Uranium Single Particle Analysis for Simultaneous Fluorine and Uranium Isotopic Determinations via Laser-Induced Breakdown Spectroscopy/Laser Ablation-Multicollector-Inductively Coupled Plasma-Mass Spectrometry.

Uranyl fluoride (UO2F2) particles (<20 µm) were subjected to first-of-its-kind analysis via simultaneous laser-induced breakdown spectroscopy (LIBS) and laser ablation multi-collector inductively coupled plasma-mass spectrometry (LA-MC-ICP-MS). Briefly, a nanosecond pulsed high-energy laser was focused onto the sample (particle) surface. In a single laser pulse, the UO2F2 particle was excited/ionized within the microplasma volume, and the emission of light was collected via fiber optics such that emission spectroscopy could be employed for the detection of uranium (U) and fluorine (F). The ablated particle was simultaneously transported into the MC-ICP-MS for high precision isotopic (i.e., 234U, 235U, and 238U) analysis. This method, LIBS/LA-MC-ICP-MS was optimized and employed to rapidly measure 80+ UO2F2 particles, which were subjected to different calcination processes, which results in varying degrees of F loss from the individual particles. In measuring the particles, the average F/U ratios for the populations treated at 100 and 500 °C were 2.78 ± 1.28 and 1.01 ± 0.50, respectively, confirming loss of F through the calcination process. The average 235U/238U on the particle populations for the 100 and 500 °C were 0.007262 (22) and 0.007231 (23), which was determined to be <0.2% from the expected value. The 234U/238U ratios on the same particles were 0.000053 (11) and 0.000050 (10) for the 100 and 500 °C, respectively, <10% from the expected value. Notably, each population was analyzed in under 5 min, demonstrating the truly rapid analysis technique presented here.

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.

Mapping of uranium particles on J-type swipes with microextraction-ICP-MS.

A microextraction liquid sampling system coupled to a quadrupole inductively coupled plasma-mass spectrometer (ICP-MS) was utilized to spatially discern uranium particles, isotopically, on a cellulose-based swipe material (i.e., J-type swipe). These types of swipes are often used by the International Atomic Energy Agency (IAEA) as part of their environmental sampling program. A grid was created such that extraction locations covered the center circle (n = 34 without overlapping). Uranium (U) particulates (<20 µm) of varying U isotopic abundance and chemical form (i.e., uranyl fluoride and uranyl nitrate hexahydrate) were mechanically placed on the swipes in random locations and detected via the microextraction-ICP-MS methodology. Heat maps were subsequently generated to show the placement of the particulate with their respective intensity and isotopic determination. This detection of the uranium particulates, via isotopic determination, agreed with reference values for these materials. Additionally, depleted (235U/238U = 0.002) uranium particulates were placed directly within a clay matrix, on the swipe surface, and subjected to analysis by microextraction-ICP-MS. The mapping of the swipe demonstrated, for the first time, the employment of the microextraction-ICP-MS method for extracting sample from a complex matrix, and correctly identifying the uranium isotopic composition. This example ultimately demonstrates the utility of the methodology for detecting particles of interest in complex matrices.

Novel Calibration Approach for Monitoring Aerosol Hydrogen Isotopes Using Laser-Induced Breakdown Spectroscopy for Molten Salt Reactor Off-Gas Streams.

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.

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.

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 (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), Sm3+, HNO3, and temperature, respectively. The approach may be useful for quantifying fluorescent fission products (e.g., Sm3+) 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 Ablation Plasmas and Spectroscopy for Nuclear Applications.

The development of measurement methodologies to detect and monitor nuclear-relevant materials remains a consistent and significant interest across the nuclear energy, nonproliferation, safeguards, and forensics communities. Optical spectroscopy of laser-produced plasmas is becoming an increasingly popular diagnostic technique to measure radiological and nuclear materials in the field without sample preparation, where current capabilities encompass the standoff, isotopically resolved and phase-identifiable (e.g., UO and UO2) detection of elements across the periodic table. These methods rely on the process of laser ablation (LA), where a high-powered pulsed laser is used to excite a sample (solid, liquid, or gas) into a luminous microplasma that rapidly undergoes de-excitation through the emission of electromagnetic radiation, which serves as a spectroscopic fingerprint for that sample. This review focuses on LA plasmas and spectroscopy for nuclear applications, covering topics from the wide-area environmental sampling and atmospheric sensing of radionuclides to recent implementations of multivariate machine learning methods that work to enable the real-time analysis of spectrochemical measurements with an emphasis on fundamental research and development activities over the past two decades. Background on the physical breakdown mechanisms and interactions of matter with nanosecond and ultrafast laser pulses that lead to the generation of laser-produced microplasmas is provided, followed by a description of the transient spatiotemporal plasma conditions that control the behavior of spectroscopic signatures recorded by analytical methods in atomic and molecular spectroscopy. High-temperature chemical and thermodynamic processes governing reactive LA plasmas are also examined alongside investigations into the condensation pathways of the plasma, which are believed to serve as chemical surrogates for fallout particles formed in nuclear fireballs. Laser-supported absorption waves and laser-induced shockwaves that accompany LA plasmas are also discussed, which could provide insights into atmospheric ionization phenomena from strong shocks following nuclear detonations. Furthermore, the standoff detection of trace radioactive aerosols and fission gases is reviewed in the context of monitoring atmospheric radiation plumes and off-gas streams of molten salt reactors. Finally, concluding remarks will present future outlooks on the role of LA plasma spectroscopy in the nuclear community.

Spatially Resolved Raman Spectroscopic Investigation of Uranyl Fluoride: A Case Study in the Importance of Instrument Optimization.

Raman spectroscopy is an emerging technique for rapid and nondestructive analysis of nuclear materials for forensic and nonproliferation applications as it is a powerful tool for distinguishing multiple chemical forms of materials with similar stoichiometries. Recent developments in spectroscopic software have enabled rapid data collection with high-speed Raman spectroscopic mapping capabilities. However, some uranium-rich materials are susceptible to degradation in humid air and/or laser-induced phase transformations. To mitigate environmental or measurement-related sample degradation of potential samples of interest, we have taken a systematic approach to define optimized data collection parameters for high-throughput measurements of uranyl fluoride (UO2F2), which is an important intermediate material in the nuclear fuel cycle. First, we systematically describe the influence of optical magnification (5× to 100×), laser power, and exposure time on obtained signal for identical particles of UO2F2 and find that at low laser power and exposure times, comparable signal is obtained regardless of optical magnification. Second, we ensure sample integrity during data collection, and third, collect spectroscopic maps that employ optimized parameters to reduce the time required to obtain spatially resolved spectroscopic information. Reductions of 90% and 99% in measurement times are discussed as they relate to differences in resolving spectroscopic features of particles in identical mapping areas. During this work, we found that additional data processing options were needed and thus developed a customized Python script for importing, processing, analyzing, and visualizing Raman spectroscopic map data.

Quantifying platinum binding on protein-functionalized magnetic microparticles using single particle-ICP-TOF-MS.

This work describes an analytical procedure, single particle-inductively coupled plasma-time-of-flight-mass spectrometry (SP-ICP-TOF-MS), that was developed to determine the platinum binding efficiency of protein-coated magnetic microparticles. SP-ICP-TOF-MS is advantageous due to its ability to quasi-simultaneously detect all nuclides (7Li-242Pu), allowing for both platinum and iron (composition of magnetic microparticles) to be measured concurrently. This method subsequently allows for the differentiation between bound and unbound platinum. The 1 µm magnetic microparticles were fully characterized for their iron concentration, particle concentration, and trace element composition by bulk digestion-ICP-MS and SP-ICP-TOF-MS. The results of both approaches agreed with the certificate values. Using the single particle methodology the platinum loading was quantified to be to 0.18 ± 0.02 fg per particle and 0.32 ± 0.02 fg per particle, for the streptavidin-coated and azurin-coated microparticles, respectively. Both streptavidin-coated and the azurin-coated microparticles had a particle-platinum association of >65%. Platinum bound samples were also analyzed via bulk digestion-based ICP-MS. The bulk ICP-MS results overestimated platinum loading due to free platinum in the samples. This highlights the importance of single particle analysis for a closer inspection of platinum binding performance. The SP-ICP-TOF-MS approach offers advantages over typical bulk digestion methods by eliminating laborious sample preparation, enabling differentiation between bound/unbound platinum in a solution, and quantification of platinum on a particle-by-particle basis. The procedure presented here enables quantification of metal content per particle, which could be broadly implemented for other single particle applications.

Density Measurements of Molten LiF-BeF2 and LiF-BeF2-LaF3 Salt Mixtures by Neutron Radiography.

The densities of eutectic (LiF)2-BeF2 and mixtures of this salt (FLiBe) with LaF3 were measured by dilatometry and by neutron attenuation from 673 K to 1,073 K. Because LaF3 has a limited solubility in FLiBe, it was necessary to determine the amount of LaF3 in solution before the density could be determined. The FLiBe density determination was favorably benchmarked against the literature data. A simple comparison was not available for the LaF3-FLiBe mixtures, so extrapolation of published data was necessary based on analysis using the Molten Salt Thermal Properties Database-Thermochemistry, or MSTDB-TC, developed by the US Department of Energy. Solubilities for LaF3 in FLiBe ranged from 1 to 4 mol % over 673 to 1,073 K. The salt system was heated and cooled over 24 h to evaluate potential changes in composition and hysteresis during the measurement. Changes in the meniscus were observed, and these were included in the correction for density determinations. Salt surface tension may have led to supersaturation of LaF3 in the salt because the solubility curve was nonlinear with respect to the inverse temperature, as would be expected for an ideal system. Surface tension measurements are currently underway to test this hypothesis.

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.

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.

Towards Automated and High-Throughput Quantitative Sizing and Isotopic Analysis of Nanoparticles via Single Particle-ICP-TOF-MS.

The work described herein assesses the ability to characterize gold nanoparticles (Au NPs) of 50 and 100 nm, as well as 60 nm silver shelled gold core nanospheres (Au/Ag NPs), for their mass, respective size, and isotopic composition in an automated and unattended fashion. Here, an innovative autosampler was employed to mix and transport the blanks, standards, and samples into a high-efficiency single particle (SP) introduction system for subsequent analysis by inductively coupled plasma-time of flight-mass spectrometry (ICP-TOF-MS). Optimized NP transport efficiency into the ICP-TOF-MS was determined to be >80%. This combination, SP-ICP-TOF-MS, allowed for high-throughput sample analysis. Specifically, 50 total samples (including blanks/standards) were analyzed over 8 h, to provide an accurate characterization of the NPs. This methodology was implemented over the course of 5 days to assess its long-term reproducibility. Impressively, the in-run and day-to-day variation of sample transport is assessed to be 3.54 and 9.52% relative standard deviation (%RSD), respectively. The determination of Au NP size and concentration was of <5% relative difference from the certified values over these time periods. Isotopic characterization of the 107Ag/109Ag particles (n = 132,630) over the course of the measurements was determined to be 1.0788 ± 0.0030 with high accuracy (0.23% relative difference) when compared to the multi-collector-ICP-MS determination.

Monitoring Noble Gases (Xe and Kr) and Aerosols (Cs and Rb) in a Molten Salt Reactor Surrogate Off-Gas Stream Using Laser-Induced Breakdown Spectroscopy (LIBS).

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.

Quantification of Lanthanides in a Molten Salt Reactor Surrogate Off-Gas Stream Using Laser-Induced Breakdown Spectroscopy.

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.

Monitoring Xenon Capture in a Metal Organic Framework Using Laser-Induced Breakdown Spectroscopy.

Molten salt reactor operation will necessitate circulation of a cover gas to remove certain evolved fission products and maintain an inert atmosphere. The cover gas leaving the reactor core is expected to contain both noble and non-noble gases, aerosols, volatile species, tritium, and radionuclides and their daughters. To remove these radioactive gases, it is necessary to develop a robust off-gas system, along with novel sensors to monitor the gas stream and the treatment system performance. In this study, a metal organic framework (MOF) was engineered for the capture of Xe, a major contributor to the off-gas source term. The engineered MOF column was tested with a laser-induced breakdown spectroscopy (LIBS) sensor for noble gas monitoring. The LIBS sensor was used to monitor breakthrough tests with various Xe, Kr, and Ar mixtures to determine the Xe selectivity of the MOF column. This study offers an initial demonstration of the feasibility of monitoring off-gas treatment systems using a LIBS sensor to aid in the development of new capture systems for molten salt reactors.

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%.

Quantification of Rare Earth Elements in the Parts Per Million Range: A Novel Approach in the Application of Laser-Induced Breakdown Spectroscopy.

This work extends a previous percentage level concentration study of the optical emission spectra for six rare earth elements, europium (Eu), gadolinium (Gd), lanthanum (La), praseodymium (Pr), neodymium (Nd), and samarium (Sm), along with the transition metal, yttrium (Y) using laser-induced breakdown spectroscopy (LIBS). The concentration of these six rare earth elements and yttrium has been attempted for the first time systematically down to parts per million (ppm) concentration levels ranging from 30 to 300 ppm. The authors have developed multivariate models for each element capable of predicting concentration with acceptable to excellent levels of accuracy. Additionally, partial least squares regression coefficients were used to identify key spectral features able to be used in this lower concentration regime. This study has demonstrated that it is conceivable to quantify the six rare earth elements along with yttrium at low concentrations in the parts per million levels.