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At least 739 records · Page 41

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DOE OSTI2025

Molecular Modeling and Molecular Dynamics Simulation of a Packed and Intact Bacterial Microcompartment

Bacterial microcompartments (BMCs) are protein-bound organelles found in some bacteria which encapsulate enzymes for enhanced catalytic activity. These compartments spatially sequester enzymes within semipermeable shell proteins and are packed full of enzyme cargoes and metabolites as they fulfill their function. Coupling together recent SAXS and proteomics work, it is possible to develop molecular models for these microcompartments and interrogate enzyme and metabolite dynamics within. Our primary goal of this study is to quantify the permeability of metabolite glyceraldehyde-3-phosphate (G3P) and dihydroxyacetone phosphate (DHAP) across the BMC shell through classical molecular dynamics simulation. The Haliangium ochraceum model of BMC shell (PDB: 6MZX) was used to model an intact BMC of approximately 10 million atoms. Working at this scale presented its own challenges in managing large data sets, with multiple challenges and hardware advances discussed that facilitated this work. Over approximately 750 ns of aggregate simulation, we see multiple permeation events for these metabolites that were added at high concentration through the pores present within BMC shell tiles. When compared to independent permeability estimates for the same metabolites determined through replica exchange umbrella sampling simulations, the permeabilities varied by approximately 3 orders of magnitude. Regardless, the permeability coefficients for both G3P and DHAP are highly similar and very high, such that only very small concentration gradients can be maintained across the BMC shell between the cytosol and BMC interior. The large simulation systems also facilitated comparisons for molecular diffusivity in the crowded environment within the BMC shell. By our estimates, the viscosity within a packed BMC shell is at least 10-fold higher than it would be in neat solution and is the real driver for varying permeability estimates we obtained through simulation. These findings will be used as design inputs for future bioengineering efforts to make products from BMCs, highlighting how permeable BMC shells can be.

Diffusion↗

DOE OSTI2025

Decayheatml

This code is designed to predict and analyze the decay heat generated in molten salt reactors (MSRs) using a hybrid approach that combines machine learning and segmented polynomial fitting. The accurate prediction of decay heat is essential for reactor safety and the optimization of spent fuel storage. The code operates through several key components: 1) Data Architecture: It incorporates a modular data architecture that handles various MSR-specific operational parameters such as power density, humidity content, and air ingress. These parameters are sampled using Sobol sequences to ensure comprehensive coverage of operational uncertainties. 2) Machine Learning Framework: The code employs a diverse set of machine learning models, including polynomial regression, decision trees, random forests, gradient boosting, support vector regression, k-nearest neighbors, multi-layer perceptrons, and symbolic regression. These models are trained to predict decay heat over a wide temporal range, from immediate shutdown up to 10,000 years. 3) Region-Optimized Training: The temporal domain is divided into multiple regions, each modeled separately to capture distinct decay heat characteristics across different time scales. This approach significantly improves the accuracy and interpretability of predictions. 4) Segmented Polynomial Interpretation (SPI): The SPI method translates machine learning predictions into piecewise polynomial equations. These equations are physically interpretable and can be directly integrated into existing engineering workflows and safety analyses. 5) Front-End Interfaces: The code includes both a Jupyter notebook interface for research development and a Streamlit web application for operational deployment. These interfaces allow users to interactively explore decay heat predictions, adjust operational parameters, and visualize results in real-time. 6) Applications: The framework supports various applications, including safety system validation and spent fuel container optimization. It enables real-time evaluation of worst-case decay heat scenarios, informing the design of passive safety systems and optimizing container designs for long-term storage. Overall, this code provides a robust, accurate, and user-friendly tool for predicting decay heat in MSRs, enhancing reactor safety, and optimizing spent fuel management.

Retamales, Mauricio Eduardo Tano [Idaho National L↗

NASA NTRS1971

Induced and permanent magnetism on the moon - Structural and evolutionary implications.

It is shown that the moon possesses an extraordinary response to induction from the solar wind due to a combination of a high interior electrical conductivity together with a relatively resistive crustal layer into which the solar wind dynamic pressure forces back the induced field. The dark side response, devoid of solar wind pressure, is approximately that expected for the vacuum case. These data permit an assessment of the interior conductivity and an estimate of the thermal gradient in the crustal region. The discovery of a large permanent magnetic field at the Apollo 12 site corresponds approximately to the paleomagnetic residues discovered in both Apollo 11 and 12 rock samples. The implications regarding an early lunar magnetic field are discussed and it is shown that among the various conjectures regarding the early field the most prominent are either an interior dynamo or an early approach to the earth though no extant model is free of difficulties.

Sonett, C. P.↗

NASA NTRS1989

Simulated space-based Doppler lidar performance in regions of backscatter inhomogeneities

The prospect of obtaining directly measured winds on a global scale has raised questions about the expected quality of the lidar wind measurements and the potential for biases due to sampling patterns and line-of-sight impediments. Extensive computer simulations are ongoing to address these and other issues. One source of measurement bias is found in regions of the atmosphere where gradients in both lidar backscatter and the winds occur together. The potential biases that result are identified and their magnitudes estimated.

Emmitt, G. D.↗

NASA NTRS1999

Directional Solidification of Bi-Sn on USMP-4

The experiments used MEPHISTO hardware to study the solidification and melting behavior of bismuth alloyed with 1 at% tin. Three samples, each approximately 900 mm long and 6mm in diameter, were used. A portion of each sample also included a 2 mm diameter growth capillary, to assist in the formation of a single grain. One sample provided the Seebeck voltage generated during melting and freezing processes. Another provided temperature data and Peltier pulsed demarcation of the interface shape for post flight analysis. The third sample provided resistance and growth velocity measurements, as well as additional thermal data. The third sample was also quenched at the end of the mission to preserve the composition of the liquid near the interface for post flight determination. A total of 450mm of directionally solidified samples were preserved for post mission structural and compositional characterization. Substantial differences were observed in the Seebeck signal between the ground-based experiments and the space-based experiments. The temperature gradient in the liquid for the ground-based experiments was significantly lower than the temperature gradient in the liquid for the space-based experiments.

Abbaschian, Reza↗

NASA NTRS1998

A natural view of microbial biodiversity within hot spring cyanobacterial mat communities

This review summarizes a decade of research in which we have used molecular methods, in conjunction with more traditional approaches, to study hot spring cyanobacterial mats as models for understanding principles of microbial community ecology. Molecular methods reveal that the composition of these communities is grossly oversimplified by microscopic and cultivation methods. For example, none of 31 unique 16S rRNA sequences detected in the Octopus Spring mat, Yellowstone National Park, matches that of any prokaryote previously cultivated from geothermal systems; 11 are contributed by genetically diverse cyanobacteria, even though a single cyanobacterial species was suspected based on morphologic and culture analysis. By studying the basis for the incongruity between culture and molecular samplings of community composition, we are beginning to cultivate isolates whose 16S rRNA sequences are readily detected. By placing the genetic diversity detected in context with the well-defined natural environmental gradients typical of hot spring mat systems, the relationship between gene and species diversity is clarified and ecological patterns of species occurrence emerge. By combining these ecological patterns with the evolutionary patterns inherently revealed by phylogenetic analysis of gene sequence data, we find that it may be possible to understand microbial biodiversity within these systems by using principles similar to those developed by evolutionary ecologists to understand biodiversity of larger species. We hope that such an approach guides microbial ecologists to a more realistic and predictive understanding of microbial species occurrence and responsiveness in both natural and disturbed habitats.

Review↗

DOE OSTI2021

Revisiting Dropout: Escaping Pressure for Training Neural Networks with Multiple Costs

A common approach to jointly learn multiple tasks with a shared structure is to optimize the model with a combined landscape of multiple sub-costs. However, gradients derived from each sub-cost often conflicts in cost plateaus, resulting in a subpar optimum. In this work, we shed light on such gradient conflict challenges and suggest a solution named Cost-Out, which randomly drops the sub-costs for each iteration. We provide the theoretical and empirical evidence of the existence of escaping pressure induced by the Cost-Out mechanism. While simple, the empirical results indicate that the proposed method can enhance the performance of multi-task learning problems, including two-digit image classification sampled from MNIST dataset and machine translation tasks for English from and to French, Spanish, and German WMT14 datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

The effect of secondary phases on microstructure and irradiation damage in an as-built additively manufactured 316 L stainless steel with a hafnium compositional gradient

Additive manufacturing (AM) or rapid prototyping has become a crucial tool for reducing both cost and time while increasing efficiency in qualifying structural materials for reactor use. In this study, directed energy deposition (DED) was used to develop an as-built 316 L stainless steel sample with three regions of increasing Hf-dopant to study the effects of Hf on the irradiation response of the material. Morphological and microstructural changes were analyzed before and after 2 MeV proton irradiation at 360 °C to a damage of 2.5 dpa at ~ 10 µm below the surface. The addition of Hf effectively suppressed radiation-induced damage (dislocation loops, radiation-induced segregation) due to enhanced point defect recombination. The radiation damage seen in the as-built sample was further compared to a thermo-mechanically treated counterpart of the same fabrication and irradiation parameters which was found to behave superiorly. In conclusion, the increased radiation resistance of this material may be attributed to the as-built microstructure, which includes undissolved Hf particles, delta ferrite grains and cellular sub-grain boundaries that can hinder defect motion.

316L Stainless Steel↗

DOE OSTI2022

Heterogeneous phase transformation pathways in additively manufactured Al-Ce-Mn alloys

Heat treatment of additively manufactured Al-Ce based multicomponent alloys leads to complex microstructure evolution. In this research, the ability to extend the phase transformation theories involving nucleation of a product phase from a heterogeneous multi-phase microstructure typical to that of additively manufactured samples is explored. The Al-10Ce-8Mn (wt%) was used as a model alloy system. Under additive manufacturing conditions different solidification microstructures were obtained due to spatial and temporal variations of thermal gradients (G) and liquid-solid interface velocities (R) within a given melt pool. Near the melt pool boundary (high G and low R, referred as MPB region), initially, Al 20 Mn 2 Ce forms from the liquid followed by a eutectic of FCC Al and Al 11 Ce 3 . In the melt pool interiors (low G and high R referred as ES region) a eutectic structure between FCC Al and Al 20 Mn 2 Ce is observed. During subsequent heat treatments, the MPB and ES regions transform into different sets of microstructures. In the MPB region, a fine globular microstructure containing FCC Al, Al 11 Ce 3 , Al 6 Mn, and Al 12 Mn results from the decomposition of Al 20 Mn 2 Ce. In the ES region a faceted Al 51 Mn 7 Ce 4 plate phase results from the decomposition of Al 20 Mn 2 Ce. The formation of the Al 51 Mn 7 Ce 4 phase within the eutectic microstructure at the boundaries of FCC Al and Al 20 Mn 2 Ce has not been reported in the literature. Further, these two distinct phase transformation pathways are rationalized based on the role of driving force on the nucleation of (Al 6 Mn) and/or metastable intermetallic (Al 51 Mn 7 Ce 4 ) phases at the interface of aluminum (FCC) and the non-equilibrium intermetallic (Al 20 Mn 2 Ce) phases.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Evaluation of Radiography for TRISO Buffer Layer Density Measurement

Tristructural isotropic (TRISO) fuel particles consist of a central uranium-bearing kernel and a series of coating layers designed to retain fission products and to ensure fuel performance. Several parameters such as thickness and density must be measured for these coating layers to show that they conform with fuel specifications. Current methods for measuring the density of pyrolytic carbon and silicon carbide layers (liquid gradient density column) and the buffer layer (mercury porosimetry) generate Resource Conservation and Recovery Act (RCRA) radiological-mixed waste. In addition, measurement of buffer and inner pyrolytic carbon layer densities require hot sampling or interrupted coating runs and the mercury porosimetry method used for buffer density measurement only measures the mean buffer density, not the interparticle distribution. A new approach has been evaluated to measure the density of coating layers in TRISO particles based on the dependence of x-ray attenuation in radiographs on material density. This method does not generate RCRA mixed waste, measures density on a particle-by-particle basis, and in principle is capable of measuring the density of all coating layers in a single process. Initial results using thinned TRISO particle sections to evaluate radiography measurement of density as a quality control characterization method are reported herein. In this work, the primary focus is on measurement of the density of the buffer layer; however, with appropriate calibration the method should be applicable to other coating layers. Improvements to the initial method and a full demonstration of the method on the remaining coating layers may be pursued as a future effort.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Integration of LIBS with Machine Learning for Real-Time Monitoring of Feedstock in H 2 Gasification Applications

This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Microscale Thermophoresis (MST) as a Tool to Study Binding Interactions of Oxygen-Sensitive Biohybrids

Microscale thermophoresis (MST) is a technique used to measure the strength of molecular interactions. MST is a thermophoretic-based technique that monitors the change in fluorescence associated with the movement of fluorescent-labeled molecules in response to a temperature gradient triggered by an IR LASER. MST has advantages over other approaches for examining molecular interactions, such as isothermal titration calorimetry, nuclear magnetic resonance, biolayer interferometry, and surface plasmon resonance, requiring a small sample size that does not need to be immobilized and a high-sensitivity fluorescence detection. In addition, since the approach involves the loading of samples into capillaries that can be easily sealed, it can be adapted to analyze oxygen-sensitive samples. In this Bio-protocol, we describe the troubleshooting and optimization we have done to enable the use of MST to examine protein–protein interactions, protein–ligand interactions, and protein–nanocrystal interactions. The salient elements in the developed procedures include 1) loading and sealing capabilities in an anaerobic chamber for analysis using a NanoTemper MST located on the benchtop in air, 2) identification of the optimal reducing agents compatible with data acquisition with effective protection against trace oxygen, and 3) the optimization of data acquisition and analysis procedures. The procedures lay the groundwork to define the determinants of molecular interactions in these technically demanding systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1981

Solutal convection induced macrosegregation and the dendrite to composite transition in off-eutectic alloys

The effect of solute gradient induced convection during vertical solidification on the macrosegregation of Pb-rich Pb-Sn off-eutectic alloys is determined experimentally as a function of composition and growth rate. In many cases macrosegregation is sufficient to prevent the plane front solidification of the alloy. The transition from dendritic to composite structure is found to occur when the composition of the solid is close enough to the eutectic composition to satisfy a stability criterion based on the ratio of the liquid temperature gradient to growth rate. A vertical or horizontal magnetic field of 0.1 T (1 kilogauss) does not reduce macrosegregation, but downward solidification (liquid below solid) virtually eliminates macrosegregation in small (3 mm) diameter samples.

Boettinger, W. J.↗

NASA NTRS1988

Program Collects And Analyzes Thermoelectric Data

Large gradient seebeck coefficient measurement computer program gathers many channels of data, performs analysis, and plots each set of data simutaneously. User can modify code to alter number of channels of data collected and sampling rate and equations that process data. Written in HP Basic. Program also adaptable to other types of data.

Chmielewski, Art B.↗

NASA NTRS1993

Electrical conductivity in directionally solidified lead-9 and -20 wt pct copper alloys

Composites consisting of aligned copper dendrites in a lead matrix have been produced by directional solidification processing for potential application as grids in lead-acid batteries. To promote a uniform composite of aligned copper dendrites in a protective lead matrix, two alloy compositions, Pb-9 and -20 wt pct Cu, have been directionally solidified through a temperature gradient of 4.5 K/mm at constant growth velocities which ranged from 1 to 100 micron/s. With slow growth rates (below about 10 microns/s), the copper dendrites were generally columnar and continuous along the sample length; at higher velocities (above 60 microns/s), they assumed an intricate and equiaxed morphology. In accordance with copper content and growth rate, the electrical conductivity of the directionally solidified composites was found to be as much as a 2.5 times that of pure lead. The results are compared with that predicted by a model based on a geometrical dendrite.

Kim, Shinwoo↗

NASA NTRS1998

Can Kelvin-Helmholtz Instabilities of Jet-Like Structures and Plumes Cause Solar Wind Fluctuations at 1AU

During its south polar passage in 1994, Ulysses sampled the solar wind emerging from the south polar coronal hole. In analysing these data two types of density fluctuations lasting a few hours have been reported, one characterized by fluctuations in velocity gradients ("microstreams") and the other by magnetic plus thermal-pressure balance structures ("PBS"). Microstreams were both temporal and spatial in nature. At higher frequencies, MHD turbulence was observed and found to be less evolved than in the ecliptic, but essentially independent of heliographic latitude. It is argued here that microstreams, PBS, and MHD turbulence could all be the remnant of shears associated with plumes and other filamentary structures ("jets") which have been reported to exist in coronal holes. The shear between a jet and its ambient can become unstable to the MHD Kelvin-Helmholtz ("KH') instability at 5-10 solar radius and the propagating instability can cause fluctuations like those seen by Ulysses. This motivates us to simulate coronal jets using a 3D MHD ZEUS code. The first 2D results have just started to come and are promising. To study the KH instability the jet is perturbed at the boundary with a linear amplitude and fixed frequency. The jet seems to pass through various distinct phases, one of which Is apparently dominated by KH instabilities. These instabilities drive oblique shocks into the jet as the turbulent eddies contact the jet surface. It is known that KH instabilities and internal shock waves are partially suppressed by magnetic field tension. Hence, in simulating far along the jet the Instability is expected to produce Alfvenic fluctuations like those seen near 1 AU.

Parhi, Shyamsundar↗

DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2004

Forecasting Coronal Mass Ejections from Magnetograms

We report further results from our ongoing assessment of magnetogram-based measures of active-region nonpotentiality (magnetic shear and twist) and size as predictors of coronal mass ejections (CMEs). From a set of 36 vector magnetograms of bipolar active regions (Falconer, Moore, & Gary 2004, ApJ, submitted), we have found that (1) each of our 5 measures of active-region nonpotentiality has a 75-80% success rate in predicting whether an active region will produce a CME within 2 days after the magnetogram, (2) the correlation of each of these measures with CME production in this window is statistically significant (confidence level greater than 95%), (3) our measure of active-region size has a 65% success rate in predicting CMEs in this window, but (4) the correlation between active-region size and CME productivity is not statistically significant (confidence level approximately 80%). As part of the work under our pending 2003 LWS TR&T proposal, we will double our sample to approximately 70 active regions in order to demonstrate a statistically significant correlation between active-region size and CME productivity, and to determine whether this correlation is a byproduct of any Sun-produced correlation between magnetic twist and size of active regions. Since the 2002 LWS Science Workshop, we have found a measure of active-region nonpotentiality (the length of strong-gradient neutral line) that can be measured from a line-of-sight magnetogram, and we have generalized this measure and the corresponding measure from a vector magnetogram (the length of strong-shear neutral line) so that they can be applied to multiple-bipole complex active regions as well as to bipolar active regions. Preliminary results will be presented from application of these two generalized measures to our sample of bipolar active regions and to a new sample of multiple-bipole active regions.

Falconer, D. A.↗