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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Rapid Coal-Ash Characterization using Geophysical Methods & Machine Learning

Coal combustion products (CCP) are challenging to delineate in heterogeneous field settings. Conventional methods (test pits, coring, and laboratory analyses) are labor-intensive, slow, invasive, and provide sparse spatial coverage. This study evaluates whether rapid non-invasive geophysical screening methods—induced polarization (IP), magnetic susceptibility, and nuclear magnetic resonance (NMR) —combined with surface colorimetry (RGB_24), can discriminate CCP-soil mixtures and provide reliable estimates of CCP content. Laboratory measurements were collected on five CCP-soil mixtures (series) and modeled using (i) a linear baseline, (ii) a calibrated non-linear (power-mean) model, and (iii) a machine-learning (ML) Random Forest approach, with validation via leave-one-series-out and site-specific tests. Across the five series, individual signals—particularly IP and magnetic susceptibility—were strongly predictive of ash content but were consistently outperformed by combined models. The pooled calibrated non-linear and ML models captured the observed non-linearity and achieved high accuracy and precision, improving on linear fits. Colorimetry showed the weakest direct relationship with ash content for the tested samples but improved performance when included in multi-signal models. At pre-selected 3.5% decision threshold, calibrated and ML approaches yielded near-perfect classification (Matthews correlation coefficient ˜ 1), suggesting strong practical operability for field screening. Additionally, field-analog tests highlighted the role of endmembers—accuracy declined without access to end-member measurements but was largely recovered by collecting a minimal labeled pair for local recalibration. With end members, accuracy remained high. Globally trained models performed well on three operational unknowns; however, series-specific refits provided the most accurate predictions. Overall, these results highlight the potential of combining rapid geophysics and minimal local calibration for improved coal-ash delineation.

Peshtani, Klaudio↗
DOE OSTI2025

Characterization of Caenorhabditis elegans sphingomyelin synthases through heterologous expression

Sphingomyelin (SM) is a major component of mammalian cell membranes and particularly abundant in the myelin sheath that surrounds nerve fibers. Its production is catalyzed by SM synthases SMS1 and SMS2, which interconvert phosphatidylcholine and ceramide to diacylglycerol and SM in the Golgi and at the plasma membrane, respectively. As the lipids participating in this reaction fulfill both structural and signaling functions, SMS enzymes have considerable potential to influence diverse important cellular processes. The nematode Caenorhabditis elegans is an attractive model for studying both animal development and human disease. The organism contains five SMS homologues but none of these have been characterized in any detail. Here, we carried out the first systematic analysis of SMS family members in C. elegans . Using heterologous expression systems, genetic ablation, metabolic labeling and lipidome analyses, we show that C. elegans harbors at least three distinct SM synthases and one ceramide phosphoethanolamine (CPE) synthase. Moreover, C. elegans SMS family members have partially overlapping but also unique sub-cellular distributions and together occupy all principal compartments of the secretory pathway. Our findings shed light on crucial aspects of sphingolipid metabolism in a valuable animal model and opens avenues for exploring the role of SM and its metabolic intermediates in organismal development.

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

Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization

Objective: The growing availability of electronic health records (EHR) data opens opportunities for integrative analysis of multi-institutional EHR to produce generalizable knowledge. A key barrier to such integrative analyses is the lack of semantic interoperability across different institutions due to coding differences. We propose a Multiview Incomplete Knowledge Graph Integration (MIKGI) algorithm to integrate information from multiple sources with partially overlapping EHR concept codes to enable translations between healthcare systems. Methods: The MIKGI algorithm combines knowledge graph information from (i) embeddings trained from the co-occurrence patterns of medical codes within each EHR system and (ii) semantic embeddings of the textual strings of all medical codes obtained from the Self-Aligning Pretrained BERT (SAPBERT) algorithm. Due to the heterogeneity in the coding across healthcare systems, each EHR source provides partial coverage of the available codes. MIKGI synthesizes the incomplete knowledge graphs derived from these multi-source embeddings by minimizing a spherical loss function that combines the pairwise directional similarities of embeddings computed from all available sources. MIKGI outputs harmonized semantic embedding vectors for all EHR codes, which improves the quality of the embeddings and enables direct assessment of both similarity and relatedness between any pair of codes from multiple healthcare systems. Results: With EHR co-occurrence data from Veteran Affairs (VA) healthcare and Mass General Brigham (MGB), MIKGI algorithm produces high quality embeddings for a variety of downstream tasks including detecting known similar or related entity pairs and mapping VA local codes to the relevant EHR codes used at MGB. Based on the cosine similarity of the MIKGI trained embeddings, the AUC was 0.918 for detecting similar entity pairs and 0.809 for detecting related pairs. For cross-institutional medical code mapping, the top 1 and top 5 accuracy were 91.0% and 97.5% when mapping medication codes at VA to RxNorm medication codes at MGB; 59.1% and 75.8% when mapping VA local laboratory codes to LOINC hierarchy. When trained with 500 labels, the lab code mapping attained top 1 and 5 accuracy at 77.7% and 87.9%. MIKGI also attained best performance in selecting VA local lab codes for desired laboratory tests and COVID-19 related features for COVID EHR studies. Compared to existing methods, MIKGI attained the most robust performance with accuracy the highest or near the highest across all tasks. Conclusions: The proposed MIKGI algorithm can effectively integrate incomplete summary data from biomedical text and EHR data to generate harmonized embeddings for EHR codes for knowledge graph modeling and cross-institutional translation of EHR codes.

Zhou, Doudou↗
DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Recombinant expression of computationally designed peptide-bundlemers in Escherichia coli

Computational design of fully artificial peptides is extensively researched by material scientists and engineers for the construction of novel nanostructures and biomaterials. Such design has yielded a peptide-based building block or bundlemer, a coiled coil peptide assembly that undergoes further physical-covalent interactions to form 1D, 2D and, potentially, 3D hierarchical assemblies and displays targeted and biomimetic material properties. Recombinant expression is a convenient, flexible tool to synthesize such artificial and modified peptides in large quantities while also enabling economical synthesis of isotopically labeled peptides and longer protein-like artificial peptides. This report describes the protocol for recombinant expression of a 31-amino acid, computationally designed bundlemer-forming peptide in Escherichia coli. Peptide yields of 10 mgs per liter of media were achieved which highlights complementary advantages of recombinant expression technique relative to conventional laboratory-scale synthesis, such as solid-phase peptide synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantification of active sites in yttrium containing dealuminated Beta zeolites during conversion of ethanol and acetaldehyde to butadiene

Here, in this work, yttrium containing dealuminated Beta zeolites (Y/deAlBeta) were synthesized and characterized by various spectroscopic techniques to improve understanding of ethanol upgrading over these materials. Characterization results indicate yttrium atoms partially condense with framework silanol nests formed during dealumination of parent Al-Beta supports. Active sites for conversion of ethanol and acetaldehyde to butadiene were quantified on a series of Y/deAlBeta catalysts (0.1–10 wt% yttrium) via ex situ chemisorption and transmission Fourier transformed infrared (FTIR) spectroscopy measurements by first measuring the integrated molar extinction coefficient (IMEC) for pyridine bound to Lewis acidic yttrium sites. In situ titrations with pyridine demonstrate that the number of sites quantified by ex situ chemisorption IR is quantitatively similar to the number of sites that catalyze butadiene formation, which varies (from 0.05 to 0.35) across the series of catalysts. In situ pyridine titrations impact butadiene site time yields (STY), but not crotonaldehyde STY, indicating that a distribution of yttrium sites is present, and that discrete yttrium site types participate in distinct steps in the pathway from ethanol to butadiene. Apparent kinetic parameters including activation energies and reaction orders were measured, these suggest differences in reactant (or reactant-derived intermediate) surface coverages result in higher STYs (per mol Y or per Lewis acidic Y site) for samples with low Y loadings relative to those with higher Y loadings. Isotopic labeling experiments evince the existence of other kinetically relevant steps in addition to the crotonaldehyde transformation to crotyl alcohol. Together, these findings provide further guidance into the heterogeneities in site structures in yttrium-containing zeolites and their relevance for the various steps in the pathway from ethanol to C 4 products useful for production of sustainable aviation fuel and renewable butadiene.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Elucidating the role of surface species in CO oxidation catalyzed by boron nitride nanotube supported transition metal oxides

Boron nitride nanotube (BNNT) is considered a highly promising catalyst support due to its outstanding thermal stability and chemical inertness. These characteristics make BNNT an attractive alternative for high-temperature applications. However, most studies to date have focused on incorporating platinum group metals (PGMs) to achieve high activity. Although BNNT-supported PGM catalysts are highly effective, their scarcity and high cost hinder widespread use in industrial processes. In this study, BNNT-supported transition metal oxides (TMO x /BNNT; TM = Fe, Co, Ni, and Cu) catalysts were investigated, and CO oxidation was applied as a model reaction to evaluate their catalytic performance. Several characterization techniques, including SEM-EDX, TEM, SXRD, H 2 -TPR, and XPS, were employed to examine their physicochemical properties. Notably, the particle size of the metal oxides differed significantly depending on the metal type. This variation is primarily attributed to the inherent metal–support interactions and the thermodynamic stability of each oxide during synthesis. These properties also affected catalytic activity, and various parameters, such as oxygen mobility and redox behavior, played important roles in determining performance. Finally, in situ DRIFTS, CO-TPSR, reaction-order analysis, and 18 O 2 isotope-labeling experiment were used to investigate the reaction mechanism. In conclusion, the findings provide insights into the design of cost-effective BNNT-supported catalysts and highlight their potential applicability in oxidation reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at , but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. As a result, we also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A comparative study of machine learning models for predicting the state of reactive mixing

Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Here, to investigate the evolution of mixing dynamics under different scenarios (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. The outputs comprised concentration profiles of reactants and products. The inputs to and outputs from these simulations were concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) models intended to emulate system behavior. These 20 ML emulators, based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptrons (MLPs), were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay (i.e., average concentration, square of average concentration), and degree of mixing (i.e., variances of species concentration). Unsurprisingly, linear classifiers and regressors failed to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP model accurately classified the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predicted the QoIs. At run time, trained ML emulators produced results times faster than the finite-element simulations. Due to their low computational expense and high accuracy, ensemble and MLP models are excellent emulators for these numerical simulations and great utilities in uncertainty quantification exercises, which can require 1,000s of forward model runs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Engineering uranyl-chelating peptides from NikR for electrochemical peptide-based sensing applications

We have, for the first time, designed three uranyl-chelating peptides by modeling after the uranyl binding pocket found in a mutated Ni(II)-dependent transcriptional repressor (NikR). All three thiolated and methylene blue (MB)-modified peptides, NikR-5, NikR-11, and NikR-15, contain the five core amino acids responsible for target recognition, but the two longer peptides have either one or two additional glycine residues in between the five amino acids. These three peptide probes were then used in the fabrication of electrochemical peptide-based (E-PB) uranyl ion (U(VI)) sensors, with the goal of elucidating the effects of the added glycine residues and probe flexibility on target recognition. The sensing mechanism is similar to other “signal-off” E-PB sensors, in which binding of the target rigidifies the probe, resulting in a decrease in the redox signal from the tethered MB label. Although all three sensors responded to U(VI), the NikR-15 sensor's behavior was irreproducible and thus precluded from the rest of the study. The NikR-11 sensor showed the largest response to U(VI), whereas the NikR-5 sensor showed higher specificity for U(VI). The limit of detection was 50 nM for both sensors, which is well below the U.S. Environmental Protection Agency maximum contaminant level for uranium. Both sensors were further tested and proven functional in a 50% synthetic aquifer sample. Overall, the NikR-11 sensor, fabricated with the 11-amino acid probe, is deemed the optimal design. Incorporating glycine residues is a strategic way to lengthen the peptide slightly to improve target binding. This simple yet versatile approach to recreating binding pockets on electrode surfaces can potentially be employed in the design of other E-PB and surface-based metal ion sensors.

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

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗
DOE OSTI2022

Double echo symmetry-based REDOR and RESPDOR pulse sequences for proton detected measurements of heteronuclear dipolar coupling constants

1 H{X} symmetry-based rotational echo double resonance pulse sequences (S-REDOR) and symmetry-based rotational echo saturation pulse double resonance (S-RESPDOR) solid-state NMR experiments have found widespread application for 1 H detected measurements of difference NMR spectra, dipolar coupling constants, and internuclear distances under conditions of fast magic angle spinning (MAS). In these experiments the supercycled $\text{R4}^2_1 (\text{SR4}^2_1)$ symmetry-based recoupling pulse sequence is typically applied to the 1 H spins to reintroduce heteronuclear dipolar couplings. However, the timing of $\text{SR4}^2_1$ and other symmetry-based pulse sequences must be precisely synchronized with the rotation of the sample, otherwise, the evolution of 1 H CSA and other interactions will not be properly refocused. For this reason, significant distortions are often observed in experimental dipolar dephasing difference curves obtained with S-REDOR or S-RESPDOR pulse sequences. Here we introduce a family of double echo (DE) S-REDOR/S-RESPDOR pulse sequences that function in an analogous manner to the recently introduced $t_1$-noise eliminated (TONE) family of dipolar heteronuclear multiple quantum coherence (D-HMQC) pulse sequences. Through numerical simulations and experiments the DE S-REDOR/S-RESPDOR sequences are shown to provide dephasing difference curves similar to those obtained with S-REDOR/S-RESPDOR. However, the DE sequences are more robust to the deviations of the MAS frequency from the ideal value that occurs during typical solid-state NMR experiments. In this work, the DE sequences are shown to provide more reliable 1 H detected dipolar dephasing difference curves for nuclei such as 15 N (with isotopic labelling), 183 W and 35 Cl. The double echo sequences are therefore recommended to be used in place of conventional S-REDOR/S-RESPDOR sequences for measurement of weak dipolar coupling constants and long-range distances.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Proton-detected 15 N- 1 H dipolar coupling/ 1 H chemical shift correlation experiment for the measurement of NH distances in biological solids under fast MAS solid-state NMR

Measurement of distances from dipolar couplings is essential for structural characterization, refinement and validation using the solid-state nuclear magnetic resonance (ssNMR) spectroscopy. Particularly, knowledge about NH dipolar interactions in biological solids is important for understanding the hydrogen (H)-bonding interactions, molecular geometry and spin dynamics. In this regard, we have proposed a proton-detected two-dimensional (2D) 15 N- 1 H dipolar coupling/ 1 H chemical shift correlation experiment using the C-symmetry based windowless recoupling of chemical shift anisotropy (ROCSA) in combination with the DIPSHIFT pulse-based method for the measurement of short NH distances in the isotopically labeled and naturally abundant biological solids at fast magic angle spinning (MAS) rates (40–70 kHz). Our proposed method results in undistorted recoupled 15 N- 1 H dipolar coupling powder lineshapes that are free from the recoupled 1 H CSA contributions under the 15 N evolution, a feature that is essential for the measurement of NH distances with improved accuracy (± 500 Hz in terms of the NH dipolar couplings). The pulse sequence developed in the present study is also insensitive to the 1H–1H homonuclear dipolar interactions, relaxation effects owing to its constant-time implementation, and t1-noise from the fluctuations in the MAS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Efficient 15 N hyperpolarization of [ 15 N 3 ]metronidazole antibiotic via spin-relayed pulsed SABRE-SHEATH

Signal Amplification by Reversible Exchange in SHield Enables Alignment Transfer to Heteronuclei (SABRE-SHEATH) is an NMR hyperpolarization technique that relies of the simultaneous exchange of parahydrogen and a to-be-hyperpolarized molecule on the metal center of a polarization-transfer catalyst in a microtesla magnetic field. Until recently, this method has been understood to perform hyperpolarization by establishing level anti-crossings between the nuclear spins of the parahydrogen derived hydrides (acting as a source of hyperpolarization) and those of the substrate. Recently, the application of highly non-intuitive pulse sequences (comprising pulses of microtesla DC fields) was predicted to hyperpolarize nuclear spins more efficiently than the canonical (static-field) SABRE-SHEATH approach. Here we show that by employing a basic “on-off” pulse sequence of rectangular microtesla pulses, it is possible to improve the hyperpolarization efficiency for SABRE-SHEATH of [ 15 N 3 ]metronidazole, an FDA-approved antibiotic (in non-enriched and non-hyperpolarized form) and potential hypoxia sensing molecule. Specifically, we demonstrate that 15N polarization of 18.5 % can be obtained in 80 s of parahydrogen bubbling parahydrogen through a solution containing 20 mM [ 15 N 3 ]metronidazole. In practice, (1.32 ± 0.14)-fold improvements in P 15N was obtained with the pulsed method described here compared to static field technique variant. These results show that pulsed SABRE-SHEATH was successfully applied to 15 N-labeled biologically relevant molecule. Moreover, we also demonstrate that although the pulsed SABRE-SHEATH sequence was designed for polarization transfer from parahydrogen derived hydrides to the metronidazole’s 15 N catalyst-binding site, all three 15 N sites of [ 15 N 3 ]metronidazole attained the hyperpolarized state. This spin-relayed polarization transfer becomes possible due to the 15 N relay network established by their spin-spin J-couplings. The feasibility of the spin-relayed polarization transfer is demonstrated here for the first time for pulsed SABRE-SHEATH (as opposed to the static-field SABRE-SHEATH reported previously) and it paves the way to broad applicability of the technique.

Hyperpolarization↗
DOE OSTI2022

Correlative nano-imaging of metals and proteins in primary neurons by synchrotron X-ray fluorescence and STED super resolution microscopy: Experimental validation

It is becoming increasingly clear that biological metals such as iron, copper or zinc are involved in synaptic functions, and in particular in the mechanisms of synaptogenesis and subsequent plasticity. Understanding the role of metals on synaptic functions is a difficult challenge due to the very low concentration of these elements in neurons and to the submicrometer size of synaptic compartments. To address this challenge we have developed a correlative nano-imaging approach combining metal and protein detection. First, stimulated emission depletion (STED) microscopy, a super resolution optical microscopy technique, is applied to locate fluorescently labeled proteins. Then, synchrotron radiation induced X-ray fluorescence (SXRF) is performed on the same regions of interest, e.g. synaptic compartments. Here, we present here the principle scheme that allows this correlative nano-imaging and its experimental validation. We applied this correlative nano-imaging to the study of the physiological distribution of metals in synaptic compartments of primary rat hippocampal neurons. We thus compared the nanometric distribution of metals with that of synaptic proteins, such as PSD95 or cytoskeleton proteins. Compared to correlative imaging approaches currently used to characterize synaptic structures, such as electron microscopy correlated with optical fluorescence, our approach allows for ultra-sensitive detection of trace metals using highly focused synchrotron radiation beams. We provide proof-of-principle for correlative imaging of metals and proteins at the synaptic scale and discuss the present limitations and future developments in this area.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Analysis

We present a novel unsupervised machine learning method for quantitative representation of scanning electron micrographs and its applications and performance for nuclear forensic analysis of uranium ore concentrates. The method uses a vector quantizing variational autoencoder followed by a histogram operation to encode a micrograph into a single dimensional representation, called the latent vector. The method requires no extant labeling of the data and can be applied over large datasets of micrographs with minimal human interaction. The representations generated are broadly descriptive of each micrograph and the microstructure of the material imaged. In the case of uranium ore concentrate analysis, the representations were amenable to processing reagent and ore concentrate species classification with accuracy of 81:8%, which is competitive with state-of-the-art supervised networks. The representations were also used to classify previously unseen processing routes, were able to classify imaging parameters such as magnification (to 76:0% accuracy), were able to classify fine grained process parameters such as calcining temperature (to 74:4% accuracy), and their informatic properties indicate that they are generally descriptive of the image represented. This method can be applied across microstructure analysis fields to perform quantitative analysis without the need for labor intensive and possibly biased human analysis.

Scanning Electron Microscopy, Vector Quantizing Va↗
DOE OSTI2021

Automatic information extraction from neutron radiography imaging to estimate axial fuel expansion in $\mathrm{EBR-II}$

Approximately 130,000 metal fuel pins were irradiated in the Experimental Breeder Reactor II (EBR-II) during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed to characterize fuel behavior, like fuel swelling. However, due to the lack of technology or resources, many of the images have not been processed or were processed manually through visual examination. This paper represents first-attempt to develop an image processing algorithm capable of automatically extracting information regarding the degree of fuel swelling from neutron radiography imaging. The algorithm was applied to 120 images of three different metallic fuel pin compositions—U-10Zr, U-8Pu-10Zr, and U-19Pu-10Zr. The algorithm performs operations of image intensity adjustment, image binarization, region finding, and labeling to extract information about fuel swelling. The average growth for U-10Zr was found to be 8.49% with 95% Confidence Interval (CI) [8.33 –8.66%], for U-8Pu-10Zr — 7.50% with 95% CI [7.23 – 7.78%], and for U-19Pu-10Zr — 3.15%, with 95% CI [2.40 – 3.91%]. The results obtained by applying this automatic image processing algorithm are consistent with previously reported studies of the same types of fuels. The automatic image processing algorithm will be expanded to include thousands of available neutron radiography images and different types of fuels to investigate empirical dependencies of fuel swelling, which can be subsequently applied to advanced fuel modeling. Results from this study can later be compared to BISON simulations to further benchmark modeling efforts and develop assessment cases.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Battery passports for promoting electric vehicle resale and repurposing

Starting in 2026, most batteries sold in the European Union will require a battery passport. This initiative is part of a broader legislative framework, led by the European Commission, to improve sustainable battery materials sourcing practices and enable a circular economy. All batteries having a capacity of greater than 2 kilowatt-hours will be covered under the legislation, effectively encompassing all electric vehicle (EV) batteries and stationary grid storage batteries. At its core, a battery passport defines a minimum, standard set of information that must be reported for every battery made and sold (see Figure 1). Legislative actions have so far focused on labeling standards enabling raw material traceability and recycling, which has received ongoing support from the European Council and Parliament. Furthermore, work remains to finalize the electrochemical performance and durability minimum reporting requirements by 2024, which has been challenging since performance and durability are subject to many different interpretations.

25 ENERGY STORAGE↗