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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., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

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., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Online spectroscopic measurements can be used to provide unique insight into complex chemical systems, enabling new understanding and optimization of chemical processes. A key example of this is discussed here with the monitoring of pH of various acid systems in real-time. In this work the acids used in multiple chemical separations processes, such as TALSPEAK (Trivalent Actinide-Lanthanide Separation by Phosphorus reagent Extraction from Aqueous Komplexes) and oxalate precipitation, were characterized. Raman spectroscopy, a robust optical approach that can be integrated in corrosive processes, was used to follow the unique fingerprints of the various protonated and deprotonated acid species. This data was analyzed using a hierarchical modeling approach to build a consolidated model scheme using optical fingerprints from all weak acids to measure pH associated with any of the weak acid systems studied here. Validation of system performance included utilizing Raman spectroscopy under dynamic flow conditions to monitor solution pH under changing process conditions in-line. Overall, the Raman based approach provided accurate analysis of weak acid solution pH.

Optical spectroscopy is a powerful characterization tool with applications ranging from fundamental studies to real-time process monitoring. However, it can be difficult to apply to complex samples that contain interfering analytes which are common in processing streams. Multivariate (chemometric) analysis has been examined for providing selectivity and accuracy to the analysis of optical spectra and expanding its potential applications. Here we will discuss chemometric modeling with an in-depth comparison to more simplistic analysis approaches and outline how chemometric modeling works while exploring the limits on modeling accuracy. Understanding the limitations of the chemometric model can provide better analytical assessment regarding the accuracy and precision of the analytical result. This will be explored in the context of UV-Vis absorbance of neodymium (Nd 3+ ) in the presence of interferents, erbium (Er 3+ ) and copper (Cu 2+ ) under conditions simulating the liquid-liquid extraction approach used to recycle plutonium (Pu) and uranium (U) in used nuclear fuel worldwide. The selected chemometric model, partial least squares regression, accurately quantifies Nd 3+ with a low percentage error in the presence of interfering analytes and even under conditions that the training set does not describe.

Complex chemical systems that exhibit varied and matrix-dependent speciation are notoriously difficult to monitor and characterize online and in real-time. Optical spectroscopy is an ideal tool for in situ characterization of chemical species that can enable quantification as well as species identification. Chemometric modeling, a multivariate method, has been successfully paired with optical spectroscopy to enable measurement of analyte concentrations even in complex solutions where univariate methods such as Beer's law analysis fail. Here, Raman spectroscopy is used to quantify the concentration of phosphoric acid and its three deprotonated forms during a titration. In this system, univariate approaches would be difficult to apply due to multiple species being present simultaneously within the solution as the pH is varied. Locally weighted regression (LWR) modeling was used to determine phosphate concentration from spectral signature. LWR results, in tandem with multivariate curve resolution modeling, provide a direct measurement of the concentration of each phosphate species using only the Raman signal. Furthermore, results are presented within the context of fundamental solution chemistry, including Pitzer equations, to compensate for activity coefficients and nonidealities associated with high ionic strength systems.

Microfluidics have many potential applications including characterization of chemical processes on a reduced scale, spanning the study of reaction kinetics using on-chip liquid-liquid extractions, sample pretreatment to simplify off-chip analysis, and for portable spectroscopic analyses. The use of in situ characterization of process streams from laboratory-scale and microscale experiments on the same chemical system can provide comprehensive understanding and in-depth analysis of any similarities or differences between process conditions at different scales. A well-characterized extraction of Nd(NO 3 ) 3 from an aqueous phase of varying NO 3 - (aq) concentration with tributyl phosphate (TBP) in dodecane was the focus of this microscale study and was compared to an earlier laboratory-scale study utilizing counter current extraction equipment. Here, we verify that this same extraction process can be followed on the microscale using spectroscopic methods adapted for microfluidic measurement. Concentration of Nd (based on UV-vis) and nitrate (based on Raman) was chemometrically measured during the flow experiment, and resulting data were used to determine the distribution ratio for Nd. Extraction distributions measured on the microscale were compared favorably with those determined on the laboratory scale in the earlier study. Both micro-Raman and micro-UV-vis spectroscopy can be used to determine fundamental parameters with significantly reduced sample size as compared to traditional laboratory-scale approaches. This leads naturally to time, cost, and waste reductions.

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.

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 Nd 3+ (UV-vis-active) and HNO 3 (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.

Microfluidic devices provide ideal environments to study solvent extraction. When droplets form and generate plug flow down the microfluidic channel, the device acts as a microreactor in which the kinetics of chemical reactions and interfacial transfer can be examined. Here, we present a methodology that combines chemometric analysis with online micro-Raman spectroscopy to monitor biphasic extractions within a microfluidic device. Among the many benefits of microreactors is the ability to maintain small sample volumes, which is especially important when studying solvent extraction in harsh environments, such as in separations related to the nuclear fuel cycle. In solvent extraction, the efficiency of the process depends on complex formation and rates of transfer in biphasic systems. Thus, it is important to understand the kinetic parameters in an extraction system to maintain a high efficiency and effectivity of the process. This monitoring provided concentration measurements in both organic and aqueous plugs as they were pumped through the microfluidic channel. The biphasic system studied was comprised of HNO 3 as the aqueous phase and 30% (v/v) tributyl phosphate in n-dodecane comprised the organic phase, which simulated the plutonium uranium reduction extraction (PUREX) process. Using pre-equilibrated solutions (post extraction), the validity of the technique and methodology is illustrated. Following this validation, solutions that were not equilibrated were examined and the kinetics of interfacial mass transfer within the biphasic system were established. Kinetic results of extraction were compared to kinetics already determined on a macro scale to prove the efficacy of the technique.

To simplify and improve the safety of reprocessing used nuclear fuel, an initial assessment was made of Raman microscopy applied to microfluidic volumes with a view toward the on-line spectroscopic measurement of highly radioactive solutions. This study compares a microscopic Raman probe (excitation focal point diameter 70 μm) to a larger, well studied probe (excitation focal point diameter 125 μm) used in prior investigations. This was done by chemometrically modeling and predicting concentrations of HNO 3 solutions (0 M to 8 M) as they flowed through microfluidic cells based upon spectra from each probe. Spectra recorded for each probe using the same static HNO 3 solution set contained in 2 dram glass vials were used as training sets to produce models for the respective probes. Modeling required baseline, normalization and smoothing preprocessing to compensate for a reduced path length between the static glass vial training set (4 cm) and the reduced path length flow cell (1 cm), wide ranging solution concentrations, and the associated non-linear spectral changes, and abrupt and uneven concentration changes of flowing solutions. The micro-Raman probe is able to produce spectra that may be analyzed chemometrically to accurately predict the concentration of flowing HNO 3 solutions down to microliter volumes. Based upon RMSECV and RMSEP modeling statistics concentration predictions of the micro-Raman probe are comparable to those obtained for a macro-Raman on identical samples.

Microfluidic devices are a growing field with significant potential for applications to small scale processing of solutions. Much like large scale processing, fast, reliable, and cost-effective means of monitoring streams during processing are needed. Here we apply a novel micro-Raman probe to the online monitoring of streams within a microfluidic device. For either macro- or microscale process monitoring via spectroscopic response, interfering or confounded bands can obfuscate results. By utilizing chemometric analysis, a form of multivariate analysis, species can be accurately quantified in solution despite the presence of overlapping or confounding spectroscopic bands. This is demonstrated on solutions of HNO 3 and NaNO 3 within microflow and microfluidic devices.