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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Applications of ultrafast nano-spectroscopy and nano-imaging with tip-based microscopy

Innovation in microscopy has often been critical in advancing both fundamental science and technological progress. Notably, the evolution of ultrafast near-field optical nano-spectroscopy and nano-imaging has unlocked the ability to image at spatial scales from nanometers to ångströms and temporal scales from nanoseconds to femtoseconds. This approach revealed a plethora of fascinating light-matter states and quantum phenomena, including various species of polaritons, quantum phases, and complex many-body effects. This review focuses on the working principles and state-of-the-art development of ultrafast tip-enhanced and near-field microscopy, integrating diverse optical pump-probe methods across the terahertz (THz) to ultraviolet (UV) spectral ranges. It highlights their utility in examining a broad range of materials, including two-dimensional (2D), organic molecular, and hybrid materials. The review concludes with a spatio-spectral-temporal comparison of ultrafast nano-imaging techniques, both within already well-defined domains, and offering an outlook on future developments of ultrafast tip-based microscopy and their potential to address a wider range of materials.

Zhao, Zhichen [Tongji University, Shanghai (China)↗
DOE OSTI2020

High-resolution optical micro-spectroscopy extending from the near-infrared to the vacuum-ultraviolet

Optical characterization of small samples over a wide spectral range with rapid data acquisition is essential for the analysis of many material systems, such as 2D van der Waals layers and their heterostructures. In this paper, we present the design and implementation of a tabletop micro-spectroscopy system covering the near-infrared to the vacuum-ultraviolet (1.2 eV–6.8 eV or ~1.0 μm to 185 nm) using mostly off-the-shelf components. It can measure highly reproducible local reflectance spectra with a total integration time of a few minutes and a full-width-half-maximum spot size of 2.7 by 5.6 μm. For precise positioning, the design also allows simultaneous monitoring of the measurement location and the wide-field image of the sample. We demonstrate ultra-broadband reflectance spectra of exfoliated thin flakes of several wide-gap 2D materials, including ZnPS 3 , hexagonal BN, and Ca(OH) 2 .

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Investigation of spin and magnetism in two dimensional materials with atomic-scale spatial resolution (Final Technical Report)

Spintronics is an approach to energy-efficient electronics beyond silicon CMOS that utilizes the electron’s spin degree of freedom for memory and logic. The record spin diffusion length in graphene has generated considerable interest in two dimensional (2D) materials (i.e. graphene and beyond) as a platform for spintronics. Magnetism in 2D van der Waals materials may offer additional opportunities for tunability via strain, external fields, and layer-dependent quantum effects. The inherently atomic-scale nature of exchange interactions and the inherently all-surface nature of 2D sheets motivate an integrated approach combining atomic-scale magnetic characterization, epitaxial growth, and nanodevice fabrication. The mission of the research is to develop and utilize atomic-scale imaging of spin and magnetism via spin-polarized scanning tunneling microscopy (STM) in conjunction with atom-by-atom materials growth via molecular beam epitaxy (MBE) and nanoscale device fabrication to explore new frontiers of spintronics and quantum science in 2D materials. The PIs are particularly motivated by understanding how the magnetic and spin-dependent properties at the atomic scale lead to interesting and potentially useful phenomena in spintronic devices at the macroscopic scale.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Above-Room-Temperature Ferromagnetism in Thin van der Waals Flakes of Cobalt-Substituted Fe 5 GeTe 2

Two-dimensional (2D) magnetic van der Waals materials provide a powerful platform for studying the fundamental physics of low-dimensional magnetism, engineering novel magnetic phases, and enabling thin and highly tunable spintronic devices. To realize high-quality and practical devices for such applications, there is a critical need for robust 2D magnets with ordering temperatures above room temperature that can be created via exfoliation. Here, in this paper, the study of exfoliated flakes of cobalt-substituted Fe 5 GeTe 2 (CFGT) exhibiting magnetism above room temperature is reported. Via quantum magnetic imaging with nitrogen-vacancy centers in diamond, ferromagnetism at room temperature was observed in CFGT flakes as thin as 16 nm corresponding to 16 layers. This result expands the portfolio of thin room-temperature 2D magnet flakes exfoliated from robust single crystals that reach a thickness regime relevant to practical spintronic applications. The Curie temperature T c of CFGT ranges from 310 K in the thinnest flake studied to 328 K in the bulk. To investigate the prospect of high-temperature monolayer ferromagnetism, Monte Carlo calculations were performed, which predicted a high value of T c of ~270 K in CFGT monolayers. Pathways toward further enhancing monolayer T c are discussed. These results support CFGT as a promising platform for realizing high-quality room-temperature 2D magnet devices.

2D magnetism↗
DOE OSTI2021

Quantifying the effect of hydride microstructure on zirconium alloys embrittlement using image analysis

Since hydride microstructure can significantly influence Zr alloy nuclear fuel cladding’s ductility, a new metric has been developed to quantify hydride microstructure in 2D micrographs and relate it to crack propagation. As cladding failure usually results from a hoop stress, this new metric, called the Radial Hydride Continuous Path (RHCP), is based on quantifying the continuity of brittle hydride particles along the radial direction of the cladding tube. Compared to previous metrics, this approach more closely relates to the propensity of a crack to propagate radially through the cladding tube thickness. The RHCP takes into account hydride length, orientation, and connectivity to choose the optimal path for crack propagation through the cladding thickness. The RHCP can therefore be more closely linked to hydride embrittlement of the Zr alloy material, thus creating a relationship between material structure, properties, and performance. The new definition, along with previously proposed metrics such as the Radial Hydride Fraction (RHF), the Hydride Continuity Coefficient (HCC), and the Radial Hydride Continuity Factor (RHCF), have been implemented and automated in MATLAB. These codes are made available with this publication as supplementary materials. These metrics were verified by comparing their predictions of hydride morphology against expected values in simple cases, and the implementation of the new metric was validated by comparing its predictions with manual measurements of hydride microstructure performed on ImageJ. The RHCP was also validated against experimental measurements of fracture behavior and it was shown to correlate with cladding failure better than previous metrics. The information provided by these metrics will help accurately assess cladding integrity during operation, transportation, and storage.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantitative and qualitative bone imaging: A review of synchrotron radiation microtomography analysis in bone research

All levels of the unique hierarchical structure of bone, consisting of collagen and hydroxyapatite crystals at the nanoscale to osteon/lamellae structures at the microscale, contribute to its characteristic toughness and material properties. Elements of bone's density and size contribute to bone quantity (or bone mass), whereas elements of bone's material composition, material properties, internal structure, and organization describe bone quality. Furthermore, bone quantity and quality can be degraded by factors such as aging, disease, treatments, and irradiation, compromising its ability to resist fracture and sustain loading. Accessing the morphology and architecture of bone at the microscale to quantify microstructural features and assess the degree of mineralization and path of crack propagation in bone provides crucial information on how these factors are influencing bone quantity and quality. Synchrotron radiation micro-computed tomography (SRμCT) was first used to assess bone structure at the end of the 1990's. One of the main advantages of the technique is that it enables accurate three-dimensional (3D), non-destructive quantification of structure while traditional histomorphometry on histological sections is inherantly destructive to the sample and two-dimensional (2D). Additionally, SRμCT uses monochromatic, high-flux X-ray beams to provide high-resolution and high-contrast imaging of bone samples. This allows the quantification of small microstructural features (e.g. osteocyte lacunae, canals, trabeculae, microcracks) and direct gray value compositional mapping (e.g. mineral quantification, cement lines) with greater speed and fidelity than lab-based micro-computed tomography. In this article, we review how SRμCT has been applied to bone research to elucidate the mechanisms by which bone aging, disease, and other factors affect bone fragility and resistance to fracture.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom Probe Tomography (APT) is a powerful technique for visualizing the atomic-scale distribution of solutes in materials, but quantitative cluster analysis of APT datasets remains a challenge due to the need for subjective parameter selection in clustering algorithms. While distance-based and density-based methods such as HDBSCAN are widely used, their performance is highly sensitive to user-defined parameters, which undermines reproducibility and accuracy. This study proposes an image-based, deep learning-aided workflow for automating parameter selection and cluster detection in APT data analysis. By projecting 3D APT point clouds onto 2D planes, we leverage pretrained convolutional neural networks (ConvNeXt-Tiny and ResNet-50) through transfer learning to predict the number of clusters present in synthetic datasets. The output is used to guide K-means clustering and estimate HDBSCAN parameters, specifically minimum cluster size and minimum sample points. This approach reduces reliance on manual parameter tuning, improving consistency and scalability. The methodology demonstrates the feasibility of using image-based deep learning for interpreting complex spatial patterns in APT data, enabling faster and more objective analysis. The complete workflow and code are made publicly available to support reproducibility and future research.

Density-based clustering↗
DOE OSTI2022

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗
DOE OSTI2022

Distribution of Mn Oxidation States in Grassland Soils and Their Relationships with Soil Pores

Manganese (Mn) is known to be an active contributor to processing and cycling of soil organic carbon (C), yet the exact mechanisms behind its interactions with C are poorly understood. Plant diversity in terrestrial ecosystems drives feedback links between plant C inputs and soil pores, where the latter, in turn, impact the redox environment and Mn. This study examined associations between soil pores (>36 μm Ø) and Mn within intact soils from two grassland ecosystems, after their >6-year implementation in a replicated field experiment. In this work, we used μ-XRF imaging and XANES spectroscopy to explore spatial distribution patterns of Mn oxidation states, combined with X-ray computed microtomography and 2D zymography. A high plant diversity system (restored prairie) increased soil C and modified spatial distribution patterns of soil pores as compared to a single species system (monoculture switchgrass). In switchgrass, the abundance of oxidized and reduced Mn oxidation states varied with distance from pores consistently with anticipated O 2 diffusion, while in the soil from restored prairie, the spatial patterns suggested that biological activity played a greater role in influencing Mn distributions. Based on the findings, we propose a hypothesis that Mn transformations promote C gains in soils of high plant diversity grasslands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Visualizing the impact of quenched disorder on 2D electron Wigner solids

Electron Wigner solids (WSs) provide an ideal system for understanding the competing effects of electron–electron and electron–disorder interactions, a central unsolved problem in condensed matter physics. Progress in this topic has been limited by a lack of single-defect-resolved experimental measurements as well as accurate theoretical tools to enable realistic experiment/theory comparison. Here, in this study, we overcome these limitations by combining atomically resolved scanning tunnelling microscopy (STM) with neural-quantum-state quantum Monte Carlo (NQS-QMC) simulation of disordered 2D electron WSs to discover new disorder-induced physical regimes of correlated electron behaviour. STM was used to image the electron density (n e )-dependent evolution of electron WSs in gate-tunable bilayer MoSe 2 (BL-MoSe 2 ) devices with varying long-range (n LR ) and short-range (n SR ) disorder densities. These images were compared with NQS-QMC simulations using realistic disorder maps extracted from experiment, thus allowing the roles of different disorder types to be disentangled. We identify two distinct physical regimes for disordered electron WSs that depend on n SR . For n SR ≲ n e , the WS behaviour is dominated by long-range disorder and features extensive mixed solid–liquid phases, a new type of local re-entrant melting/crystallization and prominent Friedel oscillations. By contrast, when n SR ≫ n e , these features are suppressed and a more robust amorphous WS phase emerges that persists to higher n e , highlighting the importance of short-range disorder in this regime. Our work establishes a powerful framework for studying disordered quantum solids through a combined experimental–theoretical approach.

Ge, Zhehao [University of California, Berkeley, CA↗
DOE OSTI2026

Evaluation of an event-driven 3FI ASIC for spectroscopic X-ray detection with synchrotron radiation

The novel design and evaluation on the NSLS-II beamline of the 3FI application-specific integrated circuit (ASIC) bump-bonded to a simple, planar, 2D segmented silicon sensor are presented. The ASIC was developed for full-field fluorescence spectral X-ray imaging (3FI). It is a small-scale prototype that features a square array of 32 × 32 pixels, and the size of the pixels is 100 µm × 100 µm. The ASIC was implemented in a 65 nm CMOS integrated circuit fabrication process. Each pixel incorporates a charge-sensitive amplifier, a shaping filter, a discriminator, a peak detector and a sample-and-hold circuit, allowing detection of events and storage of signal amplitudes. The system operates in a frameless event-driven readout mode, outputting analog values for threshold-triggered events, allowing high-speed multi-element X-ray fluorescence data acquisition. The 3FI ASIC achieves per-channel spectrometric performance at a power consumption of only 200 µW per pixel, with nearly all dissipation confined to the analog front-end. An energy resolution is measured at the level of 308 eV full width at half-maximum (FWHM) at 8.04 keV (Cu Kα), and 138 eV FWHM at 3.69 keV (Ca Kα). This per-pixel capability makes the prototype suitable for in situ trace element microanalysis in biological and environmental studies. Moreover, the frameless architecture of the detector is designed to address limitations of conventional X-ray fluorescence microscopy, which typically requires mechanical scanning, by enabling continuous high-throughput data acquisition in future full-field implementations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Complete Strain Mapping of Nanosheets of Tantalum Disulfide

Quasi-two-dimensional (quasi-2D) materials hold promise for future electronics because of their unique band structuresthat result in electronic and mechanical properties sensitive to crystal strains in all three dimensions. Quantifying crystal strain is a prerequisite to correlating it with the performance of the device, and calls for high resolution but spatially resolved rapid characterization methods. Here we show that using fly-scan nano X-ray diffraction we can accomplish a tensile strain sensitivity below 0.001% with a spatial resolution of better than 80 nm over a spatial extent of 100 µm on quasi 2D flakes of 1T-TaS2. Coherent diffraction patterns were collected from a ~100 nm thick sheet of 1T-TaS2 by scanning 12keV focused X-ray beam across and rotating the sample. We demonstrate that the strain distribution around micron and sub-micron sized ‘bubbles’ that are present in the sample may be reconstructed from these images. The experiments use state of the art synchrotron instrumentation, and will allow rapid and non-intrusive strain mapping of thin film samples and electronic devices based on quasi 2D materials.

nano X-ray diffraction↗
DOE OSTI2020

3D fault architecture controls the dynamism of earthquake swarms

The vibrant evolutionary patterns made by earthquake swarms are incompatible with standard, effectively two-dimensional (2D) models for general fault architecture. Here we leverage advances in earthquake monitoring with a deep-learning algorithm to image a fault zone hosting a 4-year-long swarm in southern California. We infer that fluids are naturally injected into the fault zone from below and diffuse through strike-parallel channels while triggering earthquakes. A permeability barrier initially limits up-dip swarm migration but ultimately is circumvented. This enables fluid migration within a shallower section of the fault with fundamentally different mechanical properties. Our observations provide high-resolution constraints on the processes by which swarms initiate, grow, and arrest. These findings illustrate how swarm evolution is strongly controlled by 3D variations in fault architecture.

58 GEOSCIENCES↗
DOE OSTI2023

Enhancing Crystallization in Hybrid Perovskite Solar Cells Using Thermally Conductive 2D Boron Nitride Nanosheet Additive

Controlling crystallization and grain growth is crucial for realizing highly efficient hybrid perovskite solar cells (PSCs). In this work, enhanced PSC photovoltaic performance and stability by accelerating perovskite crystallization and grain growth via 2D hexagonal boron nitride (hBN) nanosheet additives incorporated into the active perovskite layer are demonstrated. Specifically, in situ X-ray scattering and infrared thermal imaging during the perovskite annealing process revealed the highly thermally conductive hBN nanosheets promoted the phase conversion and grain growth in the perovskite layer by facilitating a more rapid and spatially uniform temperature rise within the perovskite film. Complementary structural, physicochemical, and electrical characterizations further showed that the hBN nanosheets formed a physical barrier at the perovskite grain boundaries and the interfaces with charge transport layers, passivating defects, and retarding ion migration. As a result, the power conversion efficiency of the PSC is improved from 17.4% to 19.8%, along with enhanced device stability, retaining ≈90% of the initial efficiency even after 500 h ambient air storage. Here, the results not only highlight 2D hBN as an effective additive for PSCs but also suggest enhanced thermal transport as one of the pathways for improved PSC performance by 2D material additives in general.

2D materials↗
DOE OSTI2026

Experimental investigation of H-mode pedestal turbulence using a charge exchange imaging diagnostic system at DIII-D

Microtearing modes (MTMs) (ω ~ ω de k θ ρ s ~ 0.1, k r ρ s ~ 0.3 – 1.0) are predicted to cause substantial electron thermal transport in the H-mode pedestal, limiting fusion performance on high-power devices. Localized 2D measurements of low-to-intermediate wavenumber density fluctuations in the edge of DIII-D H-mode plasmas suggest the presence of temporally intermittent or ‘bursting’ MTMs during the inter-ELM cycle in the steep gradient region. These measurements are obtained using a new intermediate radial resolution multichannel Charge eXchange Imaging (CXI) diagnostic that analyzes the intensity of the CVI emission at 529 nm. The mode is peaked near ρ = 0.97 with a radial full-width half maximum of 1.5–2.0 cm and a normalized density fluctuation amplitude peak of $\frac{dn_C}{n_C}$ ~ 5%, peaking near 60 kHz in the lab frame. The mode additionally features a broad spectral width from 30 to 180 kHz and propagates in the electron diamagnetic direction in the plasma frame. CXI measurements taken during discharges with high density and current ramps provide evidence that MTMs become unstable at finite collisionality and remain at nearly the same location in ρ-space despite scanning of rational surface position. This indicates that the location of the peak in the electron diamagnetic frequency is a stronger driving force than individual rational surfaces.

DIII-D↗