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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Nanostructure and compositional segregation of soft magnetic FeNi-based nanocomposites with multiple nanocrystalline phases

Soft magnetic metal amorphous nanocomposite alloys are produced through rapid solidification and thermal annealing yielding nanocrystals embedded within an amorphous precursor. Similar free energies in Co-rich and FeNi-based alloy systems result in multiple nanocrystalline phases being formed during devitrification. Studies of multi-phase crystallization processes have been reported for Co-rich alloys but relatively few have investigated FeNi-based systems. A detailed characterization of compositional partitioning and microstructure of an optimally annealed FeNi-based MANC (Fe 70 Ni 30 ) 80 Nb 4 Si 2 B 14 alloy is presented through complementary high-resolution transmission electron microscopy (HRTEM) and atom probe tomography (APT). HRTEM demonstrates orientation relationships between FCC and BCC nanocrystals, suggesting heterogeneous nucleation of nanocrystals in the amorphous matrix or a cooperative mechanism of nucleation between BCC and FCC nanocrystallites. APT results show evidence for (i) the segregation of Fe and Ni between nanocrystals of different phases, (ii) B partitioning to the amorphous phase, and (iii) an Nb-enriched shell surrounding nanocrystals.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Geometrical-Based Generative Adversarial Network to Enhance Digital Rock Image Quality

X-ray microcomputed tomography (micro-CT) is a common tool for the study of porous media structures and properties. High-quality micro-CT data are required to accurately capture pore structures. Acquiring high-quality micro-CT data, however, is not always possible, owing to application limitations and experimental constraints. Therefore, we propose a geometrical-based generative adversarial network (GAN) to rapidly restore noisy micro-CT images to their clean counterparts. The training data and related ground-truth (GT) data are scanned for 7 min and 9.5 h, respectively. To evaluate the performance of the geometrical-based GAN, a 6003 voxel image that has never been used for training is reconstructed and compared with the corresponding GT image. Histogram matching and linear normalization are implemented to adjust the histogram of the reconstructed image to that of the GT image. A watershed-based segmentation method is then applied to delineate pore and solid phases. Lastly, we measure the Minkowski functionals and petrophysical properties, including absolute permeability, pore size distribution, drainage capillary pressure-saturation curve, and imbibition relative permeability, to estimate the physical accuracy of the denoised image. The results show that the proposed geometrical-based GAN can accurately restore noisy micro-CT data. By reducing the scanning time from 9.5 h to 7 min, the expenditure of collecting micro-CT can be decreased significantly. This is particularly important for applications where time-lapse images of a dynamic process are required, high-throughput imaging is necessary for real-time data analysis, or where the quantification of large sample volumes is required.

58 GEOSCIENCES↗
DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗
DOE OSTI2023

Development of polarization-sensitive optical coherence tomography imaging platform and metrics to quantify electrostimulation-induced peripheral nerve injury in vivo in a small animal model

Neuromodulation devices are rapidly evolving for the treatment of neurological diseases and conditions. Injury from implantation or long-term use without obvious functional losses is often only detectable through terminal histology. New technologies are needed that assess the peripheral nervous system (PNS) under normal and diseased or injured conditions. We aim to demonstrate an imaging and stimulation platform that can elucidate the biological mechanisms and impacts of neurostimulation in the PNS and apply it to the sciatic nerve to extract imaging metrics indicating electrical overstimulation. A sciatic nerve injury model in a 15-rat cohort was observed using a newly developed imaging and stimulation platform that can detect electrical overstimulation effects with polarization-sensitive optical coherence tomography. The sciatic nerve was electrically stimulated using a custom-developed nerve holder with embedded electrodes for 1 h, followed by a 1-h recovery period, delivered at above-threshold Shannon model k-values in experimental groups: sham control (SC, n = 5, 0.0 mA / 0 Hz), stimulation level 1 (SL1, n = 5, 3.4 mA / 50 Hz, and k = 2.57), and stimulation level 2 (SL2, n = 5, 6.8 mA / 100 Hz, and k = 3.17). The stimulation and imaging system successfully captured study data across the cohort. When compared to a SC after a 1-week recovery, the fascicle closest to the stimulation lead showed an average change of +4 % / – 309 % (SL1/SL2) in phase retardation and –79 % / – 148 % in optical attenuation relative to SC. Analysis of immunohistochemistry (IHC) shows a +1 % / – 36 % difference in myelin pixel counts and –13 % / + 29 % difference in axon pixel counts, and an overall increase in cell nuclei pixel count of +20 % / + 35 % . These metrics were consistent with IHC and hematoxylin/eosin tissue section analysis. The poststimulation changes observed in our study are manifestations of nerve injury and repair, specifically degeneration and angiogenesis. Optical imaging metrics quantify these processes and may help evaluate the safety and efficacy of neuromodulation devices.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Probing Quantum Speed Limits with Ultracold Gases

Here, quantum speed limits (QSLs) rule the minimum time for a quantum state to evolve into a distinguishable state in an arbitrary physical process. These fundamental results constrain a notion of distance traveled by the quantum state, known as the Bures angle, in terms of the speed of evolution set by nonadiabatic energy fluctuations. I theoretically propose how to measure QSLs in an ultracold quantum gas confined in a time-dependent harmonic trap. In this highly-dimensional system of continuous variables, quantum tomography is prohibited. Yet, QSLs can be probed whenever the dynamics is self-similar by measuring as a function of time the cloud size of the ultracold gas. This makes it possible to determine the Bures angle and energy fluctuations, as I discuss for various ultracold atomic systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Machine Learning Atom Probe Tomography Tool For Automatic And Fast Clustering

The software uses a YOLO11 segmentation model trained on synthetic data to analyze APT datasets. The workflow operates as follows: 1. Data Slicing: The APT dataset is divided into multiple 2D cross-sections of a specified thickness. 2. Segmentation: The model identifies point-dense regions within each 2D slice. 3. 3D Reconstruction: Detected regions (masks) from all slices are combined and reconstructed back into the original 3D space, forming clusters. The integration with HPC resources enables the software to process large-scale APT datasets efficiently. This combination of automation and scalability reduces manual intervention, improves reproducibility, and accelerates the clustering workflow.

Tang, Yalei [Idaho National Laboratory (INL), Idah↗
DOE OSTI2021

Enabling Particulate Materials Processing Science for High-Consequence, Small-Lot Precision Manufacturing

This Laboratory Directed Research and Development project developed and applied closely coupled experimental and computational tools to investigate powder compaction across multiple length scales. The primary motivation for this work is to provide connections between powder feedstock characteristics, processing conditions, and powder pellet properties in the context of powder-based energetic components manufacturing. We have focused our efforts on multicrystalline cellulose, a molecular crystalline surrogate material that is mechanically similar to several energetic materials of interest, but provides several advantages for fundamental investigations. We report extensive experimental characterization ranging in length scale from nanometers to macroscopic, bulk behavior. Experiments included nanoindentation of well-controlled, micron-scale pillar geometries milled into the surface of individual particles, single-particle crushing experiments, in-situ optical and computed tomography imaging of the compaction of multiple particles in different geometries, and bulk powder compaction. In order to capture the large plastic deformation and fracture of particles in computational models, we have advanced two distinct meshfree Lagrangian simulation techniques: 1.) bonded particle methods, which extend existing discrete element method capabilities in the Sandia-developed , open-source LAMMPS code to capture particle deformation and fracture and 2.) extensions of peridynamics for application to mesoscale powder compaction, including a novel material model that includes plasticity and creep. We have demonstrated both methods for simulations of single-particle crushing as well as mesoscale multi-particle compaction, with favorable comparisons to experimental data. We have used small-scale, mechanical characterization data to inform material models, and in-situ imaging of mesoscale particle structures to provide initial conditions for simulations. Both mesostructure porosity characteristics and overall stress-strain behavior were found to be in good agreement between simulations and experiments. We have thus demonstrated a novel multi-scale, closely coupled experimental and computational approach to the study of powder compaction. This enables a wide range of possible investigations into feedstock-process-structure relationships in powder-based materials, with immediate applications in energetic component manufacturing, as well as other particle-based components and processes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Soil pore network response to freeze-thaw cycles in permafrost aggregates

This dataset contains data used for the paper "Pore network response to freeze-thaw cycles in permafrost aggregates". The Related References field will be updated with a full citation when available.Climate change in Arctic landscapes may increase freeze-thaw frequency within the active layer as well as newly thawed permafrost. A highly disruptive process, freeze-thaw can deform soil pores and alter the architecture of the soil pore network with varied impacts to water transport and retention, redox conditions, and microbial activity. Our objective was to investigate how freeze-thaw cycles impacted the pore network of newly thawed permafrost aggregates to improve understanding of what type of transformations can be expected from warming Arctic landscapes. We measured the impact of freeze-thaw on pore morphology, pore throat diameter distribution, and pore connectivity with X-ray computed tomography (XCT) using six permafrost aggregates with sizes of 2.5 cm3 from a mineral soil horizon (Bw; 28-50 cm depths) in Toolik, Alaska. Freeze-thaw cycles were performed using a laboratory incubation consisting of five freeze-thaw cycles (-10˚C to 20˚C) over five weeks. Our findings indicated decreasing spatial connectivity of the pore network across all aggregates with higher frequencies of singly connected pores following freeze-thaw. Water-filled pores that were connected to the pore network decreased in volume while the overall connected pore volumetric fraction was not affected. Shifts in the pore throat diameter distribution were mostly observed in pore throats ranges of 100 microns or less with no corresponding changes to the pore shape factor of pore throats. Responses of the pore network to freeze-thaw varied with aggregate, suggesting that initial pore morphology may play a role in driving freeze-thaw response. Our research suggests that freeze-thaw alters the microenvironment of permafrost aggregates during the incipient stage of deformation following permafrost thaw, impacting soil properties and function in Arctic landscapes undergoing transition. This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv format, which can be accessed and processed using MS Excel or R. This archive can also be accessed on GitHub at https://github.com/Erin-Rooney/XCT-freezethaw (DOI: 10.5281/zenodo.5816355).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Processing and mechanical characterization of short carbon fiber-reinforced epoxy composites for material extrusion additive manufacturing

Fiber-reinforced polymer composites have been extensively utilized in recent years as feedstock materials for material extrusion additive manufacturing (AM) processes to improve strength, stiffness, and functionality of printed parts over unfilled printed polymers. However, the widespread adoption of AM of fiber-reinforced polymer composites requires a deeper understanding of the process-structure-property relationships in printed components, and such relationships are not well understood yet. Fiber length is critically important to the mechanical performance of short fiber composites, but very few studies to-date have focused on how the fiber length distribution (FLD) evolves during processing of composite feedstocks and how this evolution affects printing behavior and mechanical properties in 3D-printed composites. Here, FLD is measured for carbon fiber reinforced epoxy composites over a wide range of ink compositions and shear mixing times, and the distributions are fit with a Weibull-type distribution function. The effects of FLD on the tradeoff between ink processability, ink rheology, printing behavior and mechanical properties are investigated. Furthermore, the effects of printing parameters (nozzle size and print speed) on mechanical anisotropy and fiber orientation distribution (FOD) in printed composites are explored. Mechanical properties of printed composites are characterized via 3 pt-flexural testing, and microstructure is investigated using optical and scanning electron microscopy (SEM), and x-ray computed tomography. Finally, the fitted Weibull parameters are fed into a composite model that incorporates FLD and FOD, and model predictions are found to be in excellent agreement with experimental observations.

3D printing↗
DOE OSTI2025

Correlating energy density induced residual stress, porosity, and mechanical property variations in directed energy deposition using neutron diffraction and imaging techniques

In a directed energy deposition (DED) process, the energy density deposited into the materials drives the structure and properties. The energy density is influenced by several printing parameters, such as laser power, hatch spacing, scanning speed, etc. In this study, 316L stainless steels samples were fabricated with varying hatch spacing aiming to induce variations in sample microstructure and properties. A combination of techniques including engineering neutron diffraction, neutron computed tomography, and tensile testing, were employed to correlate the structure and macroscopic properties. Porosity was predominantly observed at the bottom of the deposited materials and was effectively reduced by decreased hatch spacing. Correlatively, compressive residual stress was observed at the bottom of the specimens, while internal stress is largely determined by the hatch spacing. The residual stress in general decreases as the hatch spacing decreases. The maximum ultimate tensile strength (UTS) was found to increase from 517.4 to 528.6 MPa with a strain increased from 0.847 to 0.897 as the hatch spacing decreased from 0.45 to 0.42 mm. However, further increase in energy density by reducing the hatch spacing resulted in a significant decrease in UTS (as low as 452 MPa). This work provides new insights from bulk non-destructive techniques into correlating energy density induced residual stress, porosity, and mechanical property variations in directed energy deposition.

316L stainless steel↗
DOE OSTI2020

Report on Progress of correlation of in-situ and ex-situ data and the use of artificial intelligence to predict defects

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear components which will be assembled into a fully functional microreactor core. Compared with traditional manufacturing technologies, AM technologies allow (1) real-time observation of the manufacturing process at a much higher resolution using in-situ monitoring technologies to capture the sensor signatures that scientifically describe each event occurring over time and space and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on in-situ and ex-situ data correlation and associated data analytics results. Examples are provided to illustrate progress with respect to laser powder bed fusion (L-PBF), binder jetting, computed tomography (CT) reconstruction, and mechanical testing. Elements of the Digital Thread and data management infrastructure are discussed in the main document, and an extensive supplemental appendix is provided detailing the Digital Platform, as well as its implementation and subcomponents. In conclusion, the path forward for the next fiscal year is also discussed.

42 ENGINEERING↗
DOE OSTI2021

Particle-Tracking Proton Computed Tomography—Data Acquisition, Preprocessing, and Preconditioning

Proton CT (pCT) is a promising new imaging technique that can reconstruct relative stopping power (RSP) more accurately than x-ray CT in each cubic millimeter voxel of the patient. This, in turn, will result in better proton range accuracy and, therefore, smaller planned tumor volumes (PTV). The hardware description and some reconstructed images have previously been reported. In a series of two contributions, we focus on presenting the software algorithms that convert pCT detector data to the final reconstructed pCT images for application in proton treatment planning. There were several options on how to accomplish this, and we will describe our solutions at each stage of the data processing chain. In the first paper of this series, we present the data acquisition with the pCT tracking and energy-range detectors and how the data are preprocessed, including the conversion to the well-formatted track information from tracking data and water-equivalent path length from the data of a calibrated multi-stage energy-range detector. These preprocessed data are then used for the initial image formation with an FDK cone-beam CT algorithm. The output of data acquisition, preprocessing, and FDK reconstruction is presented along with illustrative imaging results for two phantoms, including a pediatric head phantom. The second paper in this series will demonstrate the use of iterative solvers in conjunction with the superiorization methodology to further improve the images resulting from the upfront FDK image reconstruction and the implementation of these algorithms on a hybrid CPU/GPU computer cluster.

42 ENGINEERING↗
DOE OSTI2022

CodEx: A Modular Framework for Joint Temporal De-Blurring and Tomographic Reconstruction

In many computed tomography (CT) imaging applications, it is important to rapidly collect data from an object that is moving or changing with time. Tomographic acquisition is generally assumed to be step-and-shoot, where the object is rotated to each desired angle, and a view is taken. However, step-and-shoot acquisition is slow and can waste photons, so in practice fly-scanning is done where the object is continuously rotated while collecting data. However, this can result in motion-blurred views and consequently reconstructions with severe motion artifacts. In this paper, we introduce CodEx, a modular framework for joint de-blurring and tomographic reconstruction that can effectively invert the motion blur introduced in sparse view fly-scanning. The method is a synergistic combination of a novel acquisition method with a novel non-convex Bayesian reconstruction algorithm. CodEx works by encoding the acquisition with a known binary code that the reconstruction algorithm then inverts. Using a well chosen binary code to encode the measurements can improve the accuracy of the inversion process. The CodEx reconstruction method uses the alternating direction method of multipliers (ADMM) to split the inverse problem into iterative deblurring and reconstruction sub-problems, making reconstruction practical to implement. Here we present reconstruction results on both simulated and binned experimental data to demonstrate the effectiveness of our method.

42 ENGINEERING↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2023

Changes in soil pore structure generated by the root systems of maize , sorghum and switchgrass affect in situ N 2 O emissions and bacterial denitrification

Due to the heterogeneous nature of soil pore structure, processes such as nitrification and denitrification can occur simultaneously at microscopic levels, making prediction of small-scale nitrous oxide (N 2 O) emissions in the field notoriously difficult. We assessed N 2 O+N 2 emissions from soils under maize (Zea mays L.), switchgrass (Panicum virgatum L.), and energy sorghum (Sorghum bicolor L.), three potential bioenergy crops in order to identify the importance of different N 2 O sources to microsite production, and relate N 2 O source differences to crop-associated differences in pore structure formation. The combination of isotopic surveys of N 2 O in the field during one growing season and X-ray computed tomography (CT) enabled us to link results from isotopic mappings to soil structural properties. Further, our methodology allowed us to evaluate the potential for in situ N 2 O suppression by biological nitrification inhibition (BNI) in energy sorghum. Our results demonstrated that the fraction of N 2 O originating from bacterial denitrification and reduction of N 2 O to N 2 is largely determined by the volume of particulate organic matter occluded within the soil matrix and the anaerobic soil volume. Bacterial denitrification was greater in switchgrass than in the annual crops, related to changes in pore structure caused by the coarse root system. This led to high N-loses through N 2 emissions in the switchgrass system throughout the season a novel finding given the lack of data in the literature for total denitrification. Isotopic mapping indicated no differences in N 2 O-fluxes or their source processes between maize and energy sorghum that could be associated with the release of BNI by the investigated sorghum variety. The results of this research show how differences in soil pore structures among cropping systems can determine both N 2 O production via denitrification and total denitrification N losses in situ.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components

X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10 x while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.

Complex metal parts↗
DOE OSTI2022

Phase instabilities in austenitic steels during particle bombardment at high and low dose rates

Disruption of phase stability by energetic particle bombardment is a major challenge in designing advanced radiation-tolerant alloys and ion beam processing of nanocomposites. Particularly, ballistic dissolution susceptibility of different solute nanocluster species in alloys is poorly understood. Here, low dose rate neutron irradiations were conducted on a Fe-Cr-Ni based austenitic steel in the BOR-60 reactor (9.4 × 10 -7 dpa/s, 318 °C) followed by accelerated dose rate ion irradiations at multiple temperatures ( 10 -3 dpa/s, 380 – 420 °C). Using atom probe tomography, the stability of radiation-enhanced Cu-rich and radiation-induced Ni-Si-Mn-rich nanoclusters was evaluated. During neutron irradiation, Cu-rich clusters nucleated with their core concentrations progressively increasing with dose, while Ni-Si-Mn-rich clusters formed and evolved into G-phase precipitates. Ion irradiations dramatically altered the nanoclusters. Cu-rich clusters were ballistically dissolved, but Ni-Si-Mn-rich clusters remained stable and coarsened with dose at 400 and 420 °C, highlighting that different nanocluster species in a single microstructure can have innately distinct ballistic dissolution susceptibilities. Solute-specific recoil rates were incorporated into the Heinig precipitate stability model, which shows that in addition to radiation-enhanced diffusion, recovery from ballistic dissolution depends on solute concentration gradient near cluster interfaces. Finally, the combined experimental-modeling study quantified the critical temperatures and damage rates where ballistic dissolution dominates for each cluster species.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Si/SiGe QuBus for single electron information-processing devices with memory and micron-scale connectivity function

The connectivity within single carrier information-processing devices requires transport and storage of single charge quanta. Single electrons have been adiabatically transported while confined to a moving quantum dot in short, all-electrical Si/SiGe shuttle device, called quantum bus (QuBus). Here we show a QuBus spanning a length of 10 μm and operated by only six simply-tunable voltage pulses. We introduce a characterization method, called shuttle-tomography, to benchmark the potential imperfections and local shuttle-fidelity of the QuBus. The fidelity of the single-electron shuttle across the full device and back (a total distance of 19 μm) is (99.7 ± 0.3) %. Using the QuBus, we position and detect up to 34 electrons and initialize a register of 34 quantum dots with arbitrarily chosen patterns of zero and single-electrons. The simple operation signals, compatibility with industry fabrication and low spin-environment-interaction in 28 Si/SiGe, promises long-range spin-conserving transport of spin qubits for quantum connectivity in quantum computing architectures.

97 MATHEMATICS AND COMPUTING↗