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

Dissecting the Photochemical Reactivity of Metal Ions during Atmospheric Nitrate Transformations on Photoactive Mineral Dust

Dissecting the photochemical reactivity of metal ions is a significant contribution to understanding secondary pollutant formation, as they have a role to be reckoned with atmospheric chemistry. However, their photochemical reactivity has received limited attention within the active nitrogen cycle, particularly at the gas-solid interface. In this study, we delve into the contribution of magnesium ion (Mg2+) and ferric ion (Fe3+) to nitrate decomposition on the surface of photoactive mineral dust. Under simulated sunlight irradiation, the observed NOX production rate differs by an order of magnitude in the presence of Mg2+ (6.02 × 10-10 mol s-1) and Fe3+ (2.07 × 10-11 mol s-1). The markedly decreased fluorescence lifetime induced by Mg2+ and the change in the valence of Fe3+ revealed that Mg2+ and Fe3+ significantly affect the concentration of nitrate decomposition products by distinct photochemical reactivity with photogenerated electrons. Mg2+ promotes NOX production by accelerating charge transfer, while Fe3+ hinders nitrate decomposition by engaging in a redox cyclic reaction with Fe2+ to consume photogenerated carriers continuously. Furthermore, when Fe3+ coexists with other metal ions (e.g., Mg2+, Ca2+, Na+, and K+) and surpasses a proportion of approximately 12%, the photochemical reactivity of Fe3+ tends to be dominant in depleting photogenerated electrons and suppressing nitrate decomposition. Conversely, below this threshold, the released NOX concentration increases sharply as the proportion of Fe3+ decreases. This research offers valuable insights into the role of metal ions in nitrate transformation and the generation of reactive nitrogen species, contributing to a deep understanding of atmospheric photochemical reactions.

Quantification of Hydrogen Isotopes Utilizing Raman Spectroscopy Paired with Chemometric Analysis for Application across Multiple Systems

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.

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.

In Situ Raman Methodology for Online Analysis of CO2 and H2O Loadings in a Water-Lean Solvent for CO2 Capture

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.

Quantifying Dense Multicomponent Slurries with In-Line ATR-FTIR and Raman Spectroscopies: A Hanford Case Study

The multiphase nature of slurries can make them difficult to process and monitor in real time. For example, the nuclear waste slurries present at the Hanford site in Washington State are multicomponent, multiphase, and inhomogeneous. Current analytical techniques for analyzing radioactive waste at Hanford rely on laboratory results from an on-site analytical laboratory, which can delay processing speed and create exposure risks for workers. However, in-line probes can provide an alternative route to collect the necessary composition information. In the present work, Raman spectroscopy and attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy are tested on simulants of nuclear waste slurries containing up to 23.2 wt % solids. We observe ATR-FTIR spectroscopy to be effective in measuring the solution phase of the studied slurry systems (3.52% mean percent error), while Raman spectroscopy provides information about the suspended solids in the slurry system (18.21% mean percent error). In-line measurement of multicomponent solids typical of nuclear waste processing has been previously unreported. The composition of both the solution and solid phases is vital in ensuring stable glass formulation and effective disposal of nuclear waste at Hanford. Raman and ATR-FTIR spectroscopies can provide a safer and faster alternative for acquiring compositional information on nuclear waste slurries.

Research on the Comprehensive Evaluation Method for the Automatic Recognition of Raman Spectrum under Multidimensional Constraint

Raman spectrum contains abundant substance information with fingerprint characteristics. However, due to the huge variety of substances and their complex characteristic information, it is difficult to recognize the Raman spectrum accurately. Starting from dimensions like the Raman shift, the relative peak intensity, and the overall hit ratio of characteristic peaks, we extracted and recognized the characteristics in the Raman spectrum and analyzed these characteristics from local and global perspectives and then proposed a comprehensive evaluation method for the recognition of Raman spectrum on the basis of the data fusion of the recognition results under multidimensional constraint. Based on the common spectrum database of the normal Raman and surface-enhanced Raman of thousands of substances, we analyzed the performance of the evaluation method. It shows that even for the identification of spectra from instruments of low technical specifications, the automatic recognition rate of the sample can reach 98% and above, a great improvement compared with that of the common identification algorithms, which proves the effectiveness of the comprehensive evaluation method under multidimensional constraint.

Combined Raman and Turbidity Probe for Real-Time Analysis of Variable Turbidity Streams

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.