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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Unraveling the impact of initial choices and in-loop interventions on learning dynamics in autonomous scanning probe microscopy

The current focus in Autonomous Experimentation (AE) is on developing robust workflows to conduct the AE effectively. This entails the need for well-defined approaches to guide the AE process, including strategies for hyperparameter tuning and high-level human interventions within the workflow loop. This paper presents a comprehensive analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Kernel Learning (DKL) within the realm of AE in scanning probe microscopy. We explore the concept of the “seed effect,” where the initial experiment setup has a substantial impact on the subsequent learning trajectory. Additionally, we introduce an approach of the seed point interventions in AE allowing the operator to influence the exploration process. Using a dataset from Piezoresponse Force Microscopy on PbTiO 3 thin films, we illustrate the impact of the “seed effect” and in-loop seed interventions on the effectiveness of DKL in predicting material properties. The study highlights the importance of initial choices and adaptive interventions in optimizing learning rates and enhancing the efficiency of automated material characterization. This work offers valuable insights into designing more robust and effective AE workflows in microscopy with potential applications across various characterization techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Scenario Generation for Built Environment Decision Support under Uncertainty: Case Studies of Airflow Modeling and Climate-Resilient Infrastructure System Design

When confronted with unforeseen challenges, practicing informed decision making is crucial for enhancing resilience in the built environment. While scan-to-building information modeling (BIM) is a well-established approach for creating detailed digital representations of physical assets, its application in assessing and improving infrastructure resilience remains underexplored. This study addresses this gap by proposing a novel application of scan-to-BIM, namely, scan-to-BIM-to-digital twin (S-BIM-DT) workflow. By integrating reality capture and digital twin technologies, this workflow creates continuously updated and accurate digital representations of physical assets, enabling the generation of various scenarios. Unlike traditional methods, the S BIM-DT workflow facilitates continuous model refinement, supporting informed resilience strategies. By combining these technologies into a cohesive process, the workflow facilitates decision making under uncertainty, enabling stakeholders to evaluate and respond to various scenarios effectively. We demonstrate the implementation of the S-BIM-DT workflow through two use cases that highlight its capability to enhance resilience at different scales. The first use case involves the Combined Transportation, Emergency, and Communications Center (CTECC) in Austin, Texas. BIM-enriched computational fluid dynamics (CFD) modeling simulates airflow and develops alternative scenarios for optimizing the heating, ventilation, and air conditioning (HVAC) systems. This approach enhances resilience against airborne health threats in a postCOVID context. The second use case focuses on designated areas within Beaumont, Texas, as part of the Southeast Texas Urban Integrated Field Laboratory (SETx-UIFL) research. By developing inundation maps to assess extreme weather events, this modeling aids in preparedness efforts and informs the development of climate-resilient infrastructure in vulnerable neighborhoods. Results indicate that the S-BIM-DT workflow effectively generates scenarios that enhance resilience in the built environment by facilitating informed decision making. Furthermore, this study serves as a bridge between advanced scan-to-BIM methodologies and the practical strategies needed to improve built infrastructure resilience.

Built environment↗
DOE OSTI2023

Nanosecond Laser Drying in Wafer Foundries for Significant Energy Efficiency Improvement

Surface drying is a critical process in microelectronics wafer fabrication. In order to reduce and eliminate watermarks, many surface drying processes have been studied and used. Most of the wafer foundries currently use drying processes based on Isopropyl Alcohol (IPA), which causes serious safety (fire), health, environmental, and energy efficiency issues. The long-term goal of this project is to develop a fast, effective, chemical-free, and extremely energy efficient wafer drying technology that employs nanosecond laser-induced sub-surface evaporation and explosion. The objectives of this project are to 1) experimentally develop and verify the laser surface drying method, explore and identify how and to what extent various physical parameters influence the drying efficiency and effectiveness; and 2) understand and optimize the underlying thermodynamics in the proposed laser surface drying, including micro/nanoscale sub-surface superheating, evaporation, explosion, and photo/thermochemical bond breaking via atomistic modeling and experimental characterization. Toward these objectives, five tasks have been accomplished successfully, including 1) develop in-lab laser surface drying setup and study the effect of varied laser parameters, 2) use multiscale hybrid-modeling to study and understand the physics of water behavior during drying toward process optimization, 3) investigate the effect of drying conditions on the drying effectiveness, 4) develop a large-scale laser drying scanning system with automatic scanning, and 5) conduct large-scale simulation to study the effect of dopant level and develop strategies for system development toward industry deployment.

42 ENGINEERING↗
DOE OSTI2023

Super-resolution computational saturated absorption microscopy

Imaging beyond the diffraction limit barrier has attracted wide attention due to the ability to resolve previously hidden image features. Of the various super-resolution microscopy techniques available, a particularly simple method called saturated excitation microscopy (SAX) requires only simple modification of a laser scanning microscope: The illumination beam power is sinusoidally modulated and driven into saturation. SAX images are extracted from the harmonics of the modulation frequency and exhibit improved spatial resolution. Unfortunately, this elegant strategy is hindered by the incursion of shot noise that prevents high-resolution imaging in many realistic scenarios. Here, we demonstrate a technique for super-resolution imaging that we call computational saturated absorption (CSA) in which a joint deconvolution is applied to a set of images with diversity in spatial frequency support among the point spread functions (PSFs) used in the image formation with saturated laser scanning fluorescence microscopy. CSA microscopy allows access to the high spatial frequency diversity in a set of saturated effective PSFs, while avoiding image degradation from shot noise.

Murray, Gabe (ORCID:0000000295655485)↗
DOE OSTI2026

pyFLANK, a graph neural network based null distribution inference model for F ST outlier detection

Detecting genomic regions under selection is essential for understanding how populations adapt to different environments, yet it remains challenging due to the confounding effects of demographic history and linkage disequilibrium (LD). Fixation index (F ST ) is a widely used statistic to identify genomic regions under adaptation. However, identifying genes under selection by defining F ST outliers often remains challenging, owing to confounding effects of underlying demographic history. Traditional methods assume independence among loci and rely on simple demographic models, while newer models perform much better but are computationally expensive and not easily scalable. Here, we present pyFLANK, an open-source and automated Python implementation which detects F ST outliers using a null distribution inferred from quasi-independent loci. Our tool integrates three approaches to identify loci obeying a null distribution: graph neural network (GNN) inference, linkage disequilibrium (LD)-based inference, and user-defined input. Because pyFLANK uses GNN-based inference of quasi-independent loci, it yields a more accurate null model with less need for user parameter input. In simulation experiments, pyFLANK achieved lower false positive rates than current methods while maintaining comparable detection power, indicating that its refined null model better distinguishes true adaptive loci from background variation. The GNN-based model, in particular, detected additional loci associated with phenotypic variance that were not identified by existing methods. Assessments of simulation and real data from different species demonstrate that pyFLANK achieves lower false positive rates compared with other commonly used F ST outlier detectors, while maintaining comparable detection power and excellent computational performance, providing a robust and user-friendly tool for identifying loci under divergent selection. It extends existing F ST outlier frameworks by incorporating explicit LD-aware strategies for null model calibration. The method is intended as a practical and scalable complement to existing genome scan approaches.

FST↗
DOE OSTI2023

A Platform for Atomic Fabrication and In Situ Synthesis in a Scanning Transmission Electron Microscope

Here, the engineering of quantum materials requires the development of tools able to address various synthesis and characterization challenges. These include the establishment and refinement of growth methods, material manipulation, and defect engineering. Atomic-scale modification will be a key enabling factor for engineering quantum materials where desired phenomena are critically determined by atomic structures. Successful use of scanning transmission electron microscopes (STEMs) for atomic scale material manipulation has opened the door for a transformed view of what can be accomplished using electron-beam-based strategies. However, serious obstacles exist on the pathway from possibility to practical reality. One such obstacle is the in situ delivery of atomized material in the STEM to the region of interest for further fabrication processes. Here, progress on this front is presented with a view toward performing synthesis (deposition and growth) processes in a scanning transmission electron microscope in combination with top-down control over the reaction region. An in situ thermal deposition platform is presented, tested, and deposition and growth processes are demonstrated. In particular, it is shown that isolated Sn atoms can be evaporated from a filament and caught on the nearby sample, demonstrating atomized material delivery. This platform is envisioned to facilitate real-time atomic resolution imaging of growth processes and open new pathways toward atomic fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Plasma GFAP for populational enrichment of clinical trials in preclinical Alzheimer's disease

Abstract INTRODUCTION Cognitively unimpaired (CU) amyloid beta (Aβ)+ individuals with elevated plasma glial fibrillary acidic protein (GFAP) have an increased risk of Alzheimer's disease (AD)‐related progression. We tested the utility of plasma GFAP for population enrichment CU populations in clinical trials. METHODS We estimated longitudinal progression, effect size, and costs of hypothetical clinical trials designed to test an estimated 25% drug effect on reducing tau positron emission tomography (PET) accumulation in the medial temporal lobe (MTL) and temporal neocortical region (NEO‐T). RESULTS CU GFAP+/Aβ+ individuals present an increased annual rate of change and effect size in tau PET MTL and tau PET NEO‐T compared to the other groups. An enrichment strategy selecting CU GFAP+/Aβ+ individuals would require a smaller sample size (≈ 57% reduction) and fewer Aβ PET scans (≈ 74% reduction) than trials enriched with Aβ PET alone, reducing total clinical trial costs by up to 64%. DISCUSSION Our results suggest that clinical trials focusing on preclinical AD recruiting Aβ+ individuals with elevated GFAP levels would improve cost effectiveness. Highlights Cognitively unimpaired (CU) glial fibrillary acidic protein (GFAP)+/amyloid beta (Aβ)+ shows increased changes in tau positron emission tomography (PET) . CU GFAP+/Aβ+ enriched clinical trials require a reduced sample size compared to Aβ+ only. CU GFAP+/Aβ+ enrichment reduces Aβ PET scans required and costs. CU GFAP+/Aβ+ enrichment allows the selection of individuals at early stages of the Alzheimer's disease continuum.

Neurosciences & Neurology↗
DOE OSTI2024

Atomic-scale manipulation of buried graphene–silicon carbide interface by local electric field

Abstract Precision of scanning tunneling microscopy (STM) enables control of matter at scales of single atoms. However, transition from atomic-scale manipulation strategies to practical devices encounters fundamental problems in protection of the designer structures formed atop the surface. In this context, STM manipulation of subsurface structures on technologically relevant materials is encouraging. Here, we propose a material platform and protocols for precise manipulation of a buried graphene interface. We show that an electric field from the STM tip reversibly controls breaking and restoring of covalent bonds between the graphene buffer layer and the SiC substrate. The process involves charge redistribution at the atomically sharp interface plane under the epitaxial graphene layer(s). This buried manipulation platform is laterally defined by unit cells from the corresponding (6×6) SiC moiré lattice of the epitaxial graphene. Local and reversible electric-field-induced patterning of graphene heterostructures from the bottom interface creates an alternative architecture concept for their applications.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Molecular Catalyst Synthesis Strategies to Prepare Atomically Dispersed Fe-N-C Heterogeneous Catalysts

We report a strategy to integrate atomically dispersed iron within a heterogeneous nitrogen-doped carbon (N-C) support, inspired by routes for metalation of molecular macrocyclic iron complexes. The N-C support, derived from pyrolysis of a ZIF-8 metal–organic framework, is metalated via solution-phase reaction with FeCl 2 and tributyl amine, as a Brønsted base, at 150 °C. Fe active sites are characterized by 57 Fe Mössbauer spectroscopy and aberration-corrected scanning transmission electron microscopy. The site density can be increased by selective removal of Zn 2+ ions from the N-C support prior to metalation, resembling the transmetalation strategy commonly employed for the preparation of molecular Fe-macrocycles. As a result, the utility of this approach is validated by the higher catalytic rates (per total Fe) of these materials relative to established Fe-N-C catalysts, benchmarked using an aerobic oxidation reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Engineering Structural Transitions in a Multilevel Molecular Switch via Intermolecular Coupling

Controlling molecular conformations with atomic precision is essential for advancing molecular functional electronics, as well as our understanding of molecular dynamics. While switching between bistable molecular conformers is common in nature, creating systems with multiple, addressable states remains synthetically challenging. Here, we demonstrate a bottom-up strategy in which intermolecular interactions give rise to multilevel functionality within a simple two-molecule assembly. Using low-temperature scanning tunneling microscopy, we show that a pyrrolidine dimer on Cu(100) exhibits six distinct adsorption conformations, exceeding the four expected from two independent bistable units. This unusual complexity arises from the interplay between intermolecular van der Waals attraction and steric repulsion, which reshapes the potential energy landscape and changes a single high-energy transition into a sequential two-step pathway. Each step is driven by low-energy inelastic electron excitations, achieving a switching efficiency an order of magnitude higher than that of the monomer. Here, by tuning the bias voltage and tip–molecule distance, we achieve deterministic control over multiple stable states, establishing a general design principle for on-demand engineering of collective molecular behavior and energy-efficient multilevel molecular devices.

Molecular interactions↗
DOE OSTI2026

Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography

Diffusion models have emerged as powerful priors for solving inverse problems in computed tomography (CT). In certain applications, such as neutron CT, it can be expensive to collect large amounts of measurements even for a single scan leading to sparse data sets from which it is challenging to obtain high quality reconstructions even with diffusion models. One strategy to mitigate this challenge is to leverage a complementary, easily available imaging modality; however, such approaches typically require retraining the diffusion model with large datasets. In this work, we propose incorporating an additional modality without retraining the diffusion prior, enabling accelerated imaging of costly modalities. We further examine the impact of imperfect side modalities on cross-modal guidance. Our method is evaluated on sparse-view neutron computed tomography, where reconstruction quality is substantially improved by incorporating X-ray computed tomography of the same samples.

Efimov, Timofey [ORNL] (ORCID:000900090098471X)↗
DOE OSTI2020

Transient Studies of Nonequilibrium Transport in Two-Dimensional Semiconductors

The overarching objective of research under DOE support has been to investigate emergent carrier phenomena in different two-dimensional (2D) systems, under conditions where they are driven far from equilibrium. The description of nonequilibrium transport has long represented one of the most enduring challenges in condensed-matter physics, in large part due to the need to invoke the role of complex many-body phenomena (electron-phonon and electron-electron coupling) in this regime. In this project, we have explored aspects of this problem in a variety of different two-dimensional systems. These include the modulation-doped two-dimensional electrons gas, or 2DEG, in GaAs/AlGaAs heterostructures; the electron and hole systems in graphene and its related hetero-structures, and; the strongly-confined electron layers in transition-metal dichalcogenide (MoS 2 and WS 2 ) and trichalcogenide (TiS 3 ) materials. Significant understanding has been achieved in a number of different areas, as we elaborate upon in subsequent sections of this proposal: Nanoscale spectroscopy of defect-related barriers in MoS2 Negative differential conductance due to inter-valley scattering in WS 2 under high fields Incipient localization due to scattering from frozen-phonon disorder in quantum point contacts High-field transient transport in graphene/hexagonal BN heterostructures Universal voltage scaling of quantum corrections in graphene under nonequilibrium A defining feature of much of this research has been an approach to problem solving in which novel experimental techniques (such as transient electrical pulsing and scanning-probe microscopy) are combined with the development (in collaboration with colleagues from condensed-matter theory) of sophisticated models, a strategy that we propose to continue in our future research. In recent years, we have developed an increasing capacity for handling atomically-thin materials, and their van der Waals systems, a capacity that is reflected in the balance of topics listed above.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Small Angle X-Ray Scattering and Polymers

Polymers are some of the most versatile and useful materials on this planet. Properties of polymers are a result of their processing history, chemical makeup, and physical structure. Therefore, characterization is necessary to develop new polymer formulations and processes. Many established techniques for polymer characterization are destructive, and alternative methods that don’t destroy samples are in high demand. Thermal analysis techniques such as differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) yield important characteristic information for polymer samples while destroying the sample. Non-destructive strategies such as X ray diffraction (XRD) or small angle X-ray scattering (SAXS) can also find polymer characteristics while preserving a sample’s attributes. Sample preservation is desirable when investigating novel formulation and processing methods. SAXS analysis will be done on two different polymers: polyethylene, and a triblock copolymer. Using these two polymers allows for confidence between tests as polyethylene is very common, and well understood. Polyethylene analysis will be done first, and when methods have been refined so that the SAXS data matches accepted thermal analysis values for HDPE, there will be confidence to use the same analysis on block copolymers. Block copolymers remain relatively unexplored, so SAXS data will yield valuable information on their characteristics. Understanding the mechanical properties of these polymers is paramount to their application in upcycling, a process by which waste plastic can be converted into a higher value commodity. For this project, the upcycling process will be done via 3-dimensional (3d) printing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

On a Method For Reconstructing Computed Tomography Datasets from an Unstable Source

As work continues in neutron computed tomography, at Los Alamos Neutron Science Center (LANSCE) and other locations, source reliability over the long imaging times is an issue of increasing importance. Moreover, given the time commitment involved in a single neutron image, it is impractical to simply discard a scan and restart in the event of beam instability. In order to mitigate the cost and time associated with these options, strategies are presented in the current work to produce a successful reconstruction of computed tomography data from an unstable source. The present work uses a high energy neutron tomography dataset from a simulated munition collected at LANSCE to demonstrate the method, which is general enough to be of use in conjunction with unstable X-ray computed tomography sources as well.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling

We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled in the spatial domains, with ~30% of original data set sufficient for high-quality reconstruction, potentially enabling faster BE imaging. At the same time, reliable reconstruction along the frequency domain requires the resonance peak to be within the measured band. This behavior suggests the optimal strategy for the BE imaging on unknown samples. Finally, we discuss how GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Autonomous fabrication of tailored defect structures in 2D materials using machine learning-enabled scanning transmission electron microscopy

Materials with tailored quantum properties can be engineered from atomic-scale assembly techniques, but existing methods often lack the agility and accuracy to precisely and intelligently control the manufacturing process. Here, we demonstrate a fully autonomous approach for fabricating atomic-level defects using electron beams in scanning transmission electron microscopy (STEM) that combines advanced machine learning and automated beam control. As a proof of concept, we achieved controlled fabrication of MoS-nanowire (MoS-NW) edge structures by iterative and targeted exposure of MoS 2 monolayer to a focused electron beam to selectively eject sulfur atoms, utilizing high-angle annular dark-field (HAADF) imaging for feedback-controlled monitoring of structural evolution of defects. A machine learning framework combining a random forest model and a convolutional neural network (CNN) was developed to decode the HAADF image and accurately identify atomic positions and species. This atomic-level information was then integrated into an autonomous decision-making platform, which applied predefined fabrication strategies to instruct beam control about atomic sites to be ejected. The selected sites were subsequently exposed to a localized electron beam using an FPGA-controlled scan routine with precise control over beam positioning and duration. While the MoS-NW edge structures produced exhibit promising mechanical and electronic properties, the proposed methods to build the autonomous fabrication framework is material-agnostic and can be extended to other 2D materials for the creation of diverse defect structures and heterostructures beyond Mo S2 .

Engineering↗
DOE OSTI2020

Simultaneously Integrating Single Atomic Cobalt Sites and Co 9 S 8 Nanoparticles into Hollow Carbon Nanotubes as Trifunctional Electrocatalysts for Zn–Air Batteries to Drive Water Splitting

The development of rechargeable metal–air batteries and water electrolyzers are highly constrained by electrocatalysts for the oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER). However, the construction of efficient trifunctional electrocatalysts for ORR/OER/HER are highly desirable yet challenging. Herein, hollow carbon nanotubes integrated single cobalt atoms with Co9S8 nanoparticles (CoSA + Co 9 S 8 /HCNT) are fabricated by a straightforward in situ self-sacrificing strategy. The structure of the CoSA + Co 9 S 8 /HCNT are verified by X-ray absorption spectroscopy and aberration-corrected scanning transmission electron microscopy. Theoretical calculations and experimental results embrace the synergistic effects between Co 9 S 8 nanoparticles and single cobalt atoms through optimizing the electronic configuration of the CoN4 active sites to lower the reaction barrier and facilitating the ORR, OER, and HER simultaneously. Consequently, rechargeable liquid and all-solid-state flexible Zn–air batteries based on CoSA + Co 9 S 8 /HCNT exhibit remarkable stability and excellent power density of 177.33 and 51.85 mW cm -2 , respectively, better than Pt/C + RuO 2 counterparts. Moreover, the as-fabricated Zn–air batteries can drive an overall water splitting device assembled with CoSA + Co 9 S 8 /HCNT and achieve a current density of 10 mA cm -2 at a low voltage of 1.59 V, also superior to Pt/C + RuO 2 . Therefore, we conclude, this work presents a promising approach to an efficient trifunctional electrocatalyst toward practical applications.

25 ENERGY STORAGE↗
DOE OSTI2021

Primary Mediastinal B Cell Lymphoma in the Positron-Emission Tomography Era Executive Summary of the American Radium Society Appropriate Use Criteria

Primary mediastinal B cell lymphoma (PMBCL) is a highly curable subtype of non-Hodgkin lymphoma that is diagnosed predominantly in adolescents and young adults. Consequently, long-term treatment-related morbidity is critical to consider when devising treatment strategies that include different chemoimmunotherapy strategies with or without radiation therapy. Furthermore, adaptive approaches using the end-of-chemotherapy (EOC) positron emission tomography (PET)/computed tomography (CT) scanning may help to determine which patients may benefit from additional therapies. We aimed to develop evidence-based guidelines for treating these patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗