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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evidence-based Graph Adversary Mapping (EGRAM) [Poster]

Cybersecurity companies such as CrowdStrike, Dragos, Microsoft and Unit 42 categorize Advanced Persistent Threats (APTs) using their own naming schemes. As a result, these APTs are mapped to different malware sources and campaigns, all from differing sources, leading to inconsistent mapping. Inconsistent mapping causes confusion and adds further obscurity around these groups, making it difficult to track and mitigate APT cyberattacks. The Evidence-based Graph Adversary Mapping (EGRAM) tool remediates the mapping challenge by collecting, updating and converting adversary data and their sources into a valid, codified STIX v2.1 bundle which is then stored in a Neo4j graph database. It utilizes graph traversal methods and centrality analysis to generate actionable information as a Structured Threat Intelligence Graph (STIG), based on user queries. EGRAM exists as Python code and a Jupyter Notebook that acts as a searchable, evidence-based, source of intelligence for APT groups’ artifacts and cyber campaigns.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Foot-and-Mouth Disease Virus Interserotypic Recombination in Superinfected Carrier Cattle

Viral recombination contributes to the emergence of novel strains with the potential for altered host range, transmissibility, virulence, and immune evasion. For foot-and-mouth disease virus (FMDV), cell culture experiments and phylogenetic analyses of field samples have demonstrated the occurrence of recombination. However, the frequency of recombination and associated virus–host interactions within an infected host have not been determined. We have previously reported the detection of interserotypic recombinant FMDVs in oropharyngeal fluid (OPF) samples of 42% (5/12) of heterologously superinfected FMDV carrier cattle. The present investigation consists of a detailed analysis of the virus populations in these samples including identification and characterization of additional interserotypic minority recombinants. In every animal in which recombination was detected, recombinant viruses were identified in the OPF at the earliest sampling point after superinfection. Some recombinants remained dominant until the end of the experiment, whereas others were outcompeted by parental strains. Genomic analysis of detected recombinants suggests host immune pressure as a major driver of recombinant emergence as all recombinants had capsid-coding regions derived from the superinfecting virus to which the animals did not have detectable antibodies at the time of infection. In vitro analysis of a plaque-purified recombinant virus demonstrated a growth rate comparable to its parental precursors, and measurement of its specific infectivity suggested that the recombinant virus incurred no penalty in packaging its new chimeric genome. These findings have important implications for the potential role of persistently infected carriers in FMDV ecology and the emergence of novel strains.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Harnessing Machine Learning and Data Fusion for Accurate Undocumented Well Identification in Satellite Images

This study utilizes satellite data to detect undocumented oil and gas wells, which pose significant environmental concerns, including greenhouse gas emissions. Three key findings emerge from the study. Firstly, the problem of imbalanced data is addressed by recommending oversampling techniques like Rotation–GaussianBlur–Solarization data augmentation (RGS), the Synthetic Minority Over-Sampling Technique (SMOTE), or ADASYN (an extension of SMOTE) over undersampling techniques. The performance of borderline SMOTE is less effective than that of the rest of the oversampling techniques, as its performance relies heavily on the quality and distribution of data near the decision boundary. Secondly, incorporating pre-trained models trained on large-scale datasets enhances the models’ generalization ability, with models trained on one county’s dataset demonstrating high overall accuracy, recall, and F1 scores that can be extended to other areas. This transferability of models allows for wider application. Lastly, including persistent homology (PH) as an additional input improves performance for in-distribution testing but may affect the model’s generalization for out-of-distribution testing. A careful consideration of PH’s impact on overall performance and generalizability is recommended. Overall, this study provides a robust approach to identifying undocumented oil and gas wells, contributing to the acceleration of a net-zero economy and supporting environmental sustainability efforts.

SMOTE↗
DOE OSTI2021

Sample Identifiers and Metadata to Support Data Management and Reuse in Multidisciplinary Ecosystem Sciences

Physical samples are foundational entities for research across biological, Earth, and environmental sciences. Data generated from sample-based analyses are not only the basis of individual studies, but can also be integrated with other data to answer new and broader-scale questions. Ecosystem studies increasingly rely on multidisciplinary team-science to study climate and environmental changes. While there are widely adopted conventions within certain domains to describe sample data, these have gaps when applied in a multidisciplinary context. In this study, we reviewed existing practices for identifying, characterizing, and linking related environmental samples. We then tested practicalities of assigning persistent identifiers to samples, with standardized metadata, in a pilot field test involving eight United States Department of Energy projects. Participants collected a variety of sample types, with analyses conducted across multiple facilities. We address terminology gaps for multidisciplinary research and make recommendations for assigning identifiers and metadata that supports sample tracking, integration, and reuse. Furthermore, our goal is to provide a practical approach to sample management, geared towards ecosystem scientists who contribute and reuse sample data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗
DOE OSTI2026

Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials

This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain‐specific constraints. We introduce a two‐dimensional readiness model that combines canonical preprocessing patterns with a five‐level operational readiness scale, both tailored to high‐performance computing (HPC) environments. This construct helps outline key challenges in transforming large‐scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross‐domain support for scalable and reproducible AI for science. Finally, we evaluate this maturity matrix in the context of case studies including ClimaX (climate), AFLOW (materials), OpenFold (proteomics), and DIII‐D fusion disruption‐prediction workflows, from which we distill lessons learned and provide recommendations to guide practitioners in developing robust AI‐readiness pipelines. Finally, we discuss remaining cross‐cutting challenges that persist across scientific domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High-Performance Hemofiltration via Molecular Sieving and Ultra-Low Friction in Carbon Nanotube Capillary Membranes

Conventional dialyzer membranes typically comprise of unevenly distributed polydisperse, tortuous, rough pores, embedded in relatively thick ≈20–50 µm polymer layers wherein separation occurs via size exclusion as well as differences in diffusivity of the permeating species. However, transport in such polymeric pores is increasingly hindered as the molecule size approaches the pore dimension, resulting in significant retention of undesirable middle molecules (≥15–60 kDa) and uremic toxins. Enhanced removal of middle molecules is usually accompanied by high albumin loss (≈66 kDa) causing hypoalbuminemia. Here, the scalable bottom-up fabrication of wafer-scale carbon nanotube (CNT) membranes with highly aligned, low-friction, straight-channels/capillaries and narrow pore-diameter distributions (≈0.5–4.5 nm) is demonstrated, to overcome persistent challenges in hemofiltration/hemodialysis. Using fluorescein isothiocyanate (FITC)-Ficoll 70 and albumin in phosphate buffered saline (PBS) as well as in bovine blood plasma, it is shown that CNT membranes can allow for significantly higher hydraulic permeability (more than an order of magnitude when normalized to pore area) than commercial high-flux hemofiltration/hemodialysis membranes (HF 400), as well as greatly enhance removal of middle molecules while maintaining comparable albumin retention. These findings are rationalized via an N-pore transport model that highlights the critical role of molecular flexing and deformation during size-selective transport within nanoscale confinements of the CNTs. In conclusion, the unique transport characteristics of CNTs coupled with size-exclusion and wafer-scale fabrication offer transformative advances for hemofiltration, and the obtained insight into molecular transport can aid advancements in several other bio-systems/applications beyond hemofiltration/hemodialysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Nondestructively Visualizing and Understanding the Mechano-Electro-chemical Origins of “Soft Short” and “Creeping” in All-Solid-State Batteries

All-solid-state Li-metal batteries (ASLMBs) represent a significant breakthrough in the quest to overcome limitations associated with traditional Li-ion batteries, particularly in energy density and safety aspects. However, widespread implementation is stymied due to a lack of profound understanding of the complex mechano-electro-chemical behavior of Li metal in the ASLMBs. Herein, operando neutron imaging and X-ray computed tomography (XCT) are leveraged to nondestructively visualize Li behaviors within ASLMBs. This approach offers real-time observations of Li evolutions, both pre- and post- occurrence of a “soft short”. The coordination of 2D neutron radiography and 3D neutron tomography enables charting of the terrain of Li metal deformation operando. Concurrently, XCT offers a 3D insight into the internal structure of the battery following a “soft short”. Despite the manifestation of a “soft short”, the persistence of Faradaic processes is observed. To study the elusive “soft short”, phase field modeling is coupled with electrochemistry and solid mechanics theory. The research unravels how external pressure curbs dendrite growth, potentially leading to dendrite fractures and thus uncovering the origins of both “soft” and “hard” shorts in ASLMBs. Furthermore, by harnessing finite element modeling, it dive deeper into the mechanical deformation and the fluidity of Li metal.

25 ENERGY STORAGE↗
DOE OSTI2025

A Panoramic View of MXenes via an Atomic Coordination‐Based Design Strategy

Two‐dimensional (2D) transition metal carbides and nitrides, known as MXenes, possess unique physical and chemical properties, enabling diverse applications in fields ranging from energy storage to communication, catalysis, sensing, healthcare, and beyond. Despite extensive research and notable advancements, a fundamental understanding of MXenes’ phase diversity and its connection to their hierarchical precursors, including the intermediate MAX phases and the ancestral bulk phases, remains limited. Here, in this study, it is hypothesized that the atomic coordination environments adopted by transition metal and nonmetallic atoms in their three‐dimensional (3D) bulk precursors may persist in 2D MXenes to govern their phase diversity. Using high‐throughput modeling based on first‐principles density functional theory, a wide range of MXene phases is unveiled and comprehensively evaluate their relative stabilities across a large chemical space. The key to the approach lies in considering various atomic coordination environments drawn from four types of ancestral bulk phases. Through this comprehensive structural library of MXenes, general guiding principles are uncovered, such as a close alignment between the phase stability of MXenes and that of their 3D precursors. These findings introduce a new design strategy in which the atomic coordination environments in bulk phases can serve as reliable predictors for accessing the diverse structural landscape of MXenes.

MXenes↗
DOE OSTI2025

Domain Nucleation and Growth in an Epitaxially Grown Wurtzite Ferroelectric

Ferroelectric domain nucleation and growth in epitaxial (Al, B, Sc)N films grown on n-GaN substrates are explored using a combination of ferroelectric property measurements and scanning transmission electron microscopy, including novel in situ switching studies. The films are electrically switched to nitrogen-polar (N-polar) and metal-polar (M-polar) configurations, attaining a remanent polarization of 120 µC cm −2 with coercive fields of ≈6 MV cm −1 . In the initial switching cycle, the ferroelectric domains nucleate near the bottom n-GaN electrode and develop domain walls with zigzag morphologies, while residual “dead layers” that do not switch from the as-deposited orientation persist at the top and bottom electrodes. The in situ microscopy experiments reveal that domain walls propagate fastest in the lateral direction, parallel to the electrode/film interface. These findings provide insights into the domain dynamics and structural evolution of wurtzite ferroelectrics, offering implications for next-generation electronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Silylsilane‐Substituted Polymers: Fluorine‐Free, Liquid Repellent, and Hydrolytically Stable Alternatives to Siloxanes

Abstract Per‐ and polyfluoroalkyl substances (PFAS) are omniphobic and exceptionally stable, making them ideal for a wide range of materials applications. However, as “forever chemicals”, their exceptional persistence and bioaccumulation have generated an increasing amount of environmental and health concerns, prompting the search for fluorine‐free alternatives. Siloxane‐based materials are therefore interesting to examine as PFAS alternatives due to their low surface energy and hydrophobicity but suffer from hydrolytic instability of Si─O linkages. To address this challenge, a novel methacrylate monomer, 3‐[tris(trimethylsilyl)silyl]propyl methacrylate (M‐silyl), is designed containing Si─Si bonds rather than Si─O bonds of siloxanes. Polymers derived from M‐silyl (P‐silyl) is prepared by controlled free radical polymerization and compared with their siloxane analogues after coating on silicon wafers. Contact angle (CA) measurements confirmed P‐silyl to be hydrophobic (advancing/receding water CA = 109.7°/83.0°), with a low‐hysteresis for oil (lyophobic) (hexadecane CA hysteresis = 2.0°), comparable to its siloxane‐based counterpart. Importantly, P‐silyl maintained these surface properties after exposure to acidic or basic conditions, while siloxane analogues lost hydrophobicity. Consequently, the incorporation of Si ─ Si linkages into the design of the polymer enables chemically stable, fluorine‐free alternatives for water‐repellent and low hysteresis surface coatings and advances the design of fluorine‐free functional materials.

fluorine-free↗
DOE OSTI2026

Stereochemically‐Controlled Fluorinated Copolymers for Selectively Permeable Barrier Applications

Selective oxygen permeability coupled with low water vapor transmission is essential for biomedical and packaging applications requiring controlled oxygen flux under humid conditions. However, most high‐performance barrier polymers depend on perfluoroalkyl substances (PFAS), whose persistence and regulatory restrictions limit their long‐term applicability. We designed a series of stereocontrolled thiol‐yne‐based polyesters, including both fluorinated and non‐fluorinated variants, for selective oxygen permeability with considerable water barrier performance. Tailoring polymer crystallinity and morphology tuned both oxygen transport and mechanical properties. Fluorinated polymers demonstrated enhanced hydrophobicity and water resistance while maintaining oxygen diffusivity within a range relevant to oxygen‐sensing applications. Structure–property relationships were elucidated through small‐ and wide‐angle X‐ray scattering, revealing semi‐crystalline domains influenced by fluorine content and dithiol chain length. Barrier performance was rigorously evaluated via water vapor transmission rate and dynamic vapor sorption, showing reduced water uptake with increasing dithiol monomer length and crystallinity. In conclusion, this work introduces a PFAS‐free alternative to conventional barrier materials and establishes a tunable materials platform with potential relevance for biomedical devices and packaging systems requiring controlled oxygen permeability.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Oxygen Vacancy Injection as a Pathway to Enhancing Electromechanical Response in Ferroelectrics

Since their discovery in late 1940s, perovskite ferroelectric materials have become one of the central objects of condensed matter physics and materials science due to the broad spectrum of functional behaviors they exhibit, including electro-optical phenomena and strong electromechanical coupling. In such disordered materials, the static properties of defects such as oxygen vacancies are well explored but the dynamic effects are less understood. In this work, the first observation of enhanced electromechanical response in BaTiO3 thin films is reported driven via dynamic local oxygen vacancy control in piezoresponse force microscopy (PFM). A persistence in peizoelectricity past the bulk Curie temperature and an enhanced electromechanical response due to a created internal electric field that further enhances the intrinsic electrostriction are explicitly demonstrated. The findings are supported by a series of temperature dependent band excitation PFM in ultrahigh vacuum and a combination of modeling techniques including finite element modeling, reactive force field, and density functional theory. Furthermore, this study shows the pivotal role that dynamics of vacancies in complex oxides can play in determining functional properties and thus provides a new route toward– achieving enhanced ferroic response with higher functional temperature windows in ferroelectrics and other ferroic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Van der Waals Interface Hosting Two Groups of Magnetic Skyrmions

Multiple magnetic skyrmion phases add an additional degree of freedom for skyrmion-based ultrahigh-density spin memory devices. Extending the field to 2D van der Waals magnets is a rewarding challenge, where the realizable degree of freedoms (e.g., thickness, twist angle, and electrical gating) and high skyrmion density result in intriguing new properties and enhanced functionality. In this work, a van der Waals interface, formed by two 2D ferromagnets Cr 2 Ge 2 Te 6 and Fe 3 GeTe 2 with a Curie temperature of ≈65 and ≈205 K, respectively, hosting two groups of magnetic skyrmions, is reported. Two sets of topological Hall effect signals are observed below 6s0 K when Cr 2 Ge 2 Te 6 is magnetically ordered. These two groups of skyrmions are directly imaged using magnetic force microscopy, and supported by micromagnetic simulations. Interestingly, the magnetic skyrmions persist in the heterostructure with zero applied magnetic field. The results are promising for the realization of skyrmionic devices based on van der Waals heterostructures hosting multiple skyrmion phases.

2D magnetism↗
DOE OSTI2022

A Novel $\sqrt {19} $ × $\sqrt {19} $ Superstructure in Epitaxially Grown 1T–TaTe 2

The spontaneous formation of electronic orders is a crucial element for understanding complex quantum states and engineering heterostructures in 2D materials. A novel $\sqrt {19} $ × $\sqrt {19} $ charge order in few-layer-thick 1T-TaTe 2 transition metal dichalcogenide films grown by molecular beam epitaxy, which has not been realized, is report. The photoemission and scanning probe measurements demonstrate that monolayer 1T-TaTe 2 exhibits a variety of metastable charge density wave orders, including the $\sqrt {19} $ × $\sqrt {19} $ superstructure, which can be selectively stabilized by controlling the post-growth annealing temperature. Moreover, it is found that only the $\sqrt {19} $ × $\sqrt {19} $ order persists in 1T-TaTe 2 films thicker than a monolayer, up to 8 layers. Furthermore, the findings identify the previously unrealized novel electronic order in a much-studied transition metal dichalcogenide and provide a viable route to control it within the epitaxial growth process.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Electro-Thermal Characterization of Dynamical VO 2 Memristors via Local Activity Modeling

We report translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large-scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO 2 /SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high-spatial-resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self-oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Magnetic Ordering in a High‐Spin Donor–Acceptor Conjugated Polymer

Abstract The development of open‐shell organic molecules that magnetically order at room temperature,which can be practically applied, remains a grand challenge in chemistry, physics, and materials science. Despite the exploration of vast chemical space, design paradigms for organic paramagnetic centers generally result in unpaired electron spins that are unstable or isotropic. Here, a high‐spin conjugated polymer is demonstrated, which is composed of alternating cyclopentadithiophene and benzo[1,2‐ c ;4,5‐ c ′]bis[1,2,5]thiadiazole heterocycles, in which macromolecular structure and topology coalesce to promote the spin center generation and intermolecular exchange coupling. Electron paramagnetic resonance (EPR) spectroscopy is consistent with spatially localized spins, while magnetic susceptibility measurements show clear anisotropic spin ordering and exchange interactions that persist at room temperature. The application of long‐range π‐correlations for spin center generation promotes remarkable stability. This work offers a fundamentally new approach to the implementation of this long‐sought‐after physical phenomenon within organic materials and the integration of manifold properties within emerging technologies.

Steelman, Michael E.↗
DOE OSTI2023

NiSi: A New Venue for Antiferromagnetic Spintronics

Envisaging antiferromagnetic spintronics pivots on two key criteria of high transition temperature and tuning of underlying magnetic order using straightforward application of magnetic field or electric current. Here, it is shown that NiSi metal can provide suitable new platform in this quest. First, the study unveils high-temperature antiferromagnetism in single-crystal NiSi with Néel temperature, T N $\geqslant$ 700 K. Antiferromagnetic order in NiSi is accompanied by non-centrosymmetric magnetic character with small ferromagnetic component in the a–c plane. Second, it is found that NiSi manifests distinct magnetic and electronic hysteresis responses to field applications due to the disparity in two moment directions. While magnetic hysteresis is characterized by one-step switching between ferromagnetic states of uncompensated moment, electronic behavior is ascribed to metamagnetic switching phenomena between non-collinear spin configurations. Importantly, the switching behaviors persist to high temperature. In conclusion, the properties underscore the importance of NiSi in the pursuit of antiferromagnetic spintronics.

36 MATERIALS SCIENCE↗