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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

GRAIL Gravity Observations of the Transition from Complex Crater to Peak-Ring Basin on the Moon: Implications for Crustal Structure and Impact Basin Formation

High-resolution gravity data from the Gravity Recovery and Interior Laboratory (GRAIL) mission provide the opportunity to analyze the detailed gravity and crustal structure of impact features in the morphological transition from complex craters to peak-ring basins on the Moon. We calculate average radial profiles for free-air anomalies and Bouguer anomalies for peak-ring basins, proto-basins, and the largest complex craters. Complex craters and proto-basins have free-air anomalies that are positively correlated with surface topography, unlike the prominent lunar mascons (positive free-air anomalies in areas of low elevation) associated with large basins. The Bouguer gravity anomaly profiles of complex craters are highly irregular, with central positive anomalies that are generally absent or not clearly tied to interior morphology. In contrast, gravity profiles for peak-ring basins (approx. 200 km to 580 km) are much more regular and are highly correlated with surface morphology. A central positive Bouguer anomaly is confined within the peak ring and a negative Bouguer anomaly annulus extends from the edge of the positive anomaly outward to about the rim crest. A number of degraded basins lacking interior peak rings have diameters and gravity patterns similar to those of well-preserved peak-ring basins. If these structures represent degraded peak-ring basins, the number of peak-ring basins on the Moon would increase by more than a factor of two to 34. The gravity anomalies within basins are interpreted to be due to uplift of the mantle confined within the peak ring and an annulus of thickened crust between the peak ring and rim crest. We hypothesize that mantle uplift is influenced by interaction between the transient cavity and the mantle. Further, mascon formation is generally disconnected from the number of basin rings formed and occurs over a wide range of basin sizes. These observations have important implications for models of basin and mascon formation on the Moon and other planetary bodies.

Baker, David M. H.↗
DOE OSTI2026

Multiscale Characterization of Electrode-Induced Degradation in Perovskite Solar Cells

The stability of metal-halide-perovskite (MHP) solar cells must be understood and improved for the commercial viability of MHP technologies. Here, we apply multiscale characterization methods to study degradation modes, specifically electrode corrosion, for p-i-n MHP partial device stacks and full devices that are stored in the dark under an inert atmosphere. Our multiscale characterization approaches include full-device electro-optical performance using current-voltage (JV) curves and spatial imaging with electroluminescence (EL) and photoluminescence (PL). We further correlate interface properties using cross-sectional Kelvin probe force microscopy, which maps the nanoscale electric field properties, and electron microscopy, which demonstrates structural and chemical features. Devices stored as a full device stack degrade primarily by metal (Ag) electrode diffusion into the absorber, with formation of AgI byproducts and Ag accumulation near the indium tin oxide (ITO) contact. This causes decomposition of the perovskite absorber domains, loss of the potential drop at the electron transport layer (ETL)/perovskite interface near the metal contact, and increased equivalent resistance at the perovskite/hole transport layer (HTL) interface near the ITO contact. The devices stored without metal show a different degradation pathway dominated by corrosion of the ITO, creating voids at the ITO electrode surface with diffusion of In and Sn into the absorber. We conclude that metal electrode-induced degradation is the most severe degradation pathway under dark storage, but that ITO corrosion and absorber instability must also be mitigated. We further demonstrate mitigation of these degradation pathways by changes to the device stack, including a SnO x blocking layer at the ETL side and replacing ITO with FTO at the HTL side. These results provide a useful demonstration of specific dark degradation pathways at each electrode interface, as well as a unique multiscale example that links degradation of chemical, structural, and electrical interface properties to the full-device electro-optical characteristics.

14 SOLAR ENERGY↗
NASA NTRS1983

NASTRAN forced vibration analysis of rotating cyclic structures

Theoretical aspects of a new capability developed and implemented in NASTRAN level 17.7 to analyze forced vibration of a cyclic structure rotating about its axis of symmetry are presented. Fans, propellers, and bladed shrouded discs of turbomachines are some examples of such structures. The capability includes the effects of Coriolis and centripetal accelerations on the rotating structure which can be loaded with: (1) directly applied loads moving with the structure and (2) inertial loas due to the translational acceleration of the axis of rotation (''base' acceleration). Steady-state sinusoidal or general periodic loads are specified to represent: (1) the physical loads on various segments of the complete structure, or (2) the circumferential harmonic components of the loads in (1). The cyclic symmetry feature of the rotating structure is used in deriving and solving the equations of forced motion. Consequently, only one of the cyclic sectors is modelled and analyzed using finite elements, yielding substantial savings in the analysis cost. Results, however, are obtained for the entire structure. A tuned twelve bladed disc example is used to demonstrate the various features of the capability.

Elchuri, V.↗
DOE OSTI2025

OpenEdge: A collaborative, open-source, multi-purpose direct simulation Monte Carlo for plasma simulation in magnetic fusion environments

OpenEdge is a collaborative, open-source, object-oriented Direct Simulation Monte Carlo (DSMC) code, designed specifically for plasma simulations in magnetic fusion environments. Here, the code features include advanced structures, robust capabilities, and an effective parallelization strategy, all of which significantly enhance performance. It includes specialized modules for managing complex particle interactions, including collisions, ionization/recombination, and reflection/sputtering. Benchmarks and performance analyses have confirmed its efficiency and scalability. Versatile and adaptable, OpenEdge is applied across a broad spectrum of plasma-material interaction studies and charged particle transport in various fusion research settings.

Boundary plasma↗
DOE OSTI2025

Effect of Networking Density on the Patterning Performance of Molecular Layer Deposited Alucone Electron Beam/EUV Resists

One of the main barriers to continued device scaling in the era of extreme ultraviolet (EUV) lithography is the need for improved photoresist chemistries to address challenges such as poor EUV sensitivity, inadequate etch resistance, and pattern collapse. Metal-organic photoresists are a promising class of materials that can address many of these challenges, and among them, resists deposited via hybrid molecular layer deposition (MLD) have attracted interest for their unique advantages in thickness control, chemical homogeneity, and compatibility with vacuum processing. However, despite many successful demonstrations of patterning, little is known about how the molecular design of hybrid MLD resists affects their lithographic performance. In this work, we study the effect of the network structure, a common feature among all hybrid MLD resists, via a series of aluminum alkoxide ("alucone") negative tone resists with varying networking density. Their patterning mechanism is investigated via electron beam lithography (EBL)─a common proxy for EUV─and compared to their EUV-induced reactions studied via flood exposure and in situ characterization. In conclusion, we show that the resist with the least networking density demonstrates the best sensitivity and resolution, with the ability to resolve dense line/space gratings as small as 14 nm half pitch via EBL.

electron beam lithography↗
DOE OSTI2025

MnSi 2 Te 4 : A van der Waals Antiferromagnetic Semiconductor with Large Negative Magnetoresistance

Magnetism in van der Waals semiconductors offers significant potential for fundamental research on low-dimensional magnetism and the development of high-performance two-dimensional spintronic devices. Here, we report the growth, physical properties, and first-principles calculations of a new dual-octahedral transition metal chalcogenide (DTMC) MnSi 2 Te 4 . MnSi 2 Te 4 features a layered structure with an intralayer heterostructure, where the metal octahedra and nonmetal dimeric octahedra form zigzag chains alternately. Property characterization reveals that MnSi 2 Te 4 is a collinear G-type antiferromagnetic semiconductor, with a Néel temperature T N of 18.6 K and a significant unsaturated negative magnetoresistance (NMR) reaching −42.5% at 9 T and 100 K. First-principles calculations on the electronic band structure demonstrate that the large NMR primarily originates from the spin splitting due to parity-time symmetry breaking. This study not only discovers a new member of DTMCs with a unique crystal structure and large NMR, but also establishes a promising platform for investigating next-generation spintronic devices.

Liao, Ke [Chinese Academy of Sciences (CAS), Beiji↗
DOE OSTI2024

Nondestructive flash cathode recycling

Effective recycling of end-of-life Li-ion batteries (LIBs) is essential due to continuous accumulation of battery waste and gradual depletion of battery metal resources. The present closed-loop solutions include destructive conversion to metal compounds, by destroying the entire three-dimensional morphology of the cathode through continuous thermal treatment or harsh wet extraction methods, and direct regeneration by lithium replenishment. Here, we report a solvent- and water-free flash Joule heating (FJH) method combined with magnetic separation to restore fresh cathodes from waste cathodes, followed by solid-state relithiation. The entire process is called flash recycling. This FJH method exhibits the merits of milliseconds of duration and high battery metal recovery yields of ~98%. After FJH, the cathodes reveal intact core structures with hierarchical features, implying the feasibility of their reconstituting into new cathodes. Relithiated cathodes are further used in LIBs, and show good electrochemical performance, comparable to new commercial counterparts. Life-cycle-analysis highlights that flash recycling has higher environmental and economic benefits over traditional destructive recycling processes.

25 ENERGY STORAGE↗
DOE OSTI2024

A V 3 Sb 5 kagome superconductors

Here, the recent discovery of the AV 3 Sb 5 (A = K, Rb, Cs) kagome superconductors launched a growing field of research investigating the interplay between superconductivity and charge-density wave order in kagome metals. Specifically, the AV 3 Sb 5 family of materials naturally exhibits a Fermi level close to the Van Hove singularities associated with the saddle points formed from the prototypical kagome band structure. The charge-density wave and superconducting states that form within the kagome networks of these compounds exhibit a number of anomalous properties reminiscent of theoretical predictions of exotic states in kagome metals tuned close to their Van Hove fillings. In this Review, we discuss the key structural and electronic features of AV 3 Sb 5 compounds and survey the status of investigations of their unconventional electronic phase transitions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Machine learning prediction of enzyme optimum pH

The relationship between pH and enzyme catalytic activity, especially the optimal pH (pH opt ) at which enzymes function, is critical for biotechnological applications. Hence, computational methods to predict pH opt will enhance enzyme discovery and design by facilitating accurate identification of enzymes that function optimally at specific pH levels, and by elucidating sequence-function relationships. Here, in this study, we proposed and evaluated various machine learning methods for predicting pH opt , conducting extensive hyperparameter optimization and training over 11,000 model instances. Our results demonstrate that models utilizing language model embeddings markedly outperform other methods in predicting pHopt. We present EpHod, the best-performing model, to predict pHopt, making it publicly available to researchers. From sequence data, EpHod directly learns structural and biophysical features that relate to pH opt , including proximity of residues to the catalytic centre and the accessibility of solvent molecules. Overall, EpHod presents a promising advancement in pH opt prediction and will potentially speed up the development of enzyme technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Validation of galvanomagnetic and thermomagnetic transport measurements using Standard Reference Material 3451

In the “method of four coefficients,” electrical resistivity (ρ), Seebeck coefficient (S), Hall coefficient (RH), and Nernst coefficient (Q) of a material are measured and typically fit or modeled with theoretical expressions based on Boltzmann transport theory to glean experimental insights into features of electronic structure and/or charge carrier scattering mechanisms in materials. Although well-defined and readily available reference materials exist for validating measurements of ρ and S, none currently exists for R H or Q. We show that measurements of all four transport coefficients—ρ, S, R H , and Q—can be validated using a single reference sample, namely, the low-temperature Seebeck coefficient Standard Reference Material® (SRM) 3451 (composition Bi 2 Te 3+x ) available from the National Institute for Standards and Technology (NIST) without the need for inter-laboratory sample exchange. Here, R H and Q data for NIST SRM 3451 reported here for the temperature range 80–400 K complement the data already available for ρ and S and will therefore be of interest to researchers desiring to validate new or existing galvanomagnetic and thermomagnetic transport properties measurement systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A Hierarchical OPF Algorithm with Improved Gradient Evaluation in Three-Phase Networks

Linear approximation commonly used in solving alternating-current optimal power flow (AC-OPF) simplifies the system models but incurs accumulated voltage errors in large power networks. Such errors will make the primal-dual type gradient algorithms converge to solutions with voltage violation. In this paper, we improve a recent hierarchical OPF algorithm that rested on primal-dual gradients evaluated with a linearized distribution power flow model. Specifically, we propose a more accurate gradient evaluation method based on an unbalanced three-phase nonlinear distribution power flow model to mitigate the errors arising from linearization. The resultant gradients feature a blocked structure that enables our development of an improved hierarchical primal-dual algorithm to solve the OPF problem. Numerical results on the IEEE 123-bus test feeder and a 4,518-node test feeder show that the proposed method can enhance voltage safety at comparable computational efficiency with the linearized algorithm.

approximation algorithms↗
DOE OSTI2025

SigTime: Learning and Visually Explaining Time Series Signatures

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. Here, to address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system—SigTime—with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Deer Vigilance and Movement Behavior Are Affected by Edge Density and Connectivity

ABSTRACT Animal behavior is an important component of individual, population, and community responses to anthropogenic habitat alteration. For example, antipredator behavior (e.g., vigilance) and animal movement behavior may both be important behavioral responses to the increased density of habitat edges and changes in patch connectivity that characterize highly modified habitats. Importantly, edge density and connectivity might interact, and this interaction is likely to mediate animal behavior: linear, edge‐rich landscape features often provide structural connectivity between patches, but the functional connectedness of patches for animal use could depend upon how edge density modifies animal vigilance and movement. Using remote cameras in large‐scale experimental landscapes that manipulate edge density (high‐ vs. low‐density edges) and patch connectivity (isolated or connected patches), we examined the effects of edge density and connectivity on the antipredator behavior and movement behavior of white‐tailed deer ( Odocoileus virginianus ). Deer vigilance was 1.38 times greater near high‐density edges compared to low‐density edges, regardless of whether patches were connected or isolated. Deer were also more likely to move parallel to connected high‐density edges than all other edge types, suggesting that connectivity promotes movement along high‐density edges. These results suggest that increases in edge density that accompany human fragmentation of existing habitats may give rise to large‐scale changes in the antipredator behavior of deer. These results also suggest that conservation strategies that simultaneously manipulate edge density and connectivity (i.e., habitat corridors) may have multiple effects on different aspects of deer behavior: linear habitat corridors were areas of high vigilance, but also areas where deer movement behavior implied increased movement along the habitat edge.

Bartel, Savannah L. [University of Wisconsin‐Madis↗
DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗
DOE OSTI2026

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗
DOE OSTI2024

Artificial-Intelligence Aided Design and Synthesis of Novel Layered 2D Multi-Principal Element Materials for Energy Storage (Final Report)

This DOE-EPSCoR project aimed to predict, synthesize, and characterize novel layered two-dimensional (2D) high-entropy materials (HEMs). These 2D-HEMs, composed of multiple principal elements in nearly equal concentrations, are distinct from traditional 2D materials (typically containing two or three elements) and conventional alloys (dominated by a single primary element with minor secondary additions). Their unique structural and compositional features enable significant lattice strain accommodation, resulting in enhanced electrode performance and potential applications in catalysis, hydrogen storage, sensing, quantum information technologies, and flexible electronics. The research focused on addressing four fundamental questions: (i) What combinations of elements can form stable and synthesizable 2D-HEMs? (ii) What mechanisms drive the stability and synthesizability of crystalline single-phase 2D-HEMs? (iii) How do local chemical disorder and defects influence the macroscopic electronic and mechanical properties? and (iv) What charge storage mechanisms are active in selectively synthesized 2D-HEMs for battery and supercapacitor electrode applications? To achieve these goals, the project employed an integrated theory-experiment approach, incorporating high-throughput first-principles calculations, theoretical modeling, data mining, experimental synthesis, and advanced characterization techniques. The advanced computing resources and state-of-the-art experimental characterization facilities at Oak Ridge National Laboratory (ORNL) were leveraged through collaboration. Beyond scientific advancements, the project contributed to workforce development. Two postdoctoral researchers and three graduate students at the University of Maine were trained through co-advising by ORNL scientists and collaborative interactions, strengthening their expertise in cutting-edge materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine Learning of Plasma Science for Next Generation Microelectronics (Project Final Report)

Low temperature plasmas (LTPs) are an enabling technology behind reducing device dimensions and the continuation of Moore’s Law. It is estimated that 40-45% of all process steps necessary to manufacture semiconductor devices involve LTPs. However, challenges in plasma process design and continuous incorporation of novel materials for new device architectures are pushing the limits of what is possible with current plasma technology. For example, creating higher aspect ratio structures and etching features at the atomic scale both require finer control of the ion energy/velocity at wafer surfaces. To support these types of future innovations in the plasma processing systems that Sandia and the DOE rely upon, we have developed novel diagnostics, simulations, and machine learning capabilities to discover, characterize, and predict plasma phenomena affecting the ion energy/velocity distribution function (IEDF). These efforts also supported research program development and external collaboration with industry and academia through Sandia’s Plasma Research Facility (PRF). This report will focus on the following topics and accomplishments of this three year LDRD project, briefly summarized.

42 ENGINEERING↗
DOE OSTI2026

Singlet Fission

This report summarizes key findings from DOE Award DE‑SC0007004 – Singlet Fission, documenting more than a decade of research led by Professor Josef Michl. The project advanced foundational understanding of singlet fission (SF) through integrated synthetic, spectroscopic, crystallographic, and computational studies. Across a diverse set of chromophores—including tetracenes, cibalackrot derivatives, BODIPY dimers, TDPPs, and fluorinated isobenzofurans—the team identified structural and packing features that enable or inhibit SF, clarified the roles of charge‑transfer intermediates and excimer formation, and developed simplified theoretical models and computational screening tools for predicting optimal geometries and excited‑state-energetics. The work significantly broadened design principles for SF‑active materials and provided insights relevant to next‑generation solar energy conversion and molecular photophysics.

14 SOLAR ENERGY↗