Scoping Study for Nuclear Materials Quantification Capabilities
poster for presentation at NSARD 2025
99 GENERAL AND MISCELLANEOUS↗
SEARCH · Engineering Papers
Results for “quantification”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Characterization and effects of impurities on carbonate quantification in heterogenous matrices
Mineral carbonation simulates a natural weathering phenomena by breaking down silicates and oxides to form Ca & Mg carbonates. Various mineralization methods have been demonstrated as a potential technique to improve the quality of slags and tailings through neutralization and stabilization of problematic species to yield a product better suited for use in concrete. This study aims to characterize, quantify and analyze carbonates in various carbonated products such as mineralized CaCO 3 , CO 2 mineralized Steel Slags and Mine Tailings. More than 10 samples were analyzed for carbonate measurement and verification from industrial and academic partners that pioneer commercial CO 2 mineralization technologies. The samples were characterized primarily by using X-ray diffraction (XRD), Thermogravimetric Analyses (TGA), and Scanning Electron Microscopy (SEM) to gain insights into CaCO 3 content. A baseline characterization of lab-grade CaCO 3 and MgCO 3 also revealed important considerations for CaCO 3 measurement using TGA alone. The experiments using synthetic lab-grade samples also revealed that the presence of MgCO 3 /MgO can accelerate the decomposition of CaCO 3 and thus can affect measurement parameters. Lab-grade CaCO 3 samples dosed into steel slag and mine tailing also showed significant deviation in their decomposition behavior. These insights are used to inform the development of a standardized protocol for the measurement and verification of carbonate-bearing products.
97 MATHEMATICS AND COMPUTING↗
Uncertainty quantification in electromagnetic observables of nuclei
We present strategies to quantify theoretical uncertainties in modern ab initio calculations of electromagnetic observables in light and medium-mass nuclei. We discuss how uncertainties build up from various sources, such as the approximations introduced by the few- or many-body solver and the truncation of the chiral effective field theory expansion. We review the recent progress encompassing a broad range of electromagnetic observables in stable and unstable nuclei.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Uncertainty Quantification and Sensitivity Analysis of Nuclear Construction Cost Reduction Pathways
High construction costs have plagued recent nuclear projects and they hamper the widespread deployment of nuclear technology. The Nuclear Cost Reduction Tool is a plant economic framework that quantifies the impact that various plant design and construction attributes have on construction costs and cost overruns and shows the positive effects of learning over a series of deployments. However, a downside of the current model is that all model outputs and capabilities are deterministic. To provide a more comprehensive view, this study evaluated the impact of model parameter uncertainty through a sensitivity analysis applied to 18 model parameters. This approach quantified the impact of model uncertainty on the output variables of Net Overnight Capital Cost (Net OCC), Construction Duration (CD), and Levelized Cost of Electricity (LCOE). Monte Carlo analysis revealed uncertainty distributions for these variables, showing that absolute uncertainty decreases over a series of deployments. A local sensitivity analysis showed that even small parameter perturbations (5%) can have a significant impact on project execution, highlighting areas that could reduce costs by billions across an order book of plants. The results of this study have improved the understanding of the model and identified the most impactful model parameters and construction attributes.
Levelized Cost of Electricity↗
Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways
Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)—also known as multiple reaction monitoring (MRM)—and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.
mathematical modeling↗
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models
We use a conditional Karhunen-Lo` eve (KL) model to quantify and reduce uncertainty in a stochastic partial differential equation (SPDE) problem with partially-known space-dependent coefficient, Y (x). We assume that a small number of Y (x) measurements are available and model Y (x) with a KL expansion. We achieve reduction in uncertainty by conditioning the KL expansion coefficients on measurements. We consider two approaches for conditioning the KL expansion: In Approach 1, we condition the KL model first and then truncate it. In Approach 2, we first truncate the KL expansion and then condition it. We employ the conditional KL expansion together with Monte Carlo and sparse grid collocation methods to compute the moments of the solution of the SPDE problem. Uncertainty of the problem is further reduced by adaptively selecting additional observation locations using two active learning methods. Method 1 minimizes the variance of the PDE coefficient, while Method 2 minimizes the variance of the solution of the PDE. We demonstrate that conditioning leads to dimension reduction of the KL representation of Y (x). For a linear diffusion SPDE with uncertain log-normal coefficient, we show that Approach 1 provides a more accurate approximation of the conditional log-normal coefficient and solution of the SPDE than Approach 2 for the same number of random dimensions in a conditional KL expansion. Furthermore, Approach 2 provides a good estimate for the number of terms of the truncated KL expansion of the conditional field of Approach 1. Finally, we demonstrate that active learning based on Method 2 is more efficient for uncertainty reduction in the SPDE’s states (i.e., it leads to a larger reduction of the variance) than active learning using Method 2.
Conditioned Karhunen-Lo` eve expanion, machine lea↗
(U) Physics-Informed Deep Learning with Uncertainty Quantification for Weapons Radiography
Explore the source record for details and available documents.
97 MATHEMATICS AND COMPUTING↗
Two Problems in Applied Stochastic Processes: Scaling Limits of the Multiple Range in 1D Random Walk and Uncertainty Quantification in Satellite Imagery
Explore the source record for details and available documents.
97 MATHEMATICS AND COMPUTING↗
Uncertainty Quantification in Satellite Imagery
This is a presentation for Conner's Dissertation Defense which will be help on 2024/12/12, based on the work he has done with the NNSS.
97 MATHEMATICS AND COMPUTING↗
The Influence of C(sp 3 )H–Selenium Interactions on the 77 Se NMR Quantification of the π‐Accepting Properties of Carbenes
Abstract Selenium NMR has become a standard tool for scaling the π‐accepting character of carbenes. Herein, we highlight that non‐classical hydrogen bonding (NCHB), likely resulting from hyperconjugation, can play a significant role in the carbene–selenium 77 Se NMR chemical shift, thus triggering a non‐linear behavior of the Se‐Scale.
Junor, Glen P.↗
Theoretical, Solid‐State, and Solution Quantification of the Hydrogen Bond‐Enhanced Halogen Bond
Abstract Proximal noncovalent forces are commonplace in natural systems and understanding the consequences of their juxtaposition is critical. This paper experimentally quantifies for the first time a Hydrogen Bond‐Enhanced Halogen Bond (HBeXB) without the complexities of protein structure or preorganization. An HBeXB is a halogen bond that has been strengthened when the halogen donor simultaneously accepts a hydrogen bond. Our theoretical studies suggest that electron‐rich halogen bond donors are strengthened most by an adjacent hydrogen bond. Furthermore, stronger hydrogen bond donors enhance the halogen bond the most. X‐ray crystal structures of halide complexes (X − =Br − , I − ) reveal that HBeXBs produce shorter halogen bonds than non‐hydrogen bond analogues. 19 F NMR titrations with chloride highlight that the HBeXB analogue exhibits stronger binding. Together, these results form the foundation for future studies concerning hydrogen bonds and halogen bonds in close proximity.
Decato, Daniel A.↗
The Influence of C(sp 3 )H–Selenium Interactions on the 77 Se NMR Quantification of the π-Accepting Properties of Carbenes
Selenium NMR has become a standard tool for scaling the p-accepting character of carbenes. Herein, we highlight that non-classical hydrogen bonding (NCHB), likely resulting from hyperconjugation, can play a significant role in the carbene–selenium 77 Se NMR chemical shift, thus triggering a non-linear behavior of the Se-Scale.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Theoretical, Solid-State, and Solution Quantification of the Hydrogen Bond-Enhanced Halogen Bond
Proximal noncovalent forces are commonplace in natural systems and understanding the consequences of their juxtaposition is critical. This paper experimentally quantifies for the first time a Hydrogen Bond-Enhanced Halogen Bond (HBeXB) without the complexities of protein structure or preorganization. An HBeXB is a halogen bond that has been strengthened when the halogen donor simultaneously accepts a hydrogen bond. In this work, our theoretical studies suggest that electron-rich halogen bond donors are strengthened most by an adjacent hydrogen bond. Furthermore, stronger hydrogen bond donors enhance the halogen bond the most. X-ray crystal structures of halide complexes (X - =Br - , I - ) reveal that HBeXBs produce shorter halogen bonds than non-hydrogen bond analogues. 19 F NMR titrations with chloride highlight that the HBeXB analogue exhibits stronger binding. Together, these results form the foundation for future studies concerning hydrogen bonds and halogen bonds in close proximity.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Propane Activation on Pt Electrodes at Room Temperature: Quantification of Adsorbate Identity and Coverage
Abstract C−H bond activation is the first step in manufacturing chemical products from readily available light alkane feedstock and typically proceeds via carbon‐intensive thermal processes. The ongoing emphasis on decarbonization via electrification motivates low‐temperature electrochemical alternatives that could lead to sustainable chemicals production. Platinum (Pt) electrocatalysts have shown activity towards reacting alkanes; however, little is known about propane electrocatalytic activation and conditions suitable for enabling selective oxidation to valuable products. Herein, we utilize a combination of electrochemical mass spectrometry (ECMS) and density functional theory (DFT) calculations to elucidate the potential dependence of propane activation on Pt electrocatalysts. Results show a strong dependence of adsorption on the applied potential in room‐temperature aqueous acidic electrolyte, with a maximum coverage of propane‐derived adsorbates at 0.30 V vs RHE. Using charge deconvolution and deuterated experiments, the mechanism of adsorption was elucidated, and C 3 H 2 * was determined as the average dehydrogenated propane‐derived adsorbate species. DFT calculations further corroborate these results, showing that the formation of deeply dehydrogenated species is energetically accessible at room temperature. The combined theoretical and experimental findings yield insights for selective activation of paraffinic C−H bonds at room temperature, aqueous conditions—a critical step towards decarbonized chemical manufacturing.
Chemistry↗
Quantification of Brønsted Acid Sites in Zeolites by Water Desorption Thermogravimetry
The many studies that have been conducted on water adsorption on zeolites were carried out in “adsorption” mode, from adsorption isotherms or single-point measurements, with the samples being first degassed at high temperature, and then exposed to water vapor at variable partial pressure. Here, we report Thermogravimetric Analysis (TGA) studies of HZSM-5 saturated with water at room temperature, thus studying water adsorption in “desorption” mode. In this work, TGA was used to ascertain the amount of water remaining as a function of the pretreatment temperature, and to identify the temperature at which one water molecule per framework Al atom persists. Solid-state 1H NMR spectroscopy, combined with molecular modeling, provided evidence for water molecules hydrogen bonded to Brønsted acid sites and formation of oxonium species for distinct water per framework Al atom. TGA is shown to be a simple and expeditious technique to measure the number of Brønsted acid sites in aluminosilicate zeolites, with advantages over the traditional methods of IR or TPD of dangerous adsorbed ammonia or pyridine. TGA of adsorbed water also provides information on the water clusters formed at the acid sites of HZSM-5, which are proportional to the number of acid sites.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Quantification of modeling uncertainty in the Rayleigh damping model
Understanding and accurately characterizing energy dissipation mechanisms in civil structures during earthquakes is an important element of seismic assessment and design. The most commonly used model is attributed to Rayleigh. This paper proposes a systematic approach to quantify the uncertainty associated with Rayleigh's damping model. Bayesian calibration with embedded model error is employed to treat the coefficients of the Rayleigh model as random variables using modal damping ratios. Through a numerical example, we illustrate how this approach works and how the calibrated model can address modeling uncertainty associated with the Rayleigh damping model.
42 ENGINEERING↗
Quantification of microcystin production and biodegradation rates in the western basin of Lake Erie
Abstract Cyanobacterial biomass forecasts currently cannot predict the concentrations of microcystin, one of the most ubiquitous cyanotoxins that threaten human and wildlife health globally. Mechanistic insights into how microcystin production and biodegradation by heterotrophic bacteria change spatially and throughout the bloom season can aid in toxin concentration forecasts. We quantified microcystin production and biodegradation during two growth seasons in two western Lake Erie sites with different physicochemical properties commonly plagued by summer Microcystis blooms. Microcystin production rates were greater with elevated nutrients than under ambient conditions and were highest nearshore during the initial phases of the bloom, and production rates were lower in later bloom phases. We examined biodegradation rates of the most common and toxic microcystin by adding extracellular stable isotope‐labeled microcystin‐LR (1 μ g L −1 ), which remained stable in the abiotic treatment (without bacteria) with minimal adsorption onto sediment, but strongly decreased in all unaltered biotic treatments, suggesting biodegradation. Greatest biodegradation rates (highest of −8.76 d −1 , equivalent to the removal of 99.98% in 18 h) were observed during peak bloom conditions, while lower rates were observed with lower cyanobacteria biomass. Cell‐specific nitrogen incorporation from microcystin‐LR by nanoscale imaging mass spectrometry showed that a small percentage of the heterotrophic bacterial community actively degraded microcystin‐LR. Microcystin production and biodegradation rates, combined with the microcystin incorporation by single cells, suggest that microcystin predictive models could be improved by incorporating toxin production and biodegradation rates, which are influenced by cyanobacterial bloom stage (early vs. late bloom), nutrient availability, and bacterial community composition.
59 BASIC BIOLOGICAL SCIENCES↗