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At least 1,135 records · Page 63

New Capability at ORNL: High Precision Uranium Titration

Destructive analytical measurements establish a nuclear facility’s nuclear material inventory and inventory differences for Nuclear Material Accountancy and Control. A nuclear laboratory’s ability to perform accurate high-precision analytical measurements is key for tracking large inventories within a facility’s material balance areas and during production to track material movement through dynamic processes. For uranium, these measurements are made by using several established high-precision measurement protocols. These include isotope dilution mass spectrometry, gravimetry, and potentiometric titrations. Whatever measurement technique a nuclear lab chooses to use, reference materials (RMs) with certified attributes and accompanying uncertainties are used to calibrate measurement systems, and are the cornerstone for accurate results. In addition to calibration, RMs provide for metrological traceability, are used for method development and validation, and thus provide critical evaluations of the appropriateness and performance of analytical processes used. Evaluations may include validation that a method is fit-for-purpose, quantification of systematic and random biases, and the evaluation of long-term and short-term performance metrics. High-precision measurement techniques require that measured attributes be certified to a high degree of precision in the RMs used—ultimately, to a higher degree than that of the measurement technique itself. The US authority on the production of special nuclear material Certified Reference Materials is the NBL Program Office (NBLPO), formally known as New Brunswick Laboratory (NBL). The NBLPO is responsible for the sales and distribution of existing NBL certified reference materials (CRMs) and for the production of the next generation of nuclear RMs. To accomplish its mission, NBLPO is establishing key base capabilities within the DOE laboratory complex that formerly existed at the NBL laboratory. The Nuclear Analytical Chemistry (NAC) section within the Chemical Sciences Division (CSD) at Oak Ridge National Laboratory (ORNL) is currently working with NBLPO to set up laboratory and measurement capabilities to provide measurements and capabilities for production and/or recertification of existing and future CRMs for uranium assay. The NBL-developed high-precision titration (HPT) method is a critically-evaluated, extremely precise and accurate primary method utilized for the determination of uranium content in a variety of uranium materials. The HPT method, combined with detailed balance weighing protocols, provides for an analytical methodology that is unsurpassed in precision and one in which all sources of error have been evaluated, a requirement of CRM certification. HPT capability within the United States was lost with the closure of the labs at NBL. The NAC has been collaborating with NBLPO to stand up and demonstrate the capability to perform uranium assay via HPT at ORNL. HPT can produce results with an expanded uncertainty of approximately 0.01% for pure uranium compounds, with typical precisions of <0.006%. The major tasks required to stand up the method at ORNL were the refurbishment of a dedicated lab and equipment setup, procedure development, analyst training, establishing method-specific quality assurance, and qualification of an analyst to perform the method. This report summarizes these tasks, outlines the documents drafted, and gives the outcome of the qualification titrations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

High-order finite element method for atomic structure calculations

We introduce featom, an open source code that implements a high-order finite element solver for the radial Schrödinger, Dirac, and Kohn-Sham equations. The formulation accommodates various mesh types, such as uniform or exponential, and the convergence can be systematically controlled by increasing the number and/or polynomial order of the finite element basis functions. The Dirac equation is solved using a squared Hamiltonian approach to eliminate spurious states. Here, to address the slow convergence of the $κ=±1$ states due to divergent derivatives at the origin, we incorporate known asymptotic forms into the solutions. We achieve a high level of accuracy (10 -8 Hartree) for total energies and eigenvalues of heavy atoms such as uranium in both Schrödinger and Dirac Kohn-Sham solutions. We provide detailed convergence studies and computational parameters required to attain commonly required accuracies. Finally, we compare our results with known analytic results as well as the results of other methods. In particular, we calculate benchmark results for atomic numbers (Z) from 1 to 92, verifying current benchmarks. We demonstrate significant speedup compared to the state-of-the-art shooting solver dftatom. An efficient, modular Fortran 2008 implementation, is provided under an open source, permissive license, including examples and tests, wherein particular emphasis is placed on the independence (no global variables), reusability, and generality of the individual routines.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Sparsity Applications for Gradient‐Based Optimization of Wind Farms

Optimizing wind farms is essential for designing efficient energy systems, especially as farms grow larger and span multiple sites. However, this optimization becomes increasingly challenging due to the rising computational cost associated with more turbines. Gradient‐based optimization methods scale better than gradient‐free approaches for large problems, but the most computationally expensive component remains the calculation of gradients for the objective function and constraint Jacobians. To address this, we propose leveraging sparsity to accelerate gradient evaluations and reduce the size of the constraint Jacobian. Wind farms naturally exhibit sparsity—many turbines do not influence each other under certain wind directions. However, unlike traditional sparse problems with fixed patterns, wind farm sparsity is dynamic, requiring new strategies to handle changing interactions efficiently. This paper presents a study of sparsity in wind farm optimization and introduces several methods to exploit it. These strategies are tested on multiple farms using the analytic Cumulative Curl model, with gradients computed via automatic differentiation (AD). The same sparsity‐aware techniques are also applicable to finite difference (FD) methods, where they can yield even greater speedups due to the high cost of directional evaluations. Results show that sparse methods achieve up to a 10x speedup with less than ± 5% variance in optimized wake losses compared to traditional methods. These findings suggest that sparsity‐aware optimization not only maintains solution quality but also scales efficiently with farm size, enabling more comprehensive design exploration at reduced computational cost.

17 WIND ENERGY↗

Constraining Cosmology with Simulation-based inference and Optical Galaxy Cluster Abundance

We test the robustness of simulation-based inference (SBI) in the context of cosmological parameter estimation from galaxy cluster counts and masses in simulated optical datasets. We construct ``simulations'' using analytical models for the galaxy cluster halo mass function (HMF) and for the observed richness (number of observed member galaxies) to train and test the SBI method. We compare the SBI parameter posterior samples to those from an MCMC analysis that uses the same analytical models to construct predictions of the observed data vector. The two methods exhibit comparable performance, with reliable constraints derived for the primary cosmological parameters, ($\Omega_m$ and $\sigma_8$), and richness-mass relation parameters. We also perform out-of-domain tests with observables constructed from galaxy cluster-sized halos in the Quijote simulations. Again, the SBI and MCMC results have comparable posteriors, with similar uncertainties and biases. Unsurprisingly, upon evaluating the SBI method on thousands of simulated data vectors that span the parameter space, SBI exhibits worsened posterior calibration metrics in the out-of-domain application. We note that such calibration tests with MCMC is less computationally feasible and highlight the potential use of SBI to stress-test limitations of analytical models, such as in the use for constructing models for inference with MCMC.

79 ASTRONOMY AND ASTROPHYSICS↗

Too big, too small, or just right? A benchmark assessment of density functional theory for predicting the spatial extent of the electron density of small chemical systems

Multipole moments are the first-order responses of the energy to spatial derivatives of the electric field strength. The quality of density functional theory prediction of molecular multipole moments thus characterizes errors in modeling the electron density itself, as well as the performance in describing molecules interacting with external electric fields. However, only the lowest non-zero moment is translationally invariant, making the higher-order moments origin-dependent. Therefore, instead of using the 3 × 3 quadrupole moment matrix, we utilize the translationally invariant 3 × 3 matrix of second cumulants (or spatial variances) of the electron density as the quantity of interest (denoted by K). The principal components of K are the square of the spatial extent of the electron density along each axis. A benchmark dataset of the principal components of K for 100 small molecules at the coupled cluster singles and doubles with perturbative triples at the complete basis set limit is developed, resulting in 213 independent K components. The performance of 47 popular and recent density functionals is assessed against this Var213 dataset. Several functionals, especially double hybrids, and also SCAN and SCAN0 predict reliable second cumulants, although some modern, empirically parameterized functionals yield more disappointing performance. The H, Li, and Be atoms, in particular, are challenging for nearly all methods, indicating that future functional development could benefit from the inclusion of their density information in training or testing protocols.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Effect of second Si–O vibrational overtones/combinations on quantifying water in silicate and silica minerals using infrared spectroscopy, and an experimental method for its removal

Infrared spectroscopy (IR) is the most widely used analytical tool to quantify trace water in silicate and silica minerals. A prerequisite for highly accurate IR measurements of trace water is a good understanding of the effect of the second Si–O vibrational overtones/combination bands (2nd Si–O VOCBs) on the water peaks. Silicate and silica minerals can be divided as isolated (Q 0 ), paired (Q 1 ), ring (Q 2 ), chain (Q 1 or Q 2 ), sheet (Q 3 ) and framework (Q 4 ) structures according to the polymerization of their SiO 4 tetrahedral units, and the 2nd Si–O VOCBs of these different structural types attain different vibrational features which are expected to affect the water peaks to different extents. Here, we selected olivine (Q 0 ) and α-quartz (Q 4 ) as two endmember-like structural examples, performed extensive IR measurements on both pristine and heat-treated thin sections prepared for these two minerals, and explored the vibrational features of the 2nd Si–O VOCBs. In this work, we have found that the 2nd Si–O VOCBs are well separated from the water peaks in olivine, but severely overlap with the water peaks in α-quartz, confirming the different roles that the 2nd Si–O VOCBs play in quantifying trace water in silicate and silica minerals with different structural polymerizations. To remove the influence of the 2nd Si–O VOCBs (or any other species rather than water), an experimental protocol has been successfully developed, as approved by some fundamental equations and verified by the data of α-quartz in the literature. This development should lead to significant accuracy improvement in quantifying trace amounts of water in Earth and planetary materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Sensing analytical instrument parameters, specimen characteristics, or both from sparse datasets

Disclosed are methods for sensing conditions of an electron microscope system and/or a specimen analyzed thereby. Also disclosed are sensor systems and electron microscope systems able to sense system conditions, and/or conditions of the specimen being analyzed by such systems. In one embodiment, a sparse dataset can be acquired from a random sub-sampling of the specimen by an electron beam probe of the electron microscope system. Instrument parameters, specimen characteristics, or both can be estimated from the sparse dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Detection of analytes using porous mass spectrometry surface

The disclosure herein includes compositions and methods for ionizing targets and methods for making the compositions. In some embodiments, the compositions can include a porous substrate that has been etched for a desired average pore size, a desired porosity, or both for detection of one or more targets of interest. Also disclosed herein are methods for using the composition to ionize targets.

36 MATERIALS SCIENCE↗

Hypergraph Analytics of Domain Name System Relationships

We report on the use of novel mathematical methods in hypergraph analytice over a large quantity of DNS data. Hypergraphs generalize graphs, as used in network science, to better model complex multiway relations in cyber data. Specifically, casting DNS data from Georgia Tech's ActiveDNS repository as hypergraphs allows us to fully represent the interactions between {\em collections} of domains and IP addresses. To facilitate large-scale analytics, we fielded an analytical pipeline of two capabilities. HyperNetX (HNX) is a Python package for the exploration and visualization of hypergraphs, acting as a frontend. For the backend, the Chapel HyperGraph Library (CHGL) is a library for high performance hypergraph analytics written in the exascale programming language Chapel. CHGL was used to process gigascale DNS data, performing compute-intensive calculations for data reduction and segmentation. Identified portions are then sent to HNX for both exploratory analysis and knowledge discovery targeting known tactics, techniques, and procedures.

Joslyn, Cliff A.↗

Development of a separation method for rare earth elements using LN resin

The lanthanide elements and isotopes are analytes of significant interest for several nuclear-related fields. Chemical separation among these elements is uniquely challenging due to their nearly identical chemical properties. This difficulty in interelement separation can create obstacles for quantitative radiometric analysis, specifically for isotopes without distinct energy emissions easily measured by gamma spectroscopy, for example Tb-161. Presented in this work is an optimized method for the isolation of individual rare earth elements using Eichrom’s LN resin.

Arrigo, Leah M.↗

Transient MOC with frequency transform and DSA on unstructured mesh

We present an implementation of the transient method of characteristics (MOC) with isotropic time derivatives, accelerated by diffusion synthetic acceleration (DSA). The fully implicit frequency transform method is used to solve the transient problem with analytic precursor integration. The code works on meshes composed of almost any of the commonly used non-curvilinear finite element types, and can handle the deformation of geometry in time-dependent transport calculations. We present results of a continuous Fourier analysis for the transient multigroup DSA problem, and representative benchmarking results are presented for the C5G7-TD benchmark in 2D showing reasonable performance and agreement compared to other codes. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Elastic Solutions to 2D Plane Strain Problems: Nonlinear Contact and Settlement Analysis for Shallow Foundations

The classical Neumann boundary value problem of an isotropic, homogeneous elastic half-plane under plane strain conditions is readdressed as the limiting case of the fully three-dimensional problem. Analytical solutions of the stress and strain tensors are obtained by taking the limit from known three-dimensional solutions. It is shown that the displacement fields for the plane strain problem are not well defined. A small number of simple expressions are developed, which provide a general solution for linearly-varying traction over arbitrary regions on the boundary. A simple, efficient, and rapidly convergent algorithm is developed which uses these solutions as analytic elements and provides a solution approach to the general boundary value problem. The method is verified against known solutions for Hertzian contact between parallel cylinders. Two numerical examples are presented for the analysis of shallow foundation systems. In the first, the boundary conditions are informed by analytical elastoplastic calculations and a strain influence analysis is performed and compared with the Schmertmann method. Subsequently, empirical laboratory contact traction distributions measured by Bauer et al., in both the normal and tangential directions are employed as boundary conditions for an analysis of the underlying stress field.

42 ENGINEERING↗

Enhancing analytical merits of laser-induced breakdown spectroscopy of hydrogen isotopes using an orthogonal double-pulsing scheme

Accurate detection and quantification of hydrogen isotopes in solid materials are vital for diverse applications, including fusion energy, hydrogen storage, and tritium production. Laser-induced breakdown spectroscopy (LIBS) is a well established, rapid, standoff method for this purpose, but it faces challenges related to the analytical merits required for isotopic analyses. In this study, we enhance the analytical and detection capabilities of traditional single-pulse LIBS by implementing an orthogonal double pulsing approach, focusing on the analysis of a range of 2 H concentrations in Zircaloy-4 substrates (acting as a proxy for 3 H). The double-pulse experiments employed an orthogonal re-heating configuration with two nanosecond Nd:YAG lasers. We systematically evaluated critical parameters affecting the signal intensity in double-pulse LIBS, including interpulse delay, ambient gas pressure, and heating laser energy. Finally, our results demonstrate that employing an orthogonal double-pulse scheme significantly enhances 2 H α emission while minimizing line broadening and self-absorption, ultimately improving the technique’s analytical capabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An ICP-OES method for the precise and accurate quantification of rare earth elements in natural water: A comparative study from mine waste sites in New Mexico, USA

Inductively coupled plasma techniques such as ICP-OES and ICP-MS are routinely used to determine the concentrations of rare earth elements (REE) in water samples. However, their performance for the determination of REE concentration in mine drainage waters from epithermal vein and porphyry copper mining districts has not been evaluated extensively. In this work, we develop an REE analysis method on an Agilent 5900 ICP-OES instrument and assess the accuracy and precision for the quantification of REE in the natural waters collected from mine adits and an acid seep of mine sites in the Steeple Rock and Hillsboro mining districts, New Mexico, USA. The total REE concentrations in the water samples were measured using the methods we developed for both ICP-OES and ICP-MS. The power of the new ICP-OES method lies in routine analysis of μg/L level concentrations normally analyzed using ICP-MS, including a U.S. Geological Survey standard reference sample, laboratory blank samples spiked with a National Institute of Standards and Technology traceable standard, and surface water samples from mine waste sites. This ICP-OES method achieves low quantification limits ranging from 0.2 to 5 μg/L and excellent analytical accuracy and precision for REE analysis. The precision of light (La-Gd) and heavy (Tb-Lu) REE analysis using this method are better than 5% at average concentrations above 5 ± 4 μg/L and 3 ± 2 μg/L, respectively, and 3% at average concentrations above 10 ± 9 μg/L and 5 ± 4 μg/L, respectively. This method also shows excellent sensitivity and reproducibility for our laboratory and field samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine Learning in the Context of Laser-Induced Breakdown Spectroscopy

The integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS) has revolutionized the analytical capabilities of LIBS. The combi-nation of both methods enables more accurate and efficient data analysis. While LIBS itself is a powerful technique for elemental analysis, the vast amount of spectral data it generates can be hard to interpret. Machine learning addresses these challenges by leveraging algorithms that can learn from data, identify patterns, and make predictions without explicit programming for the interpretation of each specific task. In LIBS application, ML techniques are used to enhance various analytical processes. For example, ML algorithms can classify materials based on their spectral fingerprints, predict the concentration of elements in a sample, and identify underlying patterns within complex datasets. Here, this application improves the precision of LIBS analyses while significantly reducing the time required for data processing and interpretation. In this chapter, the fundamental concepts of ML will be discussed first. Following this, the process of data splitting and the importance of feature selection will be examined. Several machine learning methods will then be closely examined, exploring how each can benefit LIBS analysis and highlighting their respective advantages and shortcomings. This structured approach will provide a comprehensive understanding of the integration of ML in the context of LIBS analysis.

47 OTHER INSTRUMENTATION↗

Flattening the Radial Temperature Profile across the Transformational Challenge Reactor Core

The Transformational Challenge Reactor (TCR) program is demonstrating an agile development approach to advanced nuclear reactor design, which has traditionally utilized a linear design process. In leveraging artificial intelligence, additive manufacturing, advanced materials, and cutting-edge modeling and simulation, the TCR program aims to minimize the high cost and lengthy deployment timelines now standard in the nuclear industry. Within a relatively short period of time, a robust and mature advanced gas-cooled reactor was iteratively designed under the TCR program, using these cutting-edge technologies. The TCR is a 3 MWt gas-cooled microreactor fueled with uranium nitride (UN) tristructural isotropic (TRISO) fuel particles. Though manufactured via traditional means, these UN TRISO particles are loaded into additively manufactured silicon carbide (SiC) cans [4]. Once loaded with TRISO particles, the SiC cans are densified using a chemical vapor infiltration process. The additively manufactured SiC enables significantly more freedom in the design of the fuel form than could ever be achieved using traditionally manufactured SiC. The helium coolant, pressurized to 5 MPa, enters the core at 300°C and nominally exits it at 500°C. Typically, the most thermally limiting components in any reactor design are the fuel assemblies in the core center. To provide a wide thermal margin in these central fuel assemblies, the flow may be biased toward the center of the core to more effectively cool these fuel assemblies with more power deposition and flatten the core’s radial temperature distribution. An analytical fluid model of the TCR core was developed to explore methods for biasing the flow away from the cooler outer fuel assemblies and towards the hotter inner ones. Higher-fidelity models developed in STAR-CCM+ 2020.3.1, a computational fluid dynamics code, were then utilized to verify the analytical model’s findings.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A Systematic Investigation of the Effects of Standard-Sample Concentration Mismatch during Fe Isotope Measurement by MC-ICP-MS

Advances in multi-collector inductively coupled plasma-mass spectrometry (MC-ICP-MS) have led to the widespread use of iron (Fe) isotopes to elucidate the (bio)geochemical history of a range of environments. To generate Fe isotope ratio measurements, standard-sample bracketing (SSB) is commonly used to correct for instrumental mass bias inherent to MC-ICP-MS. However, SSB is only accurate when sample and isotope standard Fe concentrations match, in addition to the bulk solution matrix. When the Fe concentrations differ, Fe isotope ratio measurement results may be inaccurate, a phenomenon known as the "self-induced matrix effect." Here, this study systematically characterised the self-induced matrix effect for dry plasma Fe isotope ratio measurements on three MC-ICP-MS instruments and three introduction systems. Our extensive dataset indicates that: (1) the degree of mass bias is consistent regardless of MC-ICP-MS front-end design, (2) the degree of mass bias becomes less reproducible as the concentration difference between the sample and bracketing standard increases, and (3) this applies to both pure Fe solutions and solutions from geological materials. This study reinforces the requirement to match bracketing standard and sample concentrations within 10% and provides a correction method for that fall beyond the recommended concentration range to subsequently allow for proper concentration matching during SSB.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Synthesis of nanodiamonds encapsulated by zeolitic imidazole framework-8 for quantum sensing applications

Nitrogen vacancy (NV)-containing nanodiamonds are widely used in quantum sensing applications due to their high sensitivity to magnetic fields, relatively low cost, and ability to be initialized, manipulated, and read out at room temperature. Quantum sensing techniques such as optically detected magnetic resonance (ODMR) and spin relaxometry have exploited the sensitivity of the NV nanodiamonds to magnetic fields to detect a range of analytes, such as pH, metal ions, and biomolecules. However, diversifying the sensing targets accessible by NV diamond quantum sensors typically requires careful engineering of the diamond surface chemistry with stimuli-responsive functional groups. Here, a simple protocol for coating NV nanodiamonds with the zeolitic imidazole framework 8 (ZIF-8), a widely used metal-organic framework, is presented. ZIF-8 is a highly porous material that has been used as a selective sensor for gasses, metal ions, and other analytes. The material is well-characterized by x-ray diffraction, transmission electron microscopy, scanning electron microscopy, x-ray photoelectron spectroscopy, and luminescence spectroscopy. Encapsulation of NV nanodiamonds with a porous scaffold such as ZIF-8 provides a promising method for improving the selectivity for the quantum sensing of various analytes. Importantly, the ZIF-8 coating does not impact the luminescence properties of the NV diamond, which is a key readout in ODMR and spin relaxometry sensing approaches. Indeed, the ODMR spectra with and without the ZIF-8 shell is nearly identical. Moreover, the ZIF-8 coating increases the longitudinal spin relaxation time of the NV nanodiamond by a factor of 4 relative to aggregated diamond, a desirable outcome for spin relaxation-based quantum sensing. Metal-organic framework composites with nanodiamonds thus are an exciting strategy for enhancing NV nanodiamond performance in applications such as quantum sensing and quantum-enhanced nuclear magnetic resonance spectroscopy.

nitrogen vacancy nanodiamond↗