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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probing electronic and dielectric properties of ultrathin Ga 2 O 3 /Al 2 O 3 atomic layer stacks made with in vacuo atomic layer deposition

Ultrathin (1–4 nm) films of wide-bandgap semiconductors are important to many applications in microelectronics, and the film properties can be sensitively affected by defects especially at the substrate/film interface. Motivated by this, an in vacuo atomic layer deposition (ALD) was developed for the synthesis of ultrathin films of Ga 2 O 3 /Al 2 O 3 atomic layer stacks (ALSs) on Al electrodes. It is found that the Ga 2 O 3 /Al 2 O 3 ALS can form an interface with the Al electrode with negligible interfacial defects under the optimal ALD condition whether the starting atomic layer is Ga 2 O 3 or Al 2 O 3 . Such an interface is the key to achieving an optimal and tunable electronic structure and dielectric properties in Ga 2 O 3 /Al 2 O 3 ALS ultrathin films. In situ scanning tunneling spectroscopy confirms that the electronic structure of Ga 2 O 3 /Al 2 O 3 ALS can have tunable bandgaps (E g ) between ~2.0 eV for 100% Ga 2 O 3 and ~3.4 eV for 100% Al 2 O 3 . With variable ratios of Ga:Al, the measured E g exhibits significant non-linearity, agreeing with the density functional theory simulation, and tunable carrier concentration. Furthermore, the dielectric constant of ultrathin Ga 2 O 3 /Al 2 O 3 ALS capacitors is tunable through the variation in the ratio of the constituent Ga 2 O 3 and Al 2 O 3 atomic layer numbers from 9.83 for 100% Ga 2 O 3 to 8.28 for 100% Al 2 O 3 . The high ε leads to excellent effective oxide thickness ~1.7–2.1 nm for the ultrathin Ga 2 O 3 /Al 2 O 3 ALS, which is comparable to that of high-K dielectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Reduced-order model to approximate response matrices for filter stack spectrometers

We present a reduced-order model to calculate response matrices rapidly for filter stack spectrometers (FSSs). The reduced-order model allows response matrices to be built modularly from a set of pre-computed photon and electron transport and scattering calculations through various filter and detector materials. While these modular response matrices are not appropriate for high-fidelity analysis of experimental data, they encode sufficient physics to be used as a forward model in design optimization studies of FSSs, particularly for machine learning approaches that require sampling and testing a large number of FSS designs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Application of machine learning techniques for fast MeV x-ray spectra unfolding from filter stack spectrometer data

Recovery of MeV x-ray spectra from detector signals is difficult because the response matrix inversion is ill-conditioned and current methods are too slow for high-repetition-rate experiments. In this work, we make use of neural networks to unfold MeV x-ray spectra from measurements obtained with a filter stack spectrometer at rates of near 40 Hz. The neural network was trained on synthetic data and tested on both synthetic and experimental data, the latter obtained in two separate experiments performed at the Omega EP laser facility. We show here that this unfolding method has good performance on synthetic data and that it is a promising option for experimental data of up to 40 MeV. The accuracy on experimental data is verified by using a simple forward model to compare against measured values.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Magnetic correlations and pairing tendencies of the hybrid stacking nickelate superlattice La 7 Ni 5 O 17 (La 3 ⁢Ni 2 ⁢O 7 /La 4 Ni 3 ⁢O 10 ) under pressure

Motivated by the recent rapid progress in high-𝑇 𝑐 nickelate superconductors, we comprehensively study the physical properties of the alternating bilayer trilayer stacking nickelate La 7 ⁢Ni 5 ⁢O 17 . The high-symmetry phase of this material, without the tilting of oxygen octahedra, is not stable at ambient conditions but becomes stable under high pressure, where a small hole pocket 𝛾 0 , composed of the 𝑑 3⁢𝑧 2 −𝑟 2 states in the trilayer sublattice, appears. Here, this pocket was identified in our previous work for trilayer La 4 ⁢Ni 3 ⁢O 10 as important to develop superconductivity. Moreover, using random-phase approximation calculations, we find a leading 𝑠 ± pairing state for the high-symmetry phase under pressure with similar pairing strength as that obtained previously for the bilayer La 3⁢ Ni 2 ⁢O 7 compound, suggesting a similar or higher superconducting transition temperature 𝑇 𝑐 , at the random-phase approximation level. In addition, we find that the dominant magnetic fluctuations in the system driving this pairing state have antiferromagnetic structure both in-plane and between the planes of the top and bottom trilayer and bilayer sublattices, while the middle trilayer is magnetically decoupled.

Zhang, Yang [Univ. of Tennessee, Knoxville, TN (Un↗
DOE OSTI2024

SIGHT: Stacked Integration of Geospatial Hierarchical Typologies for Inferring Building Characteristics

Building characteristics are often absent in building stock datasets, particularly in regions most vulnerable to climate change and requiring effective disaster management strategies. Traditional machine learning approaches, while widely used to predict building attributes, typically neglect the spatial context of the data, leading to less accurate and reliable outcomes. To address these challenges, this paper introduces a novel algorithm, the Stacked Integration of Geospatial Hierarchical Typologies. This algorithm adapts a meta-learning framework to incorporate geospatial context into the predictive modeling process. We demonstrate the utility of the algorithm through two primary use cases: building use type classification and building height prediction. The algorithm consistently achieved or exceeded a 0.94 macro average F1 score across five geographically distinct countries for building use type classification. For building height prediction, it accurately predicted heights with a root mean square error of 3.01 in a comprehensive study using roughly 3.6 million buildings in Japan. These results underscore the benefits of integrating spatial hierarchies into machine learning models, enhancing both predictive accuracy and reliability in geospatial modeling. This work introduces a new algorithm to address the pervasive data sparsity issue in existing building stock datasets.

Adams, Daniel [ORNL] (ORCID:0000000196950577)↗
DOE OSTI2024

Stacked-Die SiC Half-Bridge Module with Minimum Loop Inductance

Minimizing parasitic inductance of power modules is needed to advance their electrical performance. Innovation in the past decade has driven down the inductance of SiC half-bridge power modules to around 1–2 nH by using multilayer, embedded, and hybrid structures. Further reduction becomes difficult, mainly limited by the excessive interconnects required for the planar placement of vertical conducting chips. To address this, a vertically stacked-die approach is proposed in this paper, taking advantage of the vertical conducting nature of the SiC chips. With ceramic decoupling capacitors integrated, 0.48 nH overall loop inductance is achieved and validated by experimental measurement. This paper also discusses potential approaches to further reduce the inductance.

Ribeiro, Pedro↗
DOE OSTI2025

Towards Scalable 3D Integration of 2T-nC FeRAM with Hundreds of Layer Stacking

In this article, we study the limits of the number of capacitors and read history dependence in a 2T-nC ferroelectric random-access memory (FeRAM) cell, paving the way for its high-density integration toward hundreds of stacked layers. Through a comprehensive experimental and simulation study on the scaling behavior of the 2T-nC FeRAM architecture, we demonstrate: (i) successful fabrication of 2T-64C cells with robust memory operation and clearly distinguishable ‘0’ and ‘1’ states, even in 64- capacitor configurations; (ii) that the parasitic capacitance of the floating node originates predominantly from the linear component of the ferroelectric capacitor, and its impact on n-scaling—due to degraded sense margin—can be mitigated by floating unselected capacitors with enough TΩ isolation; (iii) that sharing write and read transistors among n capacitors introduces a read history dependence issue due to fluctuating floating node voltage (VFN); and (iv) that a proposed FN discharge scheme can effectively eliminate read-sequence dependence, at the cost of reduced read endurance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optimizing Grid-interactive Efficient Building Designs with Stacked Value Streams

Grid-interactive efficient buildings (GEBs) are those characterized by the combination of energy efficiency and demand flexibility with smart technologies and communications to not only deliver greater affordability and comfort to buildings, but also help utilities manage grid operations and lower system costs. This paper presents an innovative techno-economic assessment framework to effectively examine different GEB design options, explore various use cases, define technically achievable benefits, and thereby assist in informed decision-making. In particular, building load flexibility, thermal storage, and battery energy storage are considered. Advanced optimal dispatch problem is formulated to maximize the stacked value streams from multiple, competing use cases, subject to the physical capabilities and operational flexibility associated with different designs and configurations. Comprehensive case studies were performed for a real-world building to evaluate the cost-effectiveness of different GEB designs and offer in-depth insights. It was found that the proposed assessment method could effectively capture the costs and benefits linked to each GEB design option. Furthermore, the study revealed that outage mitigation and demand response are the two most significant sources of benefits for GEBs.

Ma, Xu↗
DOE OSTI2024

Passivation Mechanisms in Locally Etched P-Type Poly-Si on Silicon Nitride/Silicon Oxide Stack

Tunneling oxide passivated contacts are quickly becoming the industry standard for high-efficiency c-Si based photovoltaic cells. Further development of these structures is essential to enable higher efficiencies and better reliability of cells. By utilizing poly-Si/SixNy/SiOx stacks, very high efficiencies have been demonstrated on small area cells. In this work we investigate the cause of the excellent passivation seen by these structures and show that the primary reason of the excellent passivation seen is the blocking B diffusion to the SiOx/c -Si interface. We also show that the interface between the silicon nitride and polysilicon affects B diffusion through to the c-Si interface. Finally, we demonstrate that the composition of the nitride used is of great importance, and that an incorrect nitride composition leads to B diffusion, and is directly correlated to poor passivation performance.

industries↗
DOE OSTI2025

Assessing VQLS for Fluid Dynamics on a Hybrid Quantum-HPC Stack

Recent advances in quantum linear solvers offer a promising direction for accelerating extreme scientific computations such as fluid dynamics. However, the deep and complex circuits required by many quantum algorithms limit their practical use on current quantum hardware. The Variational Quantum Linear Solver (VQLS) presents a viable alternative for near-term quantum devices (NISQ), and initial efforts have explored its application to select fluid dynamics problems. In this work, we evaluate the use of VQLS for canonical fluid dynamics problems, aiming to identify pathways for generalizing its implementation across a broader class of systems. We analyze the impact of various circuit ansatz and classical optimizers on solution quality and convergence behavior. Furthermore, we assess the algorithm's feasibility within a hybrid quantum–high-performance computing (HPC) framework by porting it to QFw, a state-of-the-art quantum-HPC software stack. 11This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US DOE under Contract No. DE-AC05-00OR22725.

Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗
DOE OSTI2025

Crack Identification and Characterization in Deformed Nb3Sn Rutherford Cable Stacks Using Machine Learning

An investigation of instance segmentation of cracks in Nb3Sn 4-stack 40-strand Rutherford cables using machine learning is presented. Three samples were uniaxially and biaxially loaded before metallographic inspections were performed. The Mask R-CNN model was used in the Detectron2 framework with pre-trained weights but fine-tuned to detect and segment cracks. The model detected cracks with bounding box and mask average precisions (AP) of 42.8 and 27.9, respectively, and was used for instance segmentation of all cracks in the three samples. More cracks were found in the sample pre-loaded along the z-axis (i.e., along the cable length). Pre-loading along the x-axis (i.e., on the cables edges) reduced the number of cracks and changed the crack orientation distribution, away from being highly aligned with the y-axis (i.e., normal to the cables broad faces), i.e., the direction with the highest applied load. Fine-tuning of the Segment Anything Model (SAM) was also studied but performed poorly without human-provided prompts. However, the zero-shot capability of SAM showed high promises to accelerate the image annotation process for applications beyond this study.

Croteau, Jean-Francois↗
DOE OSTI2025

Scaling Uintah on the Aurora Exascale System up to 122,880 Intel Ponte Vecchio Xe Stacks

The challenge of being able to scale application codes based on the Asynchronous Many-Task (AMT) Uintah framework on the Department of Energy (DOE) Aurora exascale system is addressed in this work by considering a challenging Reverse Monte Carlo Ray Tracing radiation benchmark calculation. This benchmark involves potentially global all-to-all communication and uses adaptive mesh refinement and ray tracing to achieve scalability. This benchmark has been used as part of previous scalability studies on a number of pre-exascale systems and on the DOE Frontier exascale system. This paper describes steps taken to enable this benchmark to run successfully on up to 10,240 nodes and 122,880 Intel® Ponte Vecchio Xe stacks on the DOE Aurora exascale system. This scalability was achieved through a limited number of experiments on Aurora, given machine loads and its uniqueness. These experiments constitute valuable lessons learned to achieve scalability at this level. The resulting scalability runs, while few in number, demonstrate relatively good strong-scaling characteristics. A detailed analysis of these results provides important indications about the path to scalability on Aurora for future work. Overall, these results continue the remarkable ability of this AMT approach to produce scalable solutions for challenging problems at extreme scale on heterogeneous architectures.

Garcia, Marta [Argonne National Laboratory (ANL)]↗
DOE OSTI2024

WETO Stack [SWR-24-81]

The WETO Stack software is a collection of data, data analysis scripts, and website content produced through the Holistic Modeling Project’s Portfolio Coordination task. The purpose of the software is to characterize the collection of wind energy software projects supported by the U.S. Department of Energy’s Wind Energy Technologies Office. The included data contains characteristics of the actively funded software projects, and the data analysis scripts provide insights into the breadth, depth, and maturity of the collection of software projects. The website content presents the results of the analyses as well as reports from workshops and other events. It can be viewed at https://nrel.github.io/WETOStack/index.html.

Mudafort, Rafael↗
DOE OSTI2024

Evaluation of Significance Tests on Stack Sample Data

In October 2023, external assessors visiting LANL’s Rad-NESHAP team reviewed the annual source term documentation package for stack emissions. This document discusses how the team uses significance tests to determine whether or not a measurement represents an actual detection of emitted radioactive material. The significance tests used in the evaluation compare the measured sample result to the Minimum Detectable Activity (MDA) for the sample; to the uncertainty of that measurement; and to the mean blank sample for the same time period. The blank test uses the average blank plus 2-standard deviations (2σ) of the blank results as a comparison threshold for this final evaluation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Comparative Analysis of DNA LLM Classification Techniques Using Intra-Layer Feature Extraction with Autoencoder Stacks [Poster]

This project conducts a comparative analysis of DNA LLM classification techniques using Evo2, Grover, and UTRML, focusing on intra-layer feature extraction in Evo2. By extracting features from multiple layers of Evo2 and integrating them into an autoencoder stack with a binary classification head, we evaluate its effectiveness in classifying genomic sequences compared to smaller DNA language models. My findings demonstrate that Evo2 outperforms Grover and UTRML in classification accuracy on a dataset provided by department 08625, CAO2021, while UTRML offers competitive performance with lower computational costs. This study highlights the potential of advanced embedding techniques in enhancing genomic data analysis and informs future research in bioinformatics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

NREL's 1MW Water Electrolysis Stack Performance Validation to Pilot-Scale Renewable Natural Gas Production [Slides]

NREL has designed, built, and operates a 1MW water electrolyzer balance-of-plant to support industrial partners and the U.S. Department of Energy in developing next-generation PEM stacks to reduce the cost of hydrogen production. With that hydrogen, we are developing, innovating and de-risking a biomethanation process capable of megawatt-scale deployment that upgrades biogas waste streams to produce pipeline quality renewable natural gas (RNG). Biomethanation is a two-step process using a methanogenic microorganism to convert renewable hydrogen (H 2 ) and waste carbon dioxide (CO 2 ) to renewable methane (CH 4 ) - the primary component in natural gas. Using biogenic CO 2 from biogas sources like dairies, wastewater treatment plants, and landfills allows production of this drop-in direct replacement fuel. Research projects and future R&D topics are also discussed during the presentation.

08 HYDROGEN↗