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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Investigating Nanoscale Electron Transfer Processes at the Cell-Mineral Interface in Cobalt-Doped Ferrihydrite Using Geobacter sulfurreducens: A Multi-Technique Approach

Cobalt is an essential element for life and plays a crucial role in supporting the drive to clean energy, due to its importance in rechargeable batteries. Co is often associated with Fe in the environment, but the fate of Co in Fe-rich biogeochemically-active environments is poorly understood. To address this, synchrotron-based scanning X-ray microscopy (SXM) was used investigate the behaviour of cobalt at the nanoscale in Co-Fe(III)-oxyhydroxides undergoing microbial reduction. SXM can assess spatial changes in metal speciation and organic compounds helping to elucidate the electron transfer processes occurring at the cell-mineral interface and inform on the fate of cobalt in redox horizons. G. sulfurreducens was used to reduce synthetic Co-ferrihydrite as an analogue of natural cobalt-iron-oxides. Magnetite [Fe(II)/Fe(III) 3 O 4 ] production was confirmed by powder X-ray diffraction (XRD), SXM and X-ray magnetic circular dichroism (XMCD) data, where best fits of the latter suggested Co-bearing magnetite. Macro-scale XAS techniques suggested Co(III) reduction occurred and complementary SXM at the nanoscale, coupled with imaging, found localised biogenic Co(III) reduction at the cell-mineral interface via direct contact with outer membrane cytochromes. No discernible localised changes in Fe speciation were detected in the reordered cobalt-iron-oxides that were formed and at the end point of the experiment only 11% Co and 1.5% Fe had been solubilised. The solid phase retention, alongside the highly localised and preferential cobalt bioreduction observed at the nanoscale is consistent with retention of Co in redox zones. This work improves our fundamental molecular-scale understanding of the fate of Co in complex environmental systems and supports the development of biogenic Co-doped magnetite for industrial applications from drug delivery systems to magnetic recording media.

58 GEOSCIENCES↗
DOE OSTI2021

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea . We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py . Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R 2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Measurement Uncertainty of Highly Asymmetrically Curved Elliptical Mirrors Using Multi-Pitch Slope Stitching Technique

Soft X-ray off-axis elliptical mirrors bring new challenges for X-ray mirror metrology. These highly asymmetrically curved elliptical cylindrical mirrors with a total slope range >10 mrad are extremely challenging to measure. Their total slope range exceeds the measuring range of most angular sensors used for X-ray mirror inspection. To overcome this problem, it is possible to stitch partial slope data by measuring the mirror at different pitch angles (multi-pitch angles). By revisiting the theory of the multi-pitch Nano-accuracy Surface Profiler (NSP), we derive the sampling position error on the mirror surface as a function of the mirror height profile and the measurement error of the pitch rotation center. When measuring “extreme”, highly asymmetrically curved, elliptical mirrors, the calculation of the mirror height profile with iterative reconstruction outperforms the classical “flat assumption” (i.e., assuming that the mirror sag is negligible). As demonstrated by our simulations, a proper tolerance evaluation on the measurement of pitch rotation center is needed to assess the measurement accuracy (systematic error) for these strongly aspherical mirrors using the multi-pitch NSP technique. Taking a real design of an “extreme” elliptical mirror as a case study, we conduct a Monte Carlo simulation to mimic the measurement and characterization process to analyze the impact of several error sources. With the measurement uncertainty of the pitch rotation center, the multi-pitch NSP measurement can estimate the grazing angle θ and the chief ray location x o with their uncertainties, as well as the slope residuals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantitative Modeling of High-Energy Electron Scattering in Thick Samples Using Monte Carlo Techniques

Cryo-electron microscopy (cryo-EM) is a powerful tool for imaging biological samples but is typically limited by sample thickness, which is restricted to a few hundred nanometers depending on the electron energy. However, there is a growing need for imaging techniques capable of studying biological samples up to 10 µm in thickness while maintaining nanoscale resolution. This need motivates the use of mega-electron-volt scanning transmission electron microscopy (MeV-STEM), which leverages the high penetration power of MeV electrons to generate high-resolution images of thicker samples. In this study, we employ Monte Carlo simulations to model electron–sample interactions and explore the signal decay of imaging electrons through thick specimens. By incorporating material properties, interaction cross-sections for energy loss, and experimental parameters, we investigate the relationship between the incident and transmitted beam intensities. Key factors such as detector collection angle, convergence semi-angle, and the material properties of samples were analyzed. Our results demonstrate that the relationship between incident and transmitted beam intensities follows the Beer–Lambert law over thicknesses ranging from a few microns to several tens of microns, depending on material composition, electron energy, and collection angles. The linear depth of silicon dioxide reaches 3.9 µm at 3 MeV, about 6 times higher than that at 300 keV. Meanwhile, the linear depth of amorphous ice reaches 17.9 µm at 3 MeV, approximately 11.5 times higher than that at 300 keV. These findings are crucial for advancing the study of thick biological and semiconductor samples using MeV-STEM.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding Selectivity in CO2 Hydrogenation to Methanol for MoP Nanoparticle Catalysts Using In Situ Techniques

Molybdenum phosphide (MoP) catalyzes the hydrogenation of CO, CO2, and their mixtures to methanol, and it is investigated as a high-activity catalyst that overcomes deactivation issues (e.g., formate poisoning) faced by conventional transition metal catalysts. MoP as a new catalyst for hydrogenating CO2 to methanol is particularly appealing for the use of CO2 as chemical feedstock. Herein, we use a colloidal synthesis technique that connects the presence of MoP to the formation of methanol from CO2, regardless of the support being used. By conducting a systematic support study, we see that zirconia (ZrO2) has the striking ability to shift the selectivity towards methanol by increasing the rate of methanol conversion by two orders of magnitude compared to other supports, at a CO2 conversion of 1.4% and methanol selectivity of 55.4%. In situ X-ray Absorption Spectroscopy (XAS) and in situ X-ray Diffraction (XRD) indicate that under reaction conditions the catalyst is pure MoP in a partially crystalline phase. Results from Diffuse Reflectance Infrared Fourier Transform Spectroscopy coupled with Temperature Programmed Surface Reaction (DRIFTS-TPSR) point towards a highly reactive monodentate formate intermediate stabilized by the strong interaction of MoP and ZrO2. This study definitively shows that the presence of a MoP phase leads to methanol formation from CO2, regardless of support and that the formate intermediate on MoP governs methanol formation rate.

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

Analyzing the Validity of Brazilian Testing Using Digital Image Correlation and Numerical Simulation Techniques

Characterizing the mechanical behavior of rocks plays a crucial role to optimize the fracturing process in unconventional reservoirs. However, due to the intrinsic anisotropy and heterogeneity in unconventional resources, fracture process prediction remains the most significant challenge for sustainable and economic hydrocarbon production. During the deformation tracking under compression, deploying conventional methods (strain gauge, extensometer, etc.) is insufficient to measure the deformation since the physical attachment of the device is restricted to the size of the sample, monitoring limited point-wise deformation, producing difficulties in data retrieval, and a tendency to lose track in failure points, etc. Where conventional methods are limited, the application of digital image correlation (DIC) provides detailed and additional information of strain evolution and fracture patterns under loading. DIC is an image-based optical method that records an object with a camera and monitors the random contrast speckle pattern painted on the facing surface of the specimen. To overcome the existing limitations, this paper presents numerical modeling of Brazilian disc tests under quasi-static conditions to understand the full-field deformation behaviors and finally, it is validated by DIC. As the direct tensile test has limitations in sample preparation and test execution, the Brazilian testing principle is commonly used to evaluate indirectly the tensile strength of rocks. The two-dimensional numerical model was built to predict the stress distribution and full-field deformation on Brazilian disc under compression based on the assumptions of a homogenous, isotropic and linear elastic material. The uniaxial compression test was conducted using the DIC technique to determine the elastic properties of Spider Berea sandstone, which were used as inputs for the simulation model. The model was verified by the analytical solution and compared with the digital image correlation. The numerical simulation results showed that the solutions matched reasonably with the analytical solutions where the maximum deviation of stress distribution was obtained as 14.59%. The strain evolution (normal and shear strains) and displacements along the central horizontal and vertical planes were investigated in three distinguishable percentages of peak loads (20%, 40%, and 90%) to understand the deformation behaviors in rock. The simulation results demonstrated that the strain evolution contours consistently matched with DIC generated contours with a reasonable agreement. The changes in displacement along the central horizontal and vertical planes showed that numerical simulation and DIC generated experimental results were repeatable and matched closely. In terms of validation, Brazilian testing to measure the indirect tensile strength of rocks is still an issue of debate. The numerical model of fracture propagation supported by digital image correlation from this study can be used to explain the fracturing process in the homogeneous material and can be extended to non-homogeneous cases by incorporating heterogeneity, which is essential for rock mechanics field applications.

58 GEOSCIENCES↗
DOE OSTI2021

A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid

The world is transitioning from the conventional grid to the smart grid at a rapid pace. Innovation always comes with some flaws; such is the case with a smart grid. One of the major challenges in the smart grid is to protect it from potential cyberattacks. There are millions of sensors continuously sending and receiving data packets over the network, so managing such a gigantic network is the biggest challenge. Any cyberattack can damage the key elements, confidentiality, integrity, and availability of the smart grid. The overall smart grid network is comprised of customers accessing the network, communication network of the smart devices and sensors, and the people managing the network (decision makers); all three of these levels are vulnerable to cyberattacks. In this survey, we explore various threats and vulnerabilities that can affect the key elements of cybersecurity in the smart grid network and then present the security measures to avert those threats and vulnerabilities at three different levels. In addition to that, we suggest techniques to minimize the chances of cyberattack at all three levels.

Tufail, Shahid↗
DOE OSTI2024

The Evaluation of Machine Learning Techniques for Isotope Identification Contextualized by Training and Testing Spectral Similarity

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset and evaluated on twelve other datasets with varying standoff distances, shielding, and background configurations. A tailored statistical approach was introduced to quantify the similarity between the training and testing configurations, which was then related to the predictive performance. Wilcoxon signed-rank tests revealed that the OVR-wrapped XGB significantly outperformed the other algorithms, with confidence levels of 99.0% or above for the 133Ba, 60Co, 137Cs, and 152Eu sources. The findings from this work are significant as they outline techniques to promote the development of robust ML-based approaches for isotope identification.

domain adaptation↗
DOE OSTI2024

Effect of Processing Parameters on Recrystallization During Hot Isostatic Pressing of Stellite-6 Fabricated Using Laser Powder Bed Fusion Technique

Crack-free Stellite-6 alloy was fabricated using the laser powder bed fusion technique equipped with a heating module as the first attempt. Single tracks were printed with a build plate heated to 400 °C to identify the processing window. Based on the melt pool dimensions, two combinations (sample A: 300 W/750 mm/s and sample B: 275 W/1000 mm/s) were identified to print the cubes. The as-printed microstructure comprised FCC-Co dendrites with M7C3 in the interdendritic region. W-rich M6C particles were found in the overlapping regions between the melt pools, matching the Scheil simulations. However, gas pores were observed due to the higher nitrogen and oxygen content of the feedstock requiring hot isostatic pressing (HIP) at 1250 °C and 150 MPa for 2 h. Sample A was partially recrystallized with slightly coarsened M7C3, while sample B underwent complete recrystallization followed by grain growth along with higher coarsening of the M7C3 after HIP. The varying recrystallization behavior can be attributed to the difference in residual stresses and grain aspect ratio in the as-built condition dictated by laser power and scanning speed. The microhardness after HIP was slightly higher than its wrought counterpart, indicating no severe impact of post-processing on the properties of Stellite-6 alloy.

Sridar, Soumya (ORCID:0000000306775408)↗
DOE OSTI2020

Preparation and Characterization of Inclusion Complexes of β-Cyclodextrin and Phenolics from Wheat Bran by Combination of Experimental and Computational Techniques

Bitterness often associated with whole wheat products may be related to phenolics in the bran. Cyclodextrins (CDs) are known to form inclusion complexes. The objective was to form inclusion complexes between β-CD and wheat phenolics. Pure phenolic acids (trans-ferulic acid (FA), caffeic acid (CA), and p-coumaric acid (CO)) and phenolic acids from wheat bran were used to investigate complex formation potential. Complexes were characterized by spectroscopy techniques, and a computational and molecular modeling study was carried out. The relative amount of complex formation between β-CD and wheat bran extract was CA > CO > FA. The phenolic compounds formed inclusion complexes with β-CDs by non-covalent bonds. The quantum-mechanical calculations supported the experimental results. The most stable complex was CO/β-CD complex. The ΔH value for CO/β-CD complex was -11.72 kcal/mol and was about 3 kcal/mol more stable than the other complexes. The QSPR model showed good correlation between binding energy and 1H NMR shift for the H5 signal. This research shows that phenolics and β-CD inclusion complexes could be utilized to improve the perception of whole meal food products since inclusion complexes have the potential to mask the bitter flavor and enhance the stability of the phenolics in wheat bran.

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

Techniques for TPC Calibration: Application to Liquid Ar-TPCs

Large liquid argon TPCs are playing an increasingly important role in neutrino physics, and their calibration will be an essential component of their capability to reach the required performance and precision. Natural sources are extensively used but present limitations, since natural radioactivity from 39 Ar is of low energy, and the rate of cosmic ray muons is low when the detectors are placed deep underground. Argon gas TPCs have been calibrated with ionizing laser beams for several decades, and more recently the technique has been further developed for use in liquid TPCs. Other recent ideas include the use of external neutron generators creating pulses that propagate into the detector. This paper reviews the development of the laser and neutron methods for the calibration of argon TPCs and describes their planned implementation in the upcoming DUNE experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Calibrating the Galactic Cepheid Period–Luminosity Relation from the Maximum-likelihood Technique

We present a realization of the maximum-likelihood technique, which is one of the latest modifications of the Baade–Becker–Wesselink method. Our approach is based on nonlinear calibrations of the effective temperature and bolometric correction, which take into account metallicity and surface gravity. It allows one to estimate the key Cepheid parameters, the distance modulus, and the interstellar reddening, combining photometric and spectroscopic data (including the effective temperature data). This method is applied to a sample of 44 Galactic Cepheids for which multiphase temperature measurements are available. The additional data correction is performed to subtract the impact of the component in binary/multiple systems. We also study the effect of shock waves, whose presence in the stellar atmosphere distorts the observational data and leads to systematic errors in the obtained parameters. We determine the optimal restriction on the input data to eliminate this effect. This restriction provides accurate period–radius and period–luminosity relations that are consistent with the results in previous studies. We found the following relations: log R = (0.68 ± 0.03) · log P + (1.14 ± 0.03), M {sub v} = − (2.67 ± 0.16) · (log P − 1) − (4.14 ± 0.05).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Ocean-Powered Oyster Tumbling: A Review of Techniques and Opportunities for Emission Reductions

Oysters perform critical roles in shoreline ecosystems by improving water quality, providing habitat for species, and preventing erosion. These ecosystem functions are present even when oysters are farmed. Because of this, and the lack of need for nutrient inputs, oyster farming is often viewed as environmentally friendly. However, fossil fuels play a large part in oyster farming practices. Fossil fuels are used to power boats, tools, and farming equipment. Oyster tumbling machines, which are used to control biofouling and produce a desirable shape and size, use a significant amount of energy and are often powered by diesel generators. As the oyster farming industry grows and practices such as integrated multi-trophic aquaculture expand, decarbonization of the industry becomes more important. One solution may be “ocean-powered” tumbling, whereby oyster grow-out gear is designed to use a range of ocean movements to tumble oysters gradually as they grow. This solution eliminates the need for fossil fuel-powered tumblers and tends to be less labor intensive. A wide range of ocean-powered gear is used by farms across the United States. New approaches and designs are being explored, making ocean-powered oyster tumbling accessible in different environments. Water movements at oyster farms are primarily driven by tidal exchange, currents, wind waves, or a combination. This paper compares methods of ocean-powered tumbling, explores the transition from standard fossil fuel-powered tumbling techniques to ocean-powered tumbling, and estimates the emission reductions of decarbonizing oyster tumbling practices.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

The Dynamic Assimilation Technique measures photosynthetic CO2 response curves with similar fidelity as steady-state approaches in half the time

The net CO2 assimilation (A) response to intercellular CO2 concentration (Ci) is a fundamental measurement in photosynthesis and plant physiology research. The conventional A/Ci protocols rely on steady-state measurements and take 15-40 minute per measurement, limiting data resolution or biological replication. Additionally, there are several CO2 protocols employed across the literature, without clear consensus as to the optimal protocol or systematic biases in their estimations. We compared the non-steady state Dynamic Assimilation Technique (DAT) protocol and the three most used CO2 protocols in steady-state measurements, and tested whether different CO2 protocols lead to systematic differences in estimations of the biochemical limitations to photosynthesis.

A/Ci response↗
DOE OSTI2025

Development of a forced advection sampling technique (FAST) for quantification of methane emissions from orphaned wells

Abstract. Orphaned wells, meaning unplugged and non-producing wells lacking responsible owners, pose a significant and undersampled environmental challenge due to their vast number and unknown associated emissions. We propose, develop and test an alternative method for estimating emissions from orphaned wells using a forced advection sampling technique (FAST) that can overcome many of the limitations in current methods (cost, accuracy, safety). In contrast to existing ambient Gaussian plume methods, our approach uses a fan-generated flow to force advection between the emission source and a point methane (CH4) sensor. The fan flow field is characterized using a colocated sonic anemometer to measure the 3D wind profile generated by the fan. Using time-series measurements of CH4 concentration and wind, a simple estimate of the CH4 emission rate of the source can be inferred. The method was calibrated using outdoor controlled-release experiments and then tested on four orphaned wells in Lufkin, TX, and Osage County, OK. Our results suggest that the FAST method can provide a low-cost, portable, fast and safe alternative to existing methods with reasonable estimates of orphaned well emissions over a range of leak rates below 40 g h−1 and within certain geometric and atmospheric constraints.

Dubey, Mohit L↗
DOE OSTI2026

Data-driven Techniques for $v^{e}$ Signal and Background Predictions in NOνA

NOν \nu A is a long baseline neutrino oscillation experiment, using two functionally identical detectors to measure νe \nu_e appearance and νμ \nu_\mu disappearance at the Far Detector (FD) with the NuMI Beam at Fermilab. The Near Detector (ND) measures the beam before oscillations, which will allow us to make a measurement of the signal flux before it has oscillated and the background components which can mimic νe \nu_e at our FD. We use the ND in three different ways to predict the amount of signal and background expected at the FD to partially cancel systematic uncertainties. The background prediction has three components: Charged Current (CC) νμ \nu_\mu , νe \nu_e and Neutral Current (NC) events. We need to determine the fraction of the selected ND sample in each of these components because some oscillate significantly (CC νμ \nu_\mu ) and some do not oscillate at all (NC). This poster will present details of these three data-driven techniques for predicting the FD spectrum.

Yu, Shiqi [Argonne; IIT, Chicago]↗