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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Effects of magnetic field assisted heat treatment on the microstructure and mechanical properties of Fe-0.63 %C alloy

This study investigates the influence of an applied magnetic field on the microstructural evolution and mechanical properties of hypoeutectoid steels subjected to heat treatment. Tensile tests and microstructural analysis were performed on samples processed under varying magnetic field strengths (0 T, 5 T, and 9 T) and different austenitization incubation times. The results indicate that the application of a magnetic field alters the fraction of proeutectoid ferrite phase without changing the cooling rates and heat treatment process. Additionally, pearlite microstructural features such as lamellar spacing and misorientation angles exhibit variations under different field strengths. While the pearlite nodule diameter remains largely unaffected, an increase in percentage elongation and strength is observed in the 5 T treated sample, attributed to changes in microstructural features with the magnetic field. Additionally, the percentage elongation is reduced in the samples heat treated with reduced austenitization incubation times. The study further demonstrates that low-angle misorientations increased in the samples taken parallel to the magnetic field direction, influencing the mechanical response. These findings suggest that applying a magnetic field during heat treatment provides an additional driving force for phase transformations, offering a manufacturing process for tailoring microstructures and optimizing mechanical properties. Moreover, integrating magnetic fields in heat treatment processes has potential benefits in energy efficiency.

High magnetic field↗
DOE OSTI2024

Prioritization of Early-Stage Research and Development of a Hydrogel-Encapsulated Anaerobic Technology for Distributed Treatment of High Strength Organic Wastewater

This study aims to support the prioritization of research and development (R&D) pathways of an anaerobic technology leveraging hydrogel-encapsulated biomass to treat high-strength organic industrial wastewaters, enabling decentralized energy recovery and treatment to reduce organic loading on centralized treatment facilities. To characterize the sustainability implications of early-stage design decisions and to delineate R&D targets, an encapsulated anaerobic process model was developed and coupled with design algorithms for integrated process simulation, techno-economic analysis, and life cycle assessment under uncertainty. Across the design space, a single-stage configuration with passive biogas collection was found to have the greatest potential for financial viability and the lowest life cycle carbon emission. Through robust uncertainty and sensitivity analyses, we found technology performance was driven by a handful of design and technological factors despite uncertainty surrounding many others. Hydraulic retention time and encapsulant volume were identified as the most impactful design decisions for the levelized cost and carbon intensity of chemical oxygen demand (COD) removal. Encapsulant longevity, a technological parameter, was the dominant driver of system sustainability and thus a clear R&D priority. Ultimately, we found encapsulated anaerobic systems with optimized fluidized bed design have significant potential to provide affordable, carbon-negative, and distributed COD removal from high strength organic wastewaters if encapsulant longevity can be maintained at 5 years or above.

Anaerobic Treatment↗
DOE OSTI2026

Spectral Properties and Energy Injection in Mercury's Magnetotail Current Sheet

Mercury's magnetotail hosts a thin and highly dynamic current sheet (CS), where magnetic reconnection and strong fluctuations frequently occur. Here, we statistically analyze magnetic field power spectra across 370 magnetotail CSs observed by MESSENGER. About 20% of the events are quasi-laminar, showing single power-law spectra, whereas ∼80% are turbulent, exhibiting a spectral break separating inertial and kinetic ranges. A dawn–dusk asymmetry is identified: inertial-range slopes are systematically shallower on the dawnside, whereas kinetic-range slopes are steeper, indicating more developed turbulence there, consistent with the higher occurrence of reconnection-related processes on the dawnside. Component analysis shows that the transverse components, orthogonal to the tail-aligned principal field ( B X ), display shallow slopes near −1 in the inertial range, suggesting energy injection at ion scales rather than a classical inertial range. These results demonstrate that Mercury's unique plasma environment fundamentally reshapes the initiation of turbulence and the redistribution of energy in the magnetotail.

Mercury↗
DOE OSTI2025

Upcycling waste polystyrene to adipic acid through a hybrid chemical and biological process

Oxidative catalytic depolymerization of polystyrene (PS) can produce benzoic acid, but the annual consumption of benzoic acid is ~40 times lower than PS. For this catalytic oxidation method to be a viable means to manage PS waste, benzoic acid should be converted to higher-volume chemicals. We demonstrate a hybrid chemical and biological process that uses PS as feedstock for production of adipic acid, a high-volume co-monomer for nylon 6,6 via benzoic acid. Mn/Br co-catalyzed autoxidation of PS to benzoic acid proceeds with a yield of up to 94% in a solvent mixture of benzoic acid and water. The PS-derived benzoic acid undergoes bioconversion at near-quantitative yield to muconic acid, which is readily converted to adipic acid through catalytic hydrogenation. Process modeling, techno-economic analysis, and life cycle assessment estimate an adipic acid minimum selling price of $3.18/kg, with a 61% decrease in greenhouse gas emissions relative to production from fossil fuels.

09 BIOMASS FUELS↗
DOE OSTI2025

Production of bio-based lactones as monomers for a circular polymer economy

To create a circular plastics economy, new polymers are being developed that can be chemically recycled. Circular polyesters are of particular interest and to this end, lactones are ideal monomers. Here, this Review examines catalytic routes to convert diols, hydroxy acids, and dicarboxylic acids to lactones, focusing on the development of scalable, atom-economic, and energy-efficient conversions of bio-derived feedstocks. Free energy analysis is used to inform process choices, such as reactor type, reaction phase, and use of solvent. Catalyst design principles are summarized for both direct (bio-substrate to lactone) and indirect (bio-substrate to intermediate to lactone) routes. Finally, we summarize literature that shows that many lactone precursors are readily accessible from various metabolic and chemo-catalytic pathways. Transitioning to bio-based monomers offers an opportunity to reduce reliance on fossil carbon resources, but requires advanced catalytic processes informed by mechanistic insights.

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

Machine learning-assisted profiling of a kinked ladder polymer structure using scattering

Ladder polymers consisting of fused rings in the backbone have very limited conformational freedom, which results in very different properties from traditional linear polymers. However, accurately determining their size and chain conformations from solution scattering remains a challenge. Their chain conformations of kinked ladder polymers are largely governed by the structures and relative orientations or configurations of the repeat units, unlike conventional polymer chains whose bending angles between repeat units follow a unimodal Gaussian distribution. Meanwhile, traditional scattering models for polymer chains do not account for these unique structural features. This work introduces a novel approach that integrates machine learning with Monte Carlo simulations to construct a model that can describe the geometry of a type of kinked CANAL ladder polymers. We first develop a Monte Carlo simulation model for sampling the configuration space of CANAL ladder polymers, where each repeat unit is modeled as a biaxial segment. Then, we establish a machine learning-assisted scattering analysis framework based on Gaussian Process Regression. Finally, we conduct small-angle neutron scattering experiments on a CANAL ladder polymer solution to apply our approach. Our method uncovers structural features of such ladder polymers that conventional methods fail to capture.

Ding, Lijie [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2024

The 4D Camera: An 87 kHz Direct Electron Detector for Scanning/Transmission Electron Microscopy

We describe the development, operation, and application of the 4D Camera—a 576 by 576 pixel active pixel sensor for scanning/transmission electron microscopy which operates at 87,000 Hz. The detector generates data at ~480 Gbit/s which is captured by dedicated receiver computers with a parallelized software infrastructure that has been implemented to process the resulting 10–700 Gigabyte-sized raw datasets. The back illuminated detector provides the ability to detect single electron events at accelerating voltages from 30 to 300 kV. Through electron counting, the resulting sparse data sets are reduced in size by 10--300× compared to the raw data, and open-source sparsity-based processing algorithms offer rapid data analysis. The high frame rate allows for large and complex scanning diffraction experiments to be accomplished with typical scanning transmission electron microscopy scanning parameters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Order-by-order uncertainties of nucleon-nucleon Wolfenstein amplitudes in chiral effective field theory

Quantum mechanical invariance principles dictate the most general operator structure that can be present in the nucleon-nucleon (NN) interaction. Five independent operators appear in the on-shell NN amplitude together with five corresponding coefficient functions. The usual choice for these coefficient functions is known as the NN Wolfenstein amplitudes. We analyze the order-by-order convergence of each of the five NN Wolfenstein amplitudes predicted by a semilocal coordinate space potential implementation of chiral effective field theory (𝜒⁢EFT). We do this at laboratory kinetic energies between 25 and 200 MeV for both neutron-proton and proton-proton scattering. Our analysis uses the Gaussian-process methods developed by the BUQEYE Collaboration to describe the contributions of each 𝜒⁢EFT order, and so yields truncation uncertainties for each Wolfenstein amplitude that are correlated across scattering angles. We combine information on the size of different orders in the EFT to infer the 𝜒⁢EFT breakdown scale for each amplitude, finding, on average, Λ 𝑏 between 750 and 800 MeV. Furthermore, with this choice of Λ 𝑏 , the EFT truncation uncertainties cover both higher-order results and empirical Wolfenstein amplitudes well for all orders other than the leading order.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

A Typology of Decision-Making Tasks for Visualization

Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxonomies and typologies exist, they often focus on more granular analytical tasks that are too low-level to describe large complex decisions, which can make it difficult to reason about and design decision-support tools. In this paper, we contribute a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review. Our typology is concise and consists of only three tasks: CHOOSE, ACTIVATE, and CREATE. Although decision types originating in other disciplines exist, we provide definitions for these tasks that are suitable for the visualization community. Our proposed typology offers two benefits. First, the ability to compose and hierarchically organize the tasks enables flexible and clear descriptions of decisions with varying levels of complexities. Second, the typology encourages productive discourse between visualization designers and domain experts by abstracting the intricacies of data, thereby promoting clarity and rigorous analysis of decision-making processes. We demonstrate the benefits of our typology through four case studies, and present an evaluation of the typology from semi-structured interviews with experienced members of the visualization community who have contributed to developing or publishing decision support systems for domain experts. Our interviewees used our typology to delineate the decision-making processes supported by their systems, demonstrating its descriptive capacity and effectiveness. Finally, we present preliminary findings on the usefulness of our typology for visualization design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Revealing atomic-scale switching pathways in van der Waals ferroelectrics

Two-dimensional (2D) van der Waals (vdW) materials hold the potential for ultrascaled ferroelectric (FE) devices due to their silicon compatibility and robust polarization down to atomic scale. However, the inherently weak vdW interactions enable facile sliding between layers, introducing complexities beyond those encountered in conventional ferroelectric materials and presenting substantial challenges in uncovering intricate switching pathways. Here, we combine atomic-resolution imaging under in situ electrical biasing conditions with first-principles calculations to unravel the atomic-scale switching mechanisms in SnSe, a vdW group IV monochalcogenide. Our results uncover the coexistence of a consecutive 90° switching pathway and a direct 180° switching pathway from antiferroelectric (AFE) to FE order in this vdW system. Atomic-scale investigations and strain analysis reveal that the switching processes simultaneously induce interlayer sliding and compressive strain, while the lattice remains coherent despite the presence of multidomain structures. These findings elucidate vdW ferroelectric switching dynamics at atomic scale and lay the foundation for the rational design of 2D ferroelectric nanodevices.

Li, Xinyan [Rice University, Houston, TX (United S↗
DOE OSTI2026

Polyketide synthase–like functionality acquired by plant fatty acid elongase

Fatty acid elongation typically proceeds through a four-step cycle of condensation, reduction, dehydration, and reduction for each two-carbon extension. Here, we describe a variation of this pathway in Orychophragmus limprichtianus, whose seed oil contains previously unknown C24-C28 keto-hydroxy fatty acids that account for ~25% of total fatty acids. These compounds are produced through an endoplasmic reticulum–localized discontinuous elongation process in which a 3-keto-hydroxy intermediate bypasses full reduction and is extended through a polyketide synthase–like mechanism. Transcriptomic and functional assays identified two divergent enzymes, a variant fatty acid elongase 1 (FAE1) and a low-activity 3-ketoacyl-CoA reductase (KCR1), as central to this process. Protein modeling and mutant analysis suggest that specific amino acid substitutions underlie altered KCR1 activity, enabling accumulation of keto intermediates. Our findings reveal unexpected flexibility in plant fatty acid elongation and provide innovative tools for engineering plants and microbes to produce renewable oils with tailored industrial functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Life Cycle Assessment of Formic Acid as a Liquid Hydrogen Carrier

This work presents a life cycle analysis of the transport and processing for formic acid into hydrogen. The system boundary begins at the formic acid plant gate and ends with hydrogen end use. The functional unit is 1 kg of hydrogen delivered to end use, 1 kWh for electricity generation, and 1 vehicle mile traveled (VMT). Renewable electricity reduced GWP by 80-90% relative to U.S. average grid.

Shen, Xinyao [NETL Site Support Contractor, Nation↗
DOE OSTI2024

A Computational Review of Privacy-Preserving Mechanisms for the Smart Grid

Smart grid technologies have rapidly become one of the largest and most comprehensive sources of data for the modern utility. For the most part, data streams are seen as an essential tool that enable utilities to carry their day-to-day business operations, but they also create the need for efficient and secure data management strategies. In the context of the smart grid, ensuring data privacy is becoming an increasing concern due to a combination of factors that range from shifts in operational paradigms and rapid technology evolution to changes in legislation. Furthermore, researchers have highlighted the risks associated with improperly protected energy records. For example, energy consumption data from homes could be used to infer the behaviors and habits of home occupants through activity recognition or user profiling (Fan, 2017), which may lead to unfair service pricing, targeted advertising, or other personal security violations. Similarly, Electric Vehicles’ (EVs) charging metadata could be used to reveal private information about the owner such as their payment methods, preferred charging stations, and other locational and timing information that could be used to reconstruct the vehicle owner’s behaviors. The privacy of user data, even when used for statistical analysis or machine learning training processes, also needs to be carefully considered, as an individual’s private traits may still be vulnerable if their inclusion/exclusion greatly impacts the result or could be linked to a public dataset through cross-reference. The breach of user privacy also has severe impacts for organizations that store, transmit, or work on the data in the form of diminishing the public’s trust in them while potentially incurring legal consequences (e.g., fines and suspensions under the European Union General Data Protection Regulation, Health Insurance Portability and Accountability Act, etc.). Because of these risks, several privacy-preserving mechanisms are available to help organizations comply with privacy legislations and prevent the unauthorized and malicious use of user data. In light of these concerns, this report focuses on performing a computational review of privacy-preserving mechanisms that have received a significant amount of interest in literature. It specifically focuses on 1) homomorphic encryption, 2) zero-knowledge proofs, 3) differential privacy, and 4) federated learning. It is worth noting that although many of the methods presented in this document rely on cryptographic primitives, their intent is not to provide perfect secrecy, but rather to enable users to maintain privacy, and thus they shall not be compared or equated to other constructs that are aimed to address cybersecurity constructs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

The Role of Three-dimensional Effects on Ion Injection and Acceleration in Perpendicular Shocks

Understanding the conditions that enable particle acceleration at nonrelativistic collisionless shocks is essential to unveil the origin of cosmic rays. We employ 2D and 3D hybrid simulations (with kinetic ions and fluid electrons) to explore particle acceleration and magnetic field amplification in nonrelativistic perpendicular shocks, focusing on the role of shock drift acceleration and its dependence on the shock Mach number. We perform an analysis of the ion injection process and demonstrate why efficient acceleration is only observed in 3D. In particular, we show that ion injection critically depends on the “porosity” of the magnetic turbulence in the downstream region near the shock, a property describing how easily the postshock region allows particles to traverse it and return upstream without being trapped. This effect can only be properly captured in 3D. Additionally, we explore the impact of numerical resolution on ion energization, highlighting how resolving small-scale turbulence—on scales below the thermal ion gyroradius—is essential for accurately modeling particle injection. Overall, our results emphasize the necessity of high-resolution 3D simulations to capture the fundamental microphysics driving particle acceleration at perpendicular shocks.

Orusa, Luca [Princeton Univ., NJ (United States); ↗
DOE OSTI2025

On-Demand Column Joining for High Energy Physics

As the Large Hadron Collider (LHC) transitions into the High-Luminosity LHC (HL-LHC) era, the volume of data to be processed is expected to increase significantly. The CMS Experiment currently utilizes various data formats, including AOD, MiniAOD, and NanoAOD, each with different levels of detail and storage requirements. This paper addresses the challenges of data duplication and storage inefficiencies in high-energy physics (HEP) analyses by proposing an on-demand column-joining solution. This approach aims to reduce data duplication by enabling the dynamic combination of NanoAOD data with auxiliary information from larger data tiers, such as MiniAOD. The proposed solution leverages Trino, a high-performance distributed SQL query engine, to perform efficient and scalable data joins. Benchmarks using CMS OpenData demonstrate the feasibility of this approach, showing that it can handle large datasets with low latency. Integration with the scikit-hep ecosystem and the coffea analysis framework is also discussed, highlighting the potential for seamless end-to-end data processing and analysis. Ongoing and future work focuses on expanding benchmarks, integrating ServiceX for data transformation, and exploring the use of native object storage solutions.

Manganelli, Nicholas [Northeastern U.]↗
DOE OSTI2026

Phenomena-based graph representations and applications to chemical process simulation

Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture the physical phenomena taking place in the process (e.g., material and energy conservation, phase equilibrium, reactions). In this work, we show that graph-theoretic representations of the physical phenomena within unit operations can help navigate and decompose equations to systematically identify alternative approaches for fast and robust numerical solutions. Specifically, we present a graph-theoretic abstraction that captures the connectivity between the model variables/equations and use this abstraction to group variables/equations into fundamental phenomena. We show that phenomena-based decomposition of the underlying equations can help decouple nonlinearities and enforce material/energy conservation at the process level to accelerate convergence. The proposed decomposition approach differs from the more traditional sequential modular simulation approach, in which equations are grouped and decomposed by unit operations. We implemented the phenomena-based decomposition in BioSTEAM—an open-source process simulation platform in Python—and demonstrated that this approach can converge a variety of separation process models. Compared to sequential modular simulation, the phenomena-based approach can converge idealized systems faster, but it can be slower for (or even fail to converge) highly coupled and nonideal process systems.

Convergence↗
DOE OSTI2024

Optimal Design of Food Packaging Considering Waste Management Technologies to Achieve Circular Economy

Plastic packaging plays a fundamental role in the food industry, avoiding food waste and facilitating food access. The increasing plastic production and the lack of appropriate plastic waste management technologies represent a threat to the environmental and human welfare. Therefore, there is an urgent need to identify sustainable packaging solutions. Circular economy (CE) promotes reducing waste and increasing recycling practices to achieve sustainability. In this work, we propose a CE framework based on multi-objective optimization, considering both economic and environmental impacts, to identify optimal packaging designs and waste management technologies. Using mixed-integer linear programming (MILP), techno-economic analysis (TEA), and life cycle assessment (LCA), this work aims to build the first steps in packaging design, informing about the best packaging alternatives and the optimal technology or technologies to process packaging waste. For the economic analysis, we consider the minimum increase in price (MIP) when adding recycling to the cost of each packaging solution, while for the environmental analysis, the greenhouse gas emissions impact was considered. A case study on ground coffee packaging is used to illustrate the proposed framework. The results demonstrate that the multilayer bag option is the most convenient when considering both the chosen economic and environmental impacts.

Life Cycle Analysis↗
DOE OSTI2024

Correlation of Processing and Structure in an Ethylene‐Glycol Side‐Chain Modified Polythiophene via Combined X‐Ray Scattering and 4D Scanning Transmission Electron Microscopy

The results of a combined grazing incidence wide-angle X-ray scattering (GIWAXS) and 4D scanning transmission microscopy (4D-STEM) analysis of the effects of thermal processing on poly(3[2-(2-methoxyethoxy)ethoxy]-methylthiophene-2,5-diyl) are reported, a conjugated semiconducting polymer used as the active layer in organic electrochemical transistor devices. GIWAXS provides a measure of overall crystallinity in the film, while 4D-STEM produces real-space maps of the morphology and orientation of individual crystallites along with their spatial extent and distribution. The sensitivity of the 4D-STEM detector allows for collection of electron diffraction patterns at each position in an image scan while limiting the imparted electron dose to below the damage threshold. In conclusion, the effects of heat treatment on the distribution and type of crystallites present in the films is determined.

36 MATERIALS SCIENCE↗