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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

PUFFIn – A user friendly fast interface for calculating and visualizing the dose distribution in materials

This article describes a new graphical user interface that uses the PENELOPE Monte Carlo code to calculate dose distributions in materials. It is named PUFFIn, for the Penelope User-Friendly Fast Interface and was developed as an educational and scoping analysis tool for non-experts in radiation modeling. PUFFIn enables the user to visualize and compare the dose distributions in objects (e.g., sterilized healthcare products) irradiated with cobalt-60 gamma-rays, electron beam (E-beam) or X-rays. From such comparisons, the user can determine the most efficient product and/or packaging designs for any given radiation field – whether for conceptual or existing products and packaging. PUFFIn is distributed in a complete, self-contained package of software, including the PENELOPE radiation transport code, a standard graphics package, and a set of simple exercises. The package is available to any user at no cost and requires a minimal amount of training compared to other similar software. PUFFIn's capabilities are described, as well as validation measurements performed at Texas A&M University E-beam facility and the Aerial E-beam Facility in France.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Modeling charging infrastructure impact on the electric vehicle market in China

The plug-in electric vehicle (PEV) is deemed as a critical technological revolution, and the governments are imposing various vehicle policies to promote its development. Meanwhile, the market success of PEVs depends on many aspects. The study reported herein integrates one’s use of charging infrastructure at home, public place and workplace into the market dynamics analysis tool, New Energy and Oil Consumption Credits (NEOCC) model, to systematically assess the charging infrastructure (home parking ratio, public charging opportunity, and charging costs) impact on PEV ownership costs and analyze how the PEV market shares may be affected by the attributes of the charging infrastructure. Compared to the charging infrastructure, the impact of battery costs is incontrovertibly decisive on PEV market shares, the charging infrastructure is still non-negligible in the PEV market dynamics. The simulation results find that the public charging infrastructure has more effectiveness on promoting the PEV sales in the PEV emerging market than it does in the PEV mature market. However, the improvement of charging infrastructure does not necessarily lead to a larger PEV market if the charging infrastructure incentives do not coordinate well with other PEV policies. Besides, the increase of public charging opportunities has limited motivations on the growth of public PEV fleets, which are highly correlated to the number of public fast charging stations or outlets. It also finds that more home parking spaces can stimulate more sales of personal plug-in hybrid electric vehicles instead of personal battery electric vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Cross-reactive immunogenicity of group A streptococcal vaccines designed using a recurrent neural network to identify conserved M protein linear epitopes

The M protein of group A streptococci (Strep A) is a major virulence determinant and protective antigen. The N-terminal sequence of the protein defines the more than 200 M types of Strep A and also contains epitopes that elicit opsonic antibodies, some of which cross-react with heterologous M types. Current efforts to develop broadly protective M protein-based vaccines are directed at identifying potential cross-protective epitopes located in the N-terminal regions of cluster-related M proteins for use as vaccine antigens. In this study, we have used a comprehensive approach using the recurrent neural network ABCpred and IEDB epitope conservancy analysis tools to predict 16 residue linear B-cell epitopes from 117 clinically relevant M types of Strep A (~88% of global Strep A infections). Furthermore, to examine the immunogenicity of these epitope-based vaccines, nine peptides that together shared ≥60% sequence identity with 37 heterologous M proteins were incorporated into two recombinant hybrid protein vaccines, in which the epitopes were repeated 2 or 3 times, respectively. The combined immune responses of immunized rabbits showed that the vaccines elicited significant levels of antibodies against all nine vaccine epitopes present in homologous N-terminal 1–50 amino acid synthetic M peptides, as well as cross-reactive antibodies against 16 of 37 heterologous M peptides predicted to contain similar epitopes. The epitope-specificity of the cross-reactive antibodies was confirmed by ELISA inhibition assays and functional opsonic activity was assayed in HL-60-based bactericidal assays. The results provide important information for the future design of broadly protective M protein-based Strep A vaccines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems

Abstract Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.

Diao, Xiaoxu (ORCID:0000000346726352)↗
DOE OSTI2024

Two-Dimensional Projected-Momentum Covariance Mapping for Coulomb Explosion Imaging

We introduce projected-momentum covariance mapping, an extension of recoil-frame covariance mapping for 2D ion imaging studies. By considering the two-dimensional projection of the ion momenta as recorded by the detector, one opens the door to a complex suite of analysis tools adapted from three-dimensional momentum imaging studies. This includes the use of different frames of reference to unravel the dynamics of fragmentation and the application of fragment momentum constraints to isolate specific fragmentation channels. The technique is demonstrated on data from a two-dimensional ion imaging study of the Coulomb explosion of the cis and trans isomers of 1,2-dichloroethene, following strong-field ionization by an intense near-infrared femtosecond laser pulse. Classical simulations are used to guide the interpretation of projected-momentum covariance maps. The results offer a detailed insight into the distinct Coulomb explosion dynamics for this pair of isomers and lay the groundwork for future time-resolved studies of photoisomerization dynamics in this molecular system.

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

Influence of Long-Range Forces on the Transition States and Dynamics of NaCl Ion-Pair Dissociation in Water

We study NaCl ion-pair dissociation in a dilute aqueous solution using computer simulations both for the full system with long-range Coulomb interactions and for a well-chosen reference system with short-range intermolecular interactions. Analyzing results using concepts from Local Molecular Field (LMF) theory and the recently proposed AI-based analysis tool “State predictive information bottleneck” (SPIB), we show that the system with short-range interactions can accurately reproduce the transition rate for the dissociation process, the dynamics for moving between the underlying metastable states, and the transition state ensemble. Contributions from long-range interactions can be largely neglected for these processes because long-range forces from the direct interionic Coulomb interactions are almost completely canceled (>90%) by those from solvent interactions over the length scale where the transition takes place. Thus, for this important monovalent ion-pair system, short-range forces alone are able to capture detailed consequences of the collective solvent motion, allowing the use of physically suggestive and computationally efficient short-range models for the dissociation event. Here, we believe that the framework here should be applicable to disentangling mechanisms for more complex processes such as multivalent ion disassociation, where previous work has suggested that long-range contributions may be more important.

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

Surface-Functionalized Cellulose Nanocrystals as Nanofillers for Crosslinking Processes: Implications for Thermosetting Resins

Understanding the response of fillers in the epoxy resin crosslinking process and characterizing polymer–filler dynamics are the key features that guide the engineering of new thermosetting resin composites. Here, in this work, X-ray photon correlation spectroscopy (XPCS) is used as a thermal analysis tool to track the microscopic changes occurring during the cure of a functionalized cellulose nanocrystal (mCNC)–epoxy composite. In contrast, traditional methods such as differential scanning calorimetry (DSC) and curing rheology are used to understand the kinetics and properties on macroscopic length scales. Of interest is the influence of the mCNC on the curing kinetics and properties of the thermosetting resin. Two levels of modification (increasing hydrophobicity) were chosen to observe the effect of functionalization. Before cure, the highly functionalized CNC (mCNC3) shows a 44% increase in complex viscosity (η*), while the less functionalized CNC (mCNC2) shows a η* value similar to that of the neat resin. As the cure cycle progresses, results from DSC, rheology, and XPCS further show the enhancement in dispersion for mCNC3. The results show a clear difference in the maximum drift velocity, maximum heat flow, and complex viscosity during the ramp to the isothermal cure temperature (T cure ). Such results suggest that mCNC3 contains a well-dispersed network of particles due to the higher level of functionalization. During T cure , a transition in elastic modulus (G') occurs only for the highly functionalized CNC particle system. We believe that heat-induced aggregation occurs, and the crosslinked resin ultimately dominates the macroscopic properties of the final cured system for all samples. The results from the three techniques are in good agreement and showcase XPCS as a beneficial experimental tool for characterizing the microscopic dynamics of particulate-filled thermosetting resins. Hence, we envision this to be a fundamental curing study for the design of thermosetting resin composites.

36 MATERIALS SCIENCE↗
DOE OSTI2020

ACS Sustainable Chemistry & Engineering Virtual Special Issue on Recent Advances in Biomass Characterization and Modeling

Recognizing the importance of biomass characterization and modeling, a virtual special issue (VSI) entitled Recent Advances in Biomass Characterization and Modeling was organized to showcase the recent contributions to this emerging field. This VSI features contributions from leading scientists in the biorefinery field, intending to provide the readers with the latest advances in analytical methodology and modeling to characterize biomass feedstocks and biomass-derived products. Further, the VSI contains perspectives and research articles. Advanced analytical methods that measure the mechanical, chemical, physical, biological, and other properties of biomass and biomass pyrolysis products are described along with their application’s limitations in two perspectives. Biomass is a multicomponent/multiscale, complex, nonconducting, and highly heterogeneous material; thus, it could be problematic for many characterization techniques. Traditional biomass analysis tools that are slow, laborious, and generally require harsh reagents have been replaced or supplemented by simpler and more rapid chromatographic and spectroscopic approaches such as GC-MS, HPLC, FTIR, Raman, and NMR spectroscopies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Unveiling Feedstock Variability: Insights into Corn Stover Conversion - Part I: Physicochemical Properties and Self-Degradation

Transforming agricultural waste into biofuels and bioproducts is crucial to advancing a low-carbon bioeconomy. However, the inherent variability in the composition and quality introduces uncertainties in the conversion efficiency and poses challenges in process development. Through integrating a high-throughput conversion system, material characterization techniques, and advanced data analysis tools, this study investigates the variability of corn stover and its subsequent impacts on carbohydrate conversion. The findings reveal that indoor storage substantially reduces the moisture and ash content and soil contamination, while other properties remain largely unchanged. Self-degradation due to microbial activity during storage decreases the carbohydrate content of corn stover but enhances glucose and xylose yields. A negative correlation is observed between sugar yields and lignin content across samples with varying ash and moisture content. The inhibitory effect of lignin diminishes in self-degraded samples likely due to the disrupted cell wall structure. Although self-degradation slightly increases cellulose crystallinity, no strong correlation was observed between the crystallinity and sugar yield. Hot water pretreatment under mild conditions effectively mitigates inherent variability, consistently improving the sugar yield from corn stover by up to 50%. By elucidating the feedstock variability and its impact on convertibility, these findings offer valuable insights into appropriate feedstock handling and management, highlighting potential strategies to address variability challenges.

09 BIOMASS FUELS↗
DOE OSTI2021

Carbon‐Neutral Pathways for the United States

Abstract The Intergovernmental Panel on Climate Change (IPCC) Special Report on Global Warming of 1.5°C points to the need for carbon neutrality by mid‐century. Achieving this in the United States in only 30 years will be challenging, and practical pathways detailing the technologies, infrastructure, costs, and tradeoffs involved are needed. Modeling the entire U.S. energy and industrial system with new analysis tools that capture synergies not represented in sector‐specific or integrated assessment models, we created multiple pathways to net zero and net negative CO 2 emissions by 2050. They met all forecast U.S. energy needs at a net cost of 0.2–1.2% of GDP in 2050, using only commercial or near‐commercial technologies, and requiring no early retirement of existing infrastructure. Pathways with constraints on consumer behavior, land use, biomass use, and technology choices (e.g., no nuclear) met the target but at higher cost. All pathways employed four basic strategies: energy efficiency, decarbonized electricity, electrification, and carbon capture. Least‐cost pathways were based on >80% wind and solar electricity plus thermal generation for reliability. A 100% renewable primary energy system was feasible but had higher cost and land use. We found multiple feasible options for supplying low‐carbon fuels for non‐electrifiable end uses in industry, freight, and aviation, which were not required in bulk until after 2035. In the next decade, the actions required in all pathways were similar: expand renewable capacity 3.5 fold, retire coal, maintain existing gas generating capacity, and increase electric vehicle and heat pump sales to >50% of market share. This study provides a playbook for carbon neutrality policy with concrete near‐term priorities.

Williams, James H.↗
DOE OSTI2022

A Bayesian Deep Learning Approach to Near-Term Climate Prediction

Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction. The example problem setting we consider consists of predicting natural variability of the North Atlantic sea surface temperature on the interannual timescale in the pre-industrial control simulation of the Community Earth System Model. While previous works have considered the use of recurrent networks such as convolutional LSTMs and reservoir computing networks in this and other similar problem settings, we currently focus on the use of feedforward convolutional networks. In particular, we find that a feedforward convolutional network with a Densenet architecture is able to outperform a convolutional LSTM in terms of predictive skill. Next, we go on to consider a probabilistic formulation of the same network based on Stein variational gradient descent and find that in addition to providing useful measures of predictive uncertainty, the probabilistic (Bayesian) version improves on its deterministic counterpart in terms of predictive skill. Finally, we characterize the reliability of the ensemble of machine learning models obtained in the probabilistic setting by using analysis tools developed in the context of ensemble numerical weather prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Real-time 3D analysis during electron tomography using tomviz

The demand for high-throughput electron tomography is rapidly increasing in biological and material sciences. However, this 3D imaging technique is computationally bottlenecked by alignment and reconstruction which runs from hours to days. We demonstrate real-time tomography with dynamic 3D tomographic visualization to enable rapid interpretation of specimen structure immediately as data is collected on an electron microscope. Using geometrically complex chiral nanoparticles, we show volumetric interpretation can begin in less than 10 minutes and a high-quality tomogram is available within 30 minutes. Real-time tomography is integrated into tomviz, an open-source and cross-platform 3D data analysis tool that contains intuitive graphical user interfaces (GUI), to enable any scientist to characterize biological and material structure in 3D.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Capturing dynamical correlations using implicit neural representations

Abstract Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S ( Q , ω ), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages ‘neural implicit representations’ that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La 2 NiO 4 , showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

Abstract The PRISMS-Fatigue open-source framework for simulation-based analysis of microstructural influences on fatigue resistance for polycrystalline metals and alloys is presented here. The framework uses the crystal plasticity finite element method as its microstructure analysis tool and provides a highly efficient, scalable, flexible, and easy-to-use ICME community platform. The PRISMS-Fatigue framework is linked to different open-source software to instantiate microstructures, compute the material response, and assess fatigue indicator parameters. The performance of PRISMS-Fatigue is benchmarked against a similar framework implemented using ABAQUS. Results indicate that the multilevel parallelism scheme of PRISMS-Fatigue is more efficient and scalable than ABAQUS for large-scale fatigue simulations. The performance and flexibility of this framework is demonstrated with various examples that assess the driving force for fatigue crack formation of microstructures with different crystallographic textures, grain morphologies, and grain numbers, and under different multiaxial strain states, strain magnitudes, and boundary conditions.

Chemistry↗
DOE OSTI2024

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Fast mass spectrometry search and clustering of untargeted metabolomics data

The throughput of mass spectrometers and the amount of publicly available metabolomics data are growing rapidly, but analysis tools such as molecular networking and Mass Spectrometry Search Tool do not scale to searching and clustering billions of mass spectral data in metabolomics repositories. To address this limitation, we designed MASST+ and Networking+, which can process datasets that are up to three orders of magnitude larger than those processed by state-of-the-art tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Large field-of-view event-mode camera for high-precision epithermal neutron resonance imaging

A large-area event-mode camera system coupled with a 6 LiF-ZnS:Ag scintillator is applied for neutron resonance imaging (NRI) on the energy-resolved neutron imaging (ERNI) flight path, also known as Flight Path 5 (FP5), at the Los Alamos Neutron Science Center (LANSCE). This novel neutron imaging system, featuring a 120 x 120 mm 2 field of view, efficiently captures resonance information across the entire image in a single acquisition, significantly reducing beam time requirements compared to conventional energy-resolved neutron imaging systems. High-quality neutron radiographs with enhanced spatial resolution are achieved through the reconstruction of neutron events based on observations of individual photons emitted from the scintillator. The system demonstrates reduced background through neutron/gamma discrimination capabilities while maintaining sharpness across a large fields of view. In the measurements presented here, a spatial resolution of approximately 340 μm was achieved using center-of-gravity photon cluster centroiding. We demonstrate the system’s capability for quantitatively determining isotopic distributions in various thin samples, as well as automatically reconstructing complex scenes with overlapping resonances from diverse samples. These results are obtained using standard data analysis tools, despite the relatively slow 6 LiF-ZnS:Ag scintillator, which may not be optimal for absorption resonance detection. The capabilities demonstrated here offer a valuable, versatile, and cost-effective solution for high spatial and temporal resolution, large field-of-view energy-resolved neutron imaging, with potential applications across various scientific and industrial domains.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The efficacy of Lewis affinity scale metrics to represent solvent interactions with reagent salts in all-inorganic metal halide perovskite solutions

Solvents employed in the solution processing of metal halide perovskites are known to play a key role in defining the morphology and properties of the resulting thin film, and thus the performance of perovskite solar cell devices. Accurate metrics are needed that are capable of differentiating among candidates, finding solvents that adequately solubilize the various precursor species in solution and facilitate the nucleation and growth of these materials. Existing metrics such as the unsaturated Mayer bond order (UMBO) and the Gutmann donor number (DN) have been tested for lead iodide perovskite systems; but there has yet to be a comprehensive study on their transferability to lead-free perovskite solutions. Here, we use ab initio methods (density functional theory) and regression analysis tools to study the usefulness of DN and BF 3 affinity scales in this regard. We compared the relative effectiveness of these scales to describe interactions between solvents and BXn perovskite salts of lead (Pb 2+ ), tin (Sn 2+ and Sn 4+ ), germanium (Ge 2+ ), bismuth (Bi 3+ ), and antimony (Sb 3+ and Sb 5+ ). The DN proved to be a better representation than the BF 3 of such interactions, reflecting the closer similarity of these species to the “parent” SbCl 5 Lewis acid than to BF 3 . In addition, we have uncovered the usefulness of the lithium cation affinity metric (LCA) to describe the strength of interactions between solvents and A-site cations (e.g. Na + , K + , Rb + and Cs + ) in all-inorganic metal halide perovskite solutions. We find that the coordination strengths of solvents towards species in all-inorganic metal halide perovskite solutions are best described by two different metrics with distinct modes of action: DN differentiates among BX n salt complexes, and LCA among A-site cation species. This revelation can help guide the choice of solvent to optimize processing conditions. It also emphasizes the importance of selecting solvents whose DN and LCA optimize coordination to key Lewis acid species in all-inorganic perovskite solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗