemical system where potential matrix effects are not known a priori.

Carbon capture represents a key pathway to meeting climate change mitigation goals. Powerful next-generation solvent-based capture processes are under development by many researchers, but optimization and testing would be significantly aided by integrating in situ monitoring capability. Further, real-time water analysis in water-lean solvents offers the potential to maintain their water balance in operation. To explore data acquisition techniques in depth for this purpose, Raman spectra of CO2, H2O, and a single-component water-lean solvent, N-(2-ethoxyethyl)-3-morpholinopropan-1-amine (2-EEMPA) were collected at different CO2 and H2O concentrations using an in situ Raman cell. The quantification of CO2 and H2O loadings in 2-EEMPA was done by principal component regression and partial least squares methods with analysis of uncertainties. We conclude with discussions on how this simultaneous online analysis method to quantify CO2 and H2O loadings can be an important tool to enable the optimal efficiency of water-lean CO2 solvents while also maintaining the critical water balance under operating conditions relevant to post-combustion CO2 capture.

Real-time and in situ process monitoring is a powerful tool that can empower operators of hazardous processes to better understand and control their chemical systems without increased risk to themselves. However, the application of monitoring techniques to complex chemical processes can face challenges. An example of this is the application of optical spectroscopy, otherwise capable of providing detailed chemical composition information, to processes exhibiting variable turbidity. Here, details on a novel combined Raman spectroscopy and turbidimetry probe are discussed, which advances current technology to enable flexible and robust in situ monitoring of a flowing process stream. Furthermore, the analytical approach to accurately account for both Raman signal and turbidity while quantifying chemical targets is detailed. This new approach allows for accurate analysis without requiring assumptions of stable process chemistry, which may be unlikely in applications such as waste cleanup. Through leveraging Raman and turbidity data simultaneously collected from the combined probe within chemometric models, accurate quantification of multiple chemical targets can be achieved under conditions of variable concentrations and turbidity.

Increasing lanthanide demand to support clean energy goals drives the need to develop more efficient approaches to separate adjacent lanthanides. Most approaches for lanthanide separations are not very selective and are based on small differences in lanthanide ionic radii. Concentrated potassium carbonate media has shown some potential to enable oxidation of praseodymium (Pr) and terbium (Tb) to their tetravalent states, which could ultimately enable a separation based on differences in oxidation states, but very little is known regarding the system’s chemistry. This work completes a detailed examination of cerium (Ce) redox chemistry in concentrated carbonate media to support the development of Pr and Tb oxidation studies. The half-wave potential (E 1/2 ) of the Ce(III)/(IV) redox couple is evaluated under various solution conditions and computational modeling of carbonate coordination environments is discussed. Cyclic voltammetry shows higher carbonate concentrations and temperatures can lower the potential required to oxidize Ce(III) by 54 mV (3.5 to 5.5 M) and 39 mV (from 10 °C to 70 °C). Chronoabsorptometry shows Ce(III) and Ce(IV) carbonate complexes are chemically stable and reversible. Computational modelling suggests the most likely coordination environment for the Ce(IV) complex is Ce(CO 3 ) 4 (OH) 5− which is less entropically favorable than the lowest energy Ce(III) complex, Ce(CO 3 ) 4 5− .

Experiments were conducted to investigate a passive production mechanism for the world’s most energy intensive commodity, ammonia. A novel method, gamma catalyzed ammonia production at ambient conditions, was investigated. Ammonia is currently produced through the highly energy intensive Haber-Fritz process, which requires an operation pressure of 400 atmosphere and 600 degrees Celsius. Due to the high demand and need for ammonia, the Haber-Bosch process consumes 25% all energy produced globally. Reported herein was an attempt to produce ammonia at ambient temperature (20 C) and ambient pressure (1 atm), through a novel process developed at PNNL, gamma driven catalysis of ammonia. Although the measurements of the ammonia production suggest wild success, reports in the literature by Gao et.al. suggest an experimental positive bias in the results. To rule out the potential positive bias, multiple additional production campaigns would be needed to with an alternate analysis technique such as ion chromatography, as suggested by Gao et.al. Unfortunately, due to this late determination of potential positive bias, the results of this study remain inconclusive to the feasibility of gamma driven catalysis of ammonia and more work is needed to describe the chemical evolution with time. The results are a first step and demonstrate that gamma-catalyst mediated reactions are possible. This represents a key opportunity to explore the fundamental chemistry of high band gap catalysts that can change the paradigm of radiation, transforming it from a waste to a valuable energy source.

Processing nuclear waste from sites such as Hanford is a significant environmental cleanup need while being a significant logistical challenge. Integration of process monitoring tools, which can provide in situ and real-time feedback about the process, can significantly alleviate needs to collect grab samples for process control and product characterization. Raman spectroscopy paired with chemometric analysis is one process monitoring tool that can provide chemical composition information on a large number of chemical targets in nuclear waste streams. However, methods to improve limits of detection as well as drop uncertainty in quantification are needed. Optimizing instrument specifications can achieve this; here, this is demonstrated by comparing limits of detection for key analytes when using Raman systems with 671, 532, and 405 nm excitation wavelengths. Generally, limits of detection decrease (allowing the measurement of lower salt concentrations) with decreasing wavelength. Similarly, data collection times and averaging were optimized. Finally, multiple chemometric modeling approaches were leveraged, including multiblock methods that combined data from all three Raman systems to simultaneously quantify targets with improved sensitivity.