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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Linking chemical precursors to the synthesis of erythritol tetranitrate

Erythritol tetranitrate (ETN) is a homemade explosive with high explosive performance which can easily be synthesized using widely available chemical precursors. Ascertaining the manner in which ETN is synthesized and being able to trace the ETN back to its precursor materials may aid in the forensic identification. As a proof of concept, seven different types of ETN were synthesized with laboratory grade materials as well as commercially available chemicals from the hardware store and grocery store. The seven different types of solid ETN and the reaction quenches were analyzed using high performance liquid chromatography (HPLC). Partial least squares discriminant analysis (PLS-DA) was used to probe the different classes of ETN and reaction quenches. Several PLS-DA models were produced with the seven different types of ETN and quenches having their own classification, ETN and quenches based on their acid source, and ETN and the quenches based on their nitrate source. Regardless of which classification manner was used, there were no differences found between the different types of ETN or their reaction quenches. Furthermore, it is demonstrated that there is inherent variability in the synthesis of ETN making HPLC an unreliable method to determine the source of the synthesis materials. Elemental impurities were measured in seven solid ETN samples and their reaction quenches by inductively coupled plasma mass spectrometry (ICP-MS). No differences were discovered between the solid ETN samples, but there were trace metals found in the reaction quenches which were found to be identifiers of the source material.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Inorganic characterization of switchgrass biomass using laser-induced breakdown spectroscopy

The inorganic characterization of 74 samples of switchgrass using laser-induced breakdown spectroscopy (LIBS) was undertaken. Determination of ash and inorganic elements content in biomass materials is vital for feedstock screening for bioconversion processes. Hierarchical models using principal component analysis (PCA) and partial least square analysis (PLS) were used to determine the presence of specific elemental micronutrients that are important in determining plant health for robust biomass production. LIBS uses a 532 nm laser with 45 mJ of laser power to excite the samples of switchgrass plant material and the emission of all the elements present in the plant samples were recorded in single spectra with a wide wavelength range of 200–800 nm. The results were compared to the laboratory standard technique, e.g., ICP-OES technique, to determine the true values for major micronutrients such as, silicon (Si), potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P), and sulfur (S). Overall, our objectives were: 1) To determine the spectral features of switchgrass containing different amounts of these elements and 2) To examine the viability of this technique for determining the quality of the feedstock in terms of its inorganic composition. Cross-validation results showed that the broad-based model developed is promising for inorganics prediction in switchgrass. The LIBS validation prediction for the micronutrient elements mentioned here have been obtained. The regression coefficients for Si, were obtained to be 0.995, 0.994 for calibration and validation respectively, in case of Ca the regression coefficients were, 0.994 and 0.992 for calibration and validation. Similarly, in the case of Mg and K these were calculated to be 0.992 and 0.985, and 0.994 and 0.993 respectively. The regression coefficients are not as good as those for the elements mentioned, in case of the two elements S and P. They are 0.957, and 0.878, and 0.952 and 0.894 respectively for calibration, validation for the two elements. This demonstrates that LIBS-based techniques are inherently well suited for diverse environmental applications. Furthermore, LIBS along with PLS model can show capability in determining the viability of switchgrass as a biomass in the production of biofuels and survivability of switchgrass in processes associated with climate change. LIBS can help determining which switchgrass would be appropriate for a specific conversion process that favors low ash content overall or low value of specific inorganics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

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 NO 3 – and CO 3 2– using their bands around 1050 cm –1 . Finally, for all samples, the PLS-based model was found to be the most efficient approach from a model building and application perspective.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

Fricke, Sophia N.↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Self-diagnosis of model suitability for continuous measurements of stream-dissolved organic carbon derived from in situ UV–visible spectroscopy

Application of high-frequency monitoring of dissolved organic carbon (DOC) is difficult in instances where training datasets are challenging to develop (e.g., remote locations) and the relationship between optical features and DOC concentration changes due to environmental or landscape shifts (e.g., climate or land-use change). We developed and compared three partial least squares (PLS) models using in situ water level measurements, conductivity, and UV–Vis spectral attenuation to predict DOC. Two site-specific models were developed using data from a hillslope-dominated forest or a low-relief wetland-pond-dominated stream catchment. The third model, using data from both sites, exhibited the best performance (DOC range = 4–15.5 mg C L −1 , mean = 8.38 mg C L −1 , training RMSE = 0.34 mg C L −1 , internal validation RMSE = 0.50 mg C L −1 , external validation RMSE = 2.43 mg C L −1 ). We further demonstrate using PLS model statistics to monitor performance and elucidate when and how models should be updated. These statistics, Hotelling's T 2 and squared prediction errors, are useful consistency checks for the predictions made and detect underlying inconsistencies that, if undetected, can reduce the robustness of DOC prediction. For example, via the T 2 statistic, we identified the summer–autumn transition as a period when DOC composition differed from what was represented in the training dataset. We also determined that elevated SUVA 254 values contributed to the overall bias observed in predictions made during the subsequent year as part of the external validation. This enabled the application of a bias correction that reduced the RMSE from 2.43 to 0.89 mg C L −1 . The method presented here could be applied to future monitoring programs enabling model updates to monitor DOC fluxes accurately from optical datasets (e.g., attenuance or fluorescence) in the face of developing datasets in remote locations or environmental change. In conclusion, implementation of this approach may also identify possible regime shifts or landscape and hydrologic change associated with climate and other environmental changes relevant to terrestrial to aquatic fluxes.

UV-VIS spectroscopy↗
DOE OSTI2021

A bifunctional zeolitic porous liquid with incompatible Lewis pairs for antagonistic cascade catalysis

The emergence of porous liquids (PLs) opened opportunities to form unique antagonistic systems capable of fulfilling cascade reactions promoted by incompatible active sites in one pot, which is a long-term challenging subject in catalysis. Herein, unique bifunctional type III PL-based systems were facilely fabricated via assembly of zeolite nanosheets with ionic liquids. Rational structural design afforded PLs that feature high zeolite concentration, stable dispersion after 2 years, abundant cavity distribution, and involvement of antagonistic groups (acid and base sites) in separated and active form via steric hindrance control and electronic repulsion regulation. These unique properties worked cooperatively to fulfill the cascade deacetalization-Knoevenagel/Aldol condensation in one pot with superior catalytic efficiency outperforming the traditional systems. The key to success lies in the formation of bifunctional composites, transformation of zeolite from heterogeneous to homogeneous via surface modification, as well as rapid mass transfer ensured by the rigid porous architecture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Non-destructive measurement and real-time monitoring of apple hardness during ultrasonic contact drying via portable NIR spectroscopy and machine learning

Portable near-infrared spectrometer in the spectral range of 900–1700 nm was evaluated for the first time to assess and monitor apple hardness in real-time during ultrasonic drying. Calibration models were developed using PLS and ANN, and their performances were evaluated by internal leave-one-out cross-validation and an external dataset. Several pre-treatments including standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky–Golay first and second derivatives were employed to examine the effects of spectral variations in hardness prediction. Seven important wavelengths were selected using weighted regression coefficients to develop a simple MLR model to facilitate the model interpretation and circumvent noise. The models using PLS, MLR, and ANN with selected wavelengths predicted the apple hardness with R 2 p of 0.91, 0.91, 0.95, and RMSEP of 14.78, 14.85, and 12.46 N, respectively. Finally, the results indicate that portable NIR spectrometers are quite promising for real-time monitoring of apple hardness during ultrasonic drying.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Parametric study and speciation analysis of rare earth precipitation using oxalic acid in a chloride solution system

Oxalic acid precipitation is a common step in the purification of rare earth elements (REE) from a concentrated pregnant leach solution (PLS). However, the presence of contaminants such as Al, Fe, and Ca in given amounts decreases the REE precipitation efficiency and product purity while also increasing the amount of oxalic acid needed to maximize recovery. As such, a statistically designed test program was performed to identify the optimal conditions necessary for a relatively low REE content PLS containing elevated concentrations of contaminant ions. The performance objective was maximization of REE precipitation efficiency while minimizing the oxalic acid dosage. A central composite design was utilized to quantify performance impacts and identify the ultimate set of parameter values for oxalic acid dosage, Fe(III) contamination concentration, solution pH, and reaction temperature. The resultant model suggested that oxalic acid dosage and reaction pH are the most significant factors for the REE precipitation efficiency, followed by the interaction of oxalic dosage and Fe concentration. Test results indicate that increasing the oxalic acid concentration from 0 g/L to 80 g/L improved the REE precipitation efficiency from approximately 4.2% to 95.0%. Furthermore, raising the solution pH from 0.5 to 2.5 considerably enhanced the precipitation efficiency from 0.0% to 98.9%. A solution temperature elevation decreased REE recovery, which indicated an exothermic reaction between REEs and oxalate anions. Finally, a high level of Fe contamination adversely impacted REE precipitation efficiency. Here, to further the understanding of the REE-oxalate system, a fundamental solution chemistry study was performed using the equilibrium constants of the reactions. The study resulted in the development of oxalate speciation diagrams and provided an analysis of the REE precipitation characteristics at various oxalate anion concentrations and Fe(III) contamination levels using MINTEQ software. The dominant Fe(III) species in the solution system were found to be Fe-(C 2 O 4 ) 3 3- , Fe-(C 2 O 4 ) 2- , and Fe-(C 2 O 4 ) + , which consume the majority of the oxalate anions. The simulated model was found to be in agreement with the experimental findings and helped to explain the adverse impact of increased iron concentrations on REE precipitation efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Design of enhanced porous organic cage solubility in Type 2 porous liquids

Decreasing cost of technologies for direct air capture of carbon can be achieved through the design of new materials with high CO 2 selectivity that can be incorporated into existing industrial processes. An emerging class of materials for these applications are porous liquids (PLs). PLs are mixtures of porous hosts and solvents with intrinsic porosity due to steric exclusion of solvent from inside the porous host. It is currently unknown how solvent -porous host interactions affect porous host solubility in the bulk solvent. Here, in this study, density functional theory simulations were used to investigate interactions between nine solvents and a CC13 porous organic cage (POC). Calculations identified that solvent molecules were the most stable when placed either inside the CC13 POC or in the pore window compared to interfacial binding sites. Structural changes to the CC13 POC correlated with reported experimental solubilities, including expansion of the CC13 POC with solvent molecule infiltration and expansion or contraction of the pore window. Based on these results, new PL design guidelines should include compositions with (1) high concentrations of POCs with flexible cage structures that can expand when solvated and (2) solvent molecule-POC combinations that contract the pore window during solvent molecule-host binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Critical role of solvation on CC13 porous organic cages for design of porous liquids

Efficient carbon capture requires the design of new materials with high CO 2 selectivity and gas adsorption capacity that can be incorporated into existing industrial processes. Porous liquids (PLs) are promising candidate materials that consist of a nanoporous host and a solvent forming a liquid with permanent porosity based on exclusion of the solvent from the interior of the nanoporous host. Stable PLs are based on solvent-nanoporous host interactions, which can be evaluated through molecular simulations. Here, time- and temperature-dependent density functional theory simulations were performed between four solvents, 2-bromophenol, 4-methylphenol, 2,4-dimethylphenol, and cyclohexanone and the CC13 porous organic cage (POC) as a prototypical PL composition. Overall, minimal reactions occurred in the PL including no changes in the POC structure. Additionally, POC-solvent coordination occurred through interactions of neighboring functional groups such as methyl/bromide and hydroxyl on the solvent molecules with the POC surface. Therefore, the location rather than the number of functional groups on the solvent molecule controls the POC-solvent interactions. Additionally, the POC pore window contracted or expanded up to 8% during solvation, which correlates with the experimental solubility and static solvent-POC binding, where solvents that caused less contraction of the POC pore window increased POC solubility. Finally, these results allow for the design of optimized POC-based PL compositions based on solvent-nanoporous host binding and variation in the pore window during solvation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Chemometric modeling of power conversion efficiency of organic dyes in dye sensitized solar cells for the future renewable energy

The present study reports chemometric modeling of power conversion efficiency (PCE) of dye sensitized solar cells (DSSCs) using the biggest available data set till date which comprises around 1200 dyes covering 7 chemical classes. To extract the best structural features required for higher PCE, we have developed multiple partial least squares (PLS) quantitative structure-property relationship (QSPR) models for the Triphenylamine, Phenothiazine, Indoline, Porphyrin, Coumarin, Carbazole and Diphenylamine chemical classes using descriptors derived from the best subset selection method followed by selection of best five models in each dataset based on the Mean Absolute Error (MAE) values. The models were validated both internally and externally followed by the consensus predictions employing “Intelligent Consensus Predictor” tool to examine whether the quality of predictions can be improved with the “intelligent” selection of multiple PLS models. The quality of predictions for the respective external sets showed that the consensus models (CM) are better than the individual models (IM) in most of the cases. From the insights of the developed models, we concluded that attributes like a packed structure toward higher conductivity of electrons, auxiliary donor fragment of aromatic tertiary amines, number of thiophenes inducing the bathochromic shift and augmenting the absorption, presence of additional electron donors, enhancement of electron-donating abilities, number of non-aromatic conjugated C(sp 2 ) which helps as conjugation extension units to broaden the absorption and highly conjugated $π$-systems exert positive contributions to the PCE. On the contrary, features negatively contributing to PCE are the followings: fragments which lower the tendency of localized $π–π*$ transition, fragments related to larger volume and surface area of dyes along with hydrophobicity resulting in poor adhesion, fragment RC = N causing dye hydrolysis, steric hindrance for π electronic mobility, fragments enhancing polarity, etc. Finally, the identified features from the best QSPR model of the coumarin dataset was employed in designing of ten more efficient coumarin dyes (predicted %PCE ranging from 8.93 to 10.62) than the existing ones.

14 SOLAR ENERGY↗
DOE OSTI2025

Delineating the Effects of Counterions on the Structural and Vibrational Properties of U(IV) Lindqvist Polyoxometalate Complexes

Herein we conducted a full investigation into the fundamental structural and vibrational properties of uranium(IV) Peacock−Weakley-type lacunary Lindqvist (W 10 ) polyoxometalate (POM) complexes. We recently demonstrated the importance of the secondary lattice elements in tuning the distortion of the D 4d symmetry in W 10 POM complexes, and here, we synthesized eight UW 10 complexes with different alkali metal counterions and evaluated how the composition and packing of counterion species affected complex structural and vibrational properties. Single-crystal X-ray diffraction analysis on complexes 1−8 revealed changes in structural distortion parameters as a function of differences in counterion configurations, while far-infrared and Raman spectra for 1−8 also demonstrated that vibrational mode frequencies were sensitive to changes in counterion composition and packing. To more effectively compare different counterion configurations, we developed counterion effective ionic radius (eIR) as a new structural parameter, and comparisons between structural distortion parameters and eIR values strongly suggested that modulation by the secondary lattice elements can affect structural and vibrational manifolds within POM complexes. Partial least squares (PLS) analysis was used to quantitatively evaluate correlations observed within this investigation, and PLS statistical models showed a strong correlation between counterion eIR and both structural distortion parameters and vibrational mode frequencies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Practical Guide to Chemometric Analysis of Optical Spectroscopic Data

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 is 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 understanding 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 comprised of 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 dataset. A toolbox (PNNL Chemometric Toolbox) written in MATLAB language is supplied in the Supplemental Information file and can be used as a companion for understanding the development and deployment of the chemometric algorithms described in this paper. The datasets 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 datasets into MATLAB and performing CLS, PCR, and PLS on their data.

Upper-Division Undergraduate, Analytical Chemistry↗
DOE OSTI2025

Control of Permanent Porosity in Type 3 Porous Liquids via Solvent Clustering

Porous liquids (PLs) are an exciting new class of materials for carbon capture due to their high gas adsorption capacity and ease of industrial implementation. They are composed of sorbent particles suspended in a nonadsorbed solvent, forming a liquid with permanent porosity. While PLs have a vast number of potential compositions based on the number of solvents and sorbent materials available, most of the research has been focused on the selection of the sorbent rather than the solvent. Therefore, PL design criteria on the supramolecular structures of the solvent are explored to create a fundamental understanding of how the solvent enables PL formation for rapid discovery of new PL compositions. Atomistic molecular dynamics simulation of eight solvents with a range of molecular sizes, shapes, and intramolecular bonding was performed, identifying that the shape and size of molecular clusters formed in the solvent are the driving predictor of PL formation rather than the size of the individual solvent molecule. The results demonstrate a significant departure from common approaches to PL formation based on the steric exclusion of solvent molecules from the sorbent via the size of the pore aperture. A modeling and experimental validation study further supports these findings. In conclusion, through this computational material design study, a previously unexplored mechanism in PL formation, solvent–solvent clustering, is identified as a critical factor for the accelerated discovery of liquid phase carbon capture materials.

Carbon capture↗
DOE OSTI2020

Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device

The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA), partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. Additionally, a boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

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

In this work, 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), Sm 3+ , HNO 3 , and temperature, respectively. The approach may be useful for quantifying fluorescent fission products (e.g., Sm 3+ ) 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.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

The role of defect charge, crystal chemistry, and crystal structure on positron lifetimes of vacancies in oxides

Density functional theory based positron lifetime (PL) calculations for cation and oxygen monovacancies in a range of oxides—hematite, magnetite, hercynite, and alumina—have been conducted to compare the impact of defect chemistry and crystal structure on the predicted lifetimes. The role of defect charge state has also been examined. A comparison across the same type of crystalline structure but different composition shows that oxygen vacancies only induce a slight increase in the positron-electron overlap and thus barely modify the PL as compared to the bulk. A much more substantial increase of PL is observed for cation monovacancies, regardless of crystal structure or the elemental nature of the vacancy, which we ascribe to an enhanced localization of charge density around the vacant site. The structural and compositional richness of the oxide leads to longer defect PLs, with defected hercynite exhibiting the longest PLs. The charge state of cation monovacancies modifies only by a small percentage the positron localization, relegating to secondary importance the metal defect's oxidation state in modifying the lifetime of positrons within vacancy traps.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗