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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Design and proof of concept for targeted phage-based COVID-19 vaccination strategies with a streamlined cold-free supply chain

Significance The COVID-19 pandemic has had an unprecedented impact. Although several vaccines have received emergency use authorization, demand has created enormous logistical challenges—including supply, access, and distribution—that justify research for alternative strategies. Phage are viruses that only infect bacteria and can be safely administered to humans. Here, as a proof-of-concept study, we demonstrate that aerosol vaccination with lung-targeted phage particles displaying short SARS-CoV-2 S protein epitopes and subcutaneous vaccination with targeted AAVP particles carrying the entire S protein gene both elicit systemic and specific immune responses in immunocompetent mice. Given their unique attributes, including sturdiness, simple-to-engineer platform, cost-effectiveness for rapid large-scale production, and stability at room temperature, these phage-based approaches may become attractive tools for COVID-19 vaccine development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Achieving American Leadership in the Electric Grid Supply Chain Factsheet

The U.S. electric grid consists of more than 22,000 generators; 55,000 substations; 642,000 miles of high-voltage lines; and 6.3 million miles of distribution lines that serve 153 million customers. To date, much of the smart grid transformation has focused on applying advanced digital information and communication technologies to the power grid to improve the system’s efficiency, flexibility, and security. To achieve the full value of grid modernization, we also need advances in grid hardware. Many critical components supporting the power grid have limited to no domestic manufacturing capacity and face complex challenges in supporting a rapid expansion of the grid to meet multiple objectives, including decarbonization goals.

Source record↗
DOE OSTI2022

Specific Ion and Electric Field Controlled Diverse Ion Distribution and Electroosmotic Transport in a Polyelectrolyte Brush Grafted Nanochannel

Controlling ion distribution inside a charged nanochannel is central to using such channels in diverse applications. Here, we show the possibility of using a charged polyelectrolyte (PE) brush grafted nanochannel for triggering diverse nanoscopic ion distribution and nanofluidic electroosmotic transport by controlling the valence and size of the counterions (that screen the charges of the PE brushes) and the strength of an externally applied axial electric field. We atomistically simulate separate cases of fully charged Polyacrylic acid (PAA) brush functionalized nanochannels with Na + , Cs + , Ca 2+ , Ba 2+ , and Y 3+ counterions screening the PE charges. Four key findings emerge from our simulations. First, we find that the counterions with a greater valence and a smaller size prefer to remain localized inside the brush layer. Second, for the case where there is an added chloride salt with the same cation (as the screening counterions), there are more coions (Cl - ions) in the brush-free bulk than counterions (for counterions Na + , Ca 2+ , Ba 2+ , Y 3+ ): this is a manifestation of the overscreening (OS) of the PE brush layer. Contrastingly, the number of Cs + ions remain higher than the Cl - ions inside the brush-free bulk, ensuring that there is no OS effect for this case. Third, large applied electric field enables a few Na + , Cs + , and Ba 2+ counterions to leave the brush layer and to go to the bulk: this makes the OS of the PE brush layer disappear for the cases of PE brushes being screened by the Na+ and Ba 2+ ions. On the other hand, no such electric-field-mediated disappearance of OS is observed for the cases of Ca 2+ and Y 3+ screening counterions; we attribute this to the firm attachment of these counterions to the negatively charged monomers. Free energy associated to a counterion binding to a PE chain corroborates this diversity in the counterion-specific response to the applied electric field. Lastly, we demonstrate that such diverse ion distributions, along with specific electric-field-strength-dependent ion properties, lead to (1) EOS transport in nanochannels grafted with PAA brushes screened with Cs + ions to be always counterion dominated, (2) EOS transport in nanochannels grafted with PAA brushes screened with Ca 2+ and Y 3+ ions to be always coion dominated, and (3) EOS transport in nanochannels grafted with PAA brushes screened with Na + and Ba 2+ ions to be coion dominated for smaller electric fields and counterion dominated for larger electric fields.

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

Analyzing count data with measurement error

In this article, we analyze observed count data such as the number of defects in a steel product where the observed counts are the true counts measured with errors. We account for the measurement error by using a measurement error model based on a latent lognormal (LLN) distribution. We consider making inference about a single population (e.g., from samples of a production lot) and a regression model (e.g., from runs of a designed experiment), where the measurement system properties are known, that is, the parameters of the LLN distribution are known. Then, we consider simultaneous inference for the single population and regression model as well as the measurement system. We demonstrate the proposed methodology with both simulated and real observed counts.

42 ENGINEERING↗
DOE OSTI2026

Informing Transmission Supply Chain Needs from National Transmission Studies

Recent national studies indicate significant transmission expansion can provide the lowest-cost option to maintain grid reliability while meeting growing demand. However, constraints in domestic supply chains may limit grid expansion across the U.S., with higher costs and longer delays for required transmission equipment. Despite growing evidence of supply chain constraints for transmission components, transmission planning studies often assume transmission equipment is readily available for deployment or analyze future demand using historical trade and manufacturing data that may not capture evolving grid needs. This report aims to address this gap by demonstrating methods to quantify future demand for critical transmission components and input materials from national-scale planning models. These components include power transformers, generator step-up transformers, converter transformers, conductors, circuit breakers, and transmission towers and the materials include aluminum, steel, grain-oriented electrical steel (GOES), and copper. The analytical approach is applied to two nodal transmission expansion scenarios from the National Transmission Planning Study (NTP) to illustrate the methods. These scenarios represent different transmission expansion strategies for the contiguous U.S. to the year 2035: the Alternating Current (AC) scenario includes AC transmission expansion within each interconnection and the Multiterminal (MT) scenario includes interregional transmission expansion across the country using both AC and multiterminal HVDC options between neighboring zones. We also explore potential heuristics to derive transmission component demand from zonal capacity expansion models (CEMs) with coarse representation of the transmission grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Informing Transmission Supply Chain Needs from National Transmission Studies

Recent national studies indicate significant transmission expansion can provide the lowest-cost option to maintain grid reliability while meeting growing demand. However, constraints in domestic supply chains may limit grid expansion across the U.S., with higher costs and longer delays for required transmission equipment. Despite growing evidence of supply chain constraints for transmission components, transmission planning studies often assume transmission equipment is readily available for deployment or analyze future demand using historical trade and manufacturing data that may not capture evolving grid needs. This report aims to address this gap by demonstrating methods to quantify future demand for critical transmission components and input materials from national-scale planning models. These components include power transformers, generator step-up transformers, converter transformers, conductors, circuit breakers, and transmission towers and the materials include aluminum, steel, grain-oriented electrical steel (GOES), and copper. The analytical approach is applied to two nodal transmission expansion scenarios from the National Transmission Planning Study (NTP) to illustrate the methods. These scenarios represent different transmission expansion strategies for the contiguous U.S. to the year 2035: the Alternating Current (AC) scenario includes AC transmission expansion within each interconnection and the Multiterminal (MT) scenario includes interregional transmission expansion across the country using both AC and multiterminal HVDC options between neighboring zones. We also explore potential heuristics to derive transmission component demand from zonal capacity expansion models (CEMs) with coarse representation of the transmission grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN

Physics-informed neural network (PINN) has been successfully applied in solving a variety of nonlinear non-convex forward and inverse problems. However, the training is challenging because of the non-convex loss functions and the multiple optima in the Bayesian inverse problem. In this work, we propose a multi-variance replica exchange stochastic gradient Langevin dynamics method to tackle the challenge of the multiple local optima in the optimization and the challenge of the multiple modal posterior distribution in the inverse problem. Replica exchange methods are capable of escaping from the local traps and accelerating the convergence; two chains with different temperatures are designed where the low temperature chain aims for the local convergence, and the target of the high temperature chain is to travel globally and explore the whole loss function entropy landscape. However, it may not be efficient to solve mathematical inversion problems by using the vanilla replica method directly since the method doubles the computational cost in evaluating the forward solvers (likelihood functions) in the two chains. To address this issue, we propose to make different assumptions on the energy function estimation and this facilities one to use solvers of different fidelities in the likelihood function evaluation. More precisely, one can use a solver with low fidelity in the high temperature chain while using a solver with high fidelity in the low temperature chain. Our proposed method significantly lowers the computational cost in the high temperature chain, meanwhile preserving the accuracy and converging very fast. Here we give an unbiased estimate of the swapping rate and give an estimation of the discretization error of the scheme. To verify our idea, we design and solve four inverse problems which have multiple modes. The proposed method is also employed to train the Bayesian PINN to solve the forward and inverse problems; faster and more accurate convergence has been observed when compared to the stochastic gradient Langevin dynamics (SGLD) method and vanilla replica exchange methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Oakland University Cybersecurity Center (Final Scientific/Technical Report)

This report summarizes the outcomes of Award DE-CR0000023, “Oakland University Cybersecurity Center,” a 31-month project funded by the U.S. Department of Energy Office of Cybersecurity, Energy Security, and Emergency Response (CESER). The project addressed cybersecurity risks facing small and medium-sized manufacturers (SMMs) transitioning to Industry 4.0. The project integrated customer discovery, applied research, and cybersecurity training development. A total of 51 cybersecurity assessments identified significant gaps in baseline practices, incident response, and workforce capability. Research efforts produced a scalable mitigation framework tailored to SMM environments, and workforce analysis identified persistent talent gaps. Eight cybersecurity training modules were developed and deployed via Oakland University’s Professional and Continuing Education (PACE) platform. All objectives were completed, with 98.93% federal budget utilization and cost share exceeding requirements. The project establishes a scalable model for strengthening cybersecurity resilience and workforce capacity across U.S. manufacturing supply chains.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Logarithmic Bayesian Approach to Quantum Error Detection

We consider the problem of continuous quantum error correction from a Bayesian perspective, proposing a pair of digital filters using logarithmic probabilities that are able to achieve near-optimal performance on a three-qubit bit-flip code, while still being reasonable to implement on low-latency hardware. These practical filters are approximations of an optimal filter that we derive explicitly for finite time steps, in contrast with previous work that has relied on stochastic differential equations such as the Wonham filter. By utilizing logarithmic probabilities, we are able to eliminate the need for explicit normalization and can reduce the Gaussian noise distribution to a simple quadratic expression. The state transitions induced by the bit-flip errors are modeled using a Markov chain, which for log-probabilities must be evaluated using a LogSumExp function. We develop the two versions of our filter by constraining this LogSumExp to have either one or two inputs, which favors either simplicity or accuracy, respectively. Using simulated data, we demonstrate that the single-term and two-term filters are able to significantly outperform both a double threshold scheme and a linearized version of the Wonham filter in tests of error detection under a wide variety of error rates and time steps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Distributed sustainable metals production: opportunities for intensifying separations & alternative feedstocks

Meeting the increased demand for rare earth elements and beyond primary mined ore. Secondary sources like phosphogypsum, coal fly ash, used nuclear fuel, and electronic waste are concentrated sources of critical minerals. One strategy for a resilient supply chain is to decentralize sourcing and hydrometallurgical processing of metals. Rotating packed beds (RPBs) for solid-phase extraction and liquid-liquid extraction are gaining momentum for their ability to increase process throughput by 5-10x the state of the art. In conclusion, this review discusses advances in hydrometallurgical RPB processes and opportunities for implementation in distributed metals production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Structural and Interphasial Stabilities of Sulfurized Polyacrylonitrile (SPAN) Cathode

Sulfurized polyacrylonitrile (SPAN) has attracted a lot of attention because of its low cost, high capacity, and great reversibility. Due to its structural complexity and amorphous nature, reaction mechanism of SPAN is little understood. Here, for this paper, we study the structural and interphasial changes of SPAN using synchrotron-based pair distribution function (PDF) analysis and soft X-ray absorption spectroscopy (sXAS). PDF identifies key structural features, including C–S bond, sulfur dimer, and sulfur chain in SPAN. The sulfur dimer bridging the pyridine network partially converts to sulfur chain during the first charging. In the following cycles, sulfur chain goes through lithiation and delithiation with reversibility dependent on the electrolytes. SXAS reveals surface changes of SPAN. After the first cycle, a negatively charged carbon or fused benzene layer is formed, on top of which is another layer formed by the electrolyte decomposition. The layer formed by localized high concentration electrolyte is stable during cycling.

25 ENERGY STORAGE↗
DOE OSTI2021

Bayesian analysis for estimating statistical parameter distributions of elasto-viscoplastic material models

High temperature design methods rely on constitutive models for inelastic deformation and failure typically calibrated against the mean of experimental data without considering the associated scatter. Variability may arise from the experimental data acquisition process, from heat-to-heat material property variations, or both and need to be accurately captured to predict parameter bounds leading to efficient component design. Applying the Bayesian Markov Chain Monte Carlo (MCMC) method to produce statistical models capturing the underlying uncertainty in the experimental data is an area of ongoing research interest. This work varies aspects of the Bayesian MCMC method and explores their effect on the posterior parameter distributions for a uniaxial elasto-viscoplastic damage model using synthetically generated reference data. From our analysis with the uniaxial inelastic model we determine that an informed prior distribution including different types of test conditions results in more accurate posterior parameter distributions. The parameter posterior distributions, however, do not improve when increasing the number of similar experimental data. Additionally, changing the amount of scatter in the data affects the quality of the posterior distributions, especially for the less sensitive model parameters. Moreover, we perform a sensitivity study of the model parameters against the likelihood function prior to the Bayesian analysis. The results of the sensitivity analysis help to determine the reliability of the posterior distributions and reduce the dimensionality of the problem by fixing the insensitive parameters. The comprehensive study described in this work demonstrates how to efficiently apply the Bayesian MCMC methodology to capture parameter uncertainties in high temperature inelastic material models. Quantifying these uncertainties in inelastic models will improve high temperature engineering design practices and lead to safer, more effective component designs.

42 ENGINEERING↗
DOE OSTI2025

Data Centers and Digital Assurance Workshop 2 – Prioritizing Digital Assurance Challenges, Session 2

The second session of the TADA (Technical Assistance for Digital Assurance) Data Centers Cohort, held on November 10, 2025, focused on prioritizing digital assurance challenges at the intersection of data centers and the electric grid. Building on the foundational concepts introduced in Workshop 1, this session deepened the application of the Threat–Vulnerability–Consequence (TVC) framework and emphasized the urgency of addressing cybersecurity, supply chain integrity, and operational reliability. Participants explored the growing convergence of digital and physical systems, the expanding attack surface due to global supply chain dependencies, and the implications of AI-driven load behavior. Real-world incidents—including the Volt Typhoon campaign and vulnerabilities in Solarman and Deye platforms—were analyzed to illustrate the risks of unpatched systems, insecure APIs, and inadequate vendor oversight. Key themes included architecture and interface weaknesses, governance gaps, and human and procedural shortcomings. The workshop also examined the evolving regulatory landscape, highlighting new federal mandates around Foreign Entity of Concern (FEOC) compliance and large-load reliability standards. Through interactive exercises, stakeholders ranked and mapped digital assurance risks from their respective perspectives—utilities, operators, and vendors—laying the groundwork for mitigation strategies and shared accountability models to be developed in Workshop 3. Session 2 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations

Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high- delity simulations of radiation transport within an urban domain. The model is constructed to employ a source location (x, y, z) as input and return the estimated count rate for a set of speci ed detector locations. Because MCNP simulations are computationally expensive, we develop e cient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes (GP) and neural networks (NN) that we train and verify using the MCNP simulations. The trained surrogate models provide an e cient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo (MCMC) algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source's location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Model-Data for Joint Estimation of Biogeochemical Model Parameters from Multiple Experiments: A Bayesian Approach Applied to Mercury Methylation

This modeling archive supports the manuscript submitted for publication in the Environmental Modeling and Software. This study is supported by ORNL-SFA and IDEAS-Watershed. This study aims to improve calibration of complex biogeochemical models using datasets from multiple experiments targeting specific subprocesses. The proposed Bayesian joint-fitting scheme calibrates the entire biogeochemical model in one go using all the available datasets and estimate parameter uncertainties using Markov Chain Monte Carlo (MCMC). This allows for complete propagation of uncertainties and utilization of the information shared between different datasets. Mapping joint distribution of parameters guides model improvement by identifying null spaces in the parameter space. This archive contains files used to perform MCMC, post-process outputs and visualize results.

East Fork Poplar Creek↗
DOE OSTI2022

Dark Energy Survey Year 3 results: marginalization over redshift distribution uncertainties using ranking of discrete realizations

ABSTRACT Cosmological information from weak lensing surveys is maximized by sorting source galaxies into tomographic redshift subsamples. Any uncertainties on these redshift distributions must be correctly propagated into the cosmological results. We present hyperrank, a new method for marginalizing over redshift distribution uncertainties, using discrete samples from the space of all possible redshift distributions, improving over simple parametrized models. In hyperrank, the set of proposed redshift distributions is ranked according to a small (between one and four) number of summary values, which are then sampled, along with other nuisance parameters and cosmological parameters in the Monte Carlo chain used for inference. This approach can be regarded as a general method for marginalizing over discrete realizations of data vector variation with nuisance parameters, which can consequently be sampled separately from the main parameters of interest, allowing for increased computational efficiency. We focus on the case of weak lensing cosmic shear analyses and demonstrate our method using simulations made for the Dark Energy Survey (DES). We show that the method can correctly and efficiently marginalize over a wide range of models for the redshift distribution uncertainty. Finally, we compare hyperrank to the common mean-shifting method of marginalizing over redshift uncertainty, validating that this simpler model is sufficient for use in the DES Year 3 cosmology results presented in companion papers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Battery Energy Storage Systems Report

Battery energy storage systems (BESS) are a critical component of grid reliability and resilience today, providing rapid response capabilities while enabling grid modernization and capacity expansion across the United States. As utilities, communities, and customers prepare to deploy significant BESS capacity over the next several years, the United States has an opportunity to build security into battery system design and deployments. This report provides a framework for assessing the current dominance of foreign-manufactured components in the supply chains for BESS, inverter-based resources, and transformers. It offers high-impact, actionable solutions to service partners, industry, and government to address supply chain risks for currently installed, in design, and future deployments.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Influence of Polar Mutations on the Electronic and Structural Properties of Q A –· in Bacterial Reaction Centers

Reaction centers from Rb. sphaeroides with residue M265 mutated from isoleucine to threonine, serine, and asparagine (M265IT, M265IS, and M265IN, respectively) in the Q A –· state are studied by high-resolution ESEEM and ENDOR spectroscopies to investigate the structural characteristics of these mutants influencing the redox properties of the Q A site. All three mutants decrease the redox midpoint potential (E m ) of Q A by ~0.1 V, yet the mechanism for this drop in E m is unclear. In this work, we examine (i) the hydrogen bonding interactions between Q A –· and residues histidine M219 and alanine M260, (ii) the electron spin density distribution of the semiquinone, and (iii) the orientations of the ubiquinone methoxy substituents. 13 C measurements show no significant contribution of methoxy dihedral angles to the observed decrease in E m for the Q A mutants. Instead, 14 N three-pulse ESEEM data suggest electrostatic or hydrogen bond formation between the mutated M265 side chain and His-M219 N δ may be involved in the observed lowering of the Q A midpoint potential. For mutant M265IN, analysis of the proton hyperfine couplings reveals a weakened hydrogen bond network, resulting in an altered Q A –· spin density distribution. Here, the magnetic resonance study presented here is therefore most consistent with an electrostatic or structural perturbation of the His-M219 N δ hydrogen bond in these mutants as a mechanism for the ~0.1 V decrease in Q A E m .

14 SOLAR ENERGY↗