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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Framework for Inverse Prediction Using Functional Response Data

Inverse prediction models have commonly been developed to handle scalar data from physical experiments. However, it is not uncommon for data to be collected in functional form. When data are collected in functional form, it must be aggregated to fit the form of traditional methods, which often results in a loss of information. For expensive experiments, this loss of information can be costly. In this study, we introduce the functional inverse prediction (FIP) framework, a general approach which uses the full information in functional response data to provide inverse predictions with probabilistic prediction uncertainties obtained with the bootstrap. The FIP framework is a general methodology that can be modified by practitioners to accommodate many different applications and types of data. We demonstrate the framework, highlighting points of flexibility, with a simulation example and applications to weather data and to nuclear forensics. Results show how functional models can improve the accuracy and precision of predictions.

42 ENGINEERING↗
DOE OSTI2022

Transition metal impurities in silicon: computational search for a semiconductor qubit

Abstract Semiconductors offer a promising platform for physical implementation of qubits, but their broad adoption is presently hindered by limited scalability and/or very low operating temperatures. Learning from the nitrogen-vacancy centers in diamond, our goal is to find equivalent optically active point defect centers in crystalline silicon, which could be advantageous for their scalability and integration with classical devices. Transition metal (TM) impurities in silicon are common paramagnetic deep defects, but a comprehensive theoretical study of the whole 3 d series that considers generalized Koopmans’ condition is missing. We apply the HSE06(+U) method to examine their potential as optically active spin qubits and identify seven TM impurities that have optically allowed triplet–triplet transitions within the silicon band gap. These results provide the first step toward silicon-based qubits with higher operating temperatures for quantum sensing. Additionally, these point defects could lead to spin-photon interfaces in silicon-based qubits and devices for mid-infrared free-space communications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Selective modulation of electronic transport in VO 2 induced by 10 keV helium ion irradiation

Vanadium dioxide (VO 2 ) manifests an abrupt metal–insulator transition (MIT) from monoclinic to rutile phases, with potential use for tunable electronic and optical properties and spiking neuromorphic devices. Understanding pathways to modulate electronic transport in VO 2 , as well as its response to irradiation (e.g., for space applications), is critical to better enable these applications. In this work, we investigate the selective modulation of electronic transport in VO 2 films subject to different 10 keV helium ion (He + ) fluences. Under these conditions, the resistivity in the individual monoclinic and rutile phases varied by 50%–200%, while the MIT transformation temperature remains constant within 4 °C independent of irradiation fluence. Importantly, different trends in the resistivity of the monoclinic and rutile phases were observed both as a function of total He fluence as well as in films grown on different substrates (amorphous SiO 2 /Si vs single crystal Al 2 O 3 ). Through a combination of measurements including majority carrier sign via Seebeck, low frequency noise, and TEM, our investigation supports the presence of different kinds of point defects (V in; O in), which may arise due to grain boundary defect interactions. Our work suggests the utility of He irradiation for the selective modulation of VO 2 transport properties for neuromorphic, in contrast to other established but non-selective methods, like doping.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single-reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wave function theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. Additionally, to reduce the cost of generating inputs to the VAT model, we leverage the VAT model’s robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in an MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks

Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial a priori intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. Furthermore, these backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si–O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A strain-driven thermotropic phase boundary in BaTiO 3 at room temperature by cycling compression

In BaTiO 3 single crystals, we observed a strain-driven phase transition from the tetragonal phase to the tetragonal-orthorhombic phase boundary which can be introduced by slow cycling compressions (a loading of up to 0.5 GPa, strain rate of 10 -4 s -1 , and 100 cycles) at room temperature. Different from the well-known tetragonal to cubic phase transition under stress (~2 GPa), it only takes place locally around bent 90° domain walls. The inhomogeneous local stress and electrical fields as well as the mobile point defect pinning effect contribute to the phase re-entrance. Through comparison experiments by in situ synchrotron x-ray diffraction, Raman scattering, and (scanning) transmission electron microscopy, we explored the phase transition mechanism. Based on that, we developed a mechanical method to obtain well-stabilized high-density thermotropic phase boundary structures (with tetragonal, orthorhombic, and bridging monoclinic phases) in BaTiO 3 for potential applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermal conductivity measurement of U-Mo and U-Mo/Al interaction layers generated from in-pile irradiation using the suspended-bridge method

Here, this study presents the first measurement of the individual thermal conductivity of U-7wt.%Mo fuel particles and U-Mo/Al interaction layers (ILs) from in-pile irradiated dispersion fuel plates, using the suspended-bridge method. Nanorods of U-7wt.%Mo fuel and U-Mo/Al ILs were extracted by focused ion beam (FIB), and their microstructures were characterized with transmission electron microscopy (TEM). TEM revealed finely distributed nanobubbles in the U-7wt.%Mo matrix, along with an amorphous structure in the ILs. The thermal conductivity of in-pile irradiated U-7wt.%Mo was approximately 30% lower than that of the unirradiated material, ranging from 6.7 W/m·K at 300 K to 8.5 W/m·K at 380 K. The ILs exhibited even lower thermal conductivity, from 2.1 W/m·K at 300 K to 2.7 W/m·K at 380 K. These reductions, attributed to nanobubbles, fission products, and irradiation-induced point defects, were analyzed through a combination of microstructural characterization and literature-based transport models, which successfully reproduced the observed degradation trends.

42 - ENGINEERING↗
DOE OSTI2022

Designing silicon carbide heterostructures for quantum information science: challenges and opportunities

Abstract Silicon carbide (SiC) can be synthesized in a number of different structural forms known as polytypes with a vast array of optically active point defects of interest for quantum information sciences. The ability to control and vary the polytypes during SiC synthesis may offer a powerful methodology for the formation of new material architectures that expand our ability to manipulate these defects, including extending coherence lifetimes and enhancing room temperature operation. Polytypic control during synthesis presents a significant challenge given the extreme conditions under which SiC is typically grown and the number of factors that can influence polytype selection. In situ monitoring of the synthesis process could significantly expand our ability to formulate novel polytype structures. In this perspective, we outline the state of the art and ongoing challenges for precision synthesis in SiC. We discuss available in situ x-ray characterization methods that will be instrumental in understanding the atomic scale growth of SiC and defect formation mechanisms. We highlight optimistic use cases for SiC heterostructures that will become possible with in situ polytypic control and end by discussing extended opportunities for integration of ultrahigh quality SiC materials with other semiconductor and quantum materials.

Harmon, K. J. (ORCID:0000000164277971)↗
DOE OSTI2024

Predicting ptychography probe positions using single-shot phase retrieval neural network

Ptychography is a powerful imaging technique that is used in a variety of fields, including materials science, biology, and nanotechnology. However, the accuracy of the reconstructed ptychography image is highly dependent on the accuracy of the recorded probe positions which often contain errors. These errors are typically corrected jointly with phase retrieval through numerical optimization approaches. When the error accumulates along the scan path or when the error magnitude is large, these approaches may not converge with satisfactory result. We propose a fundamentally new approach for ptychography probe position prediction for data with large position errors, where a neural network is used to make single-shot phase retrieval on individual diffraction patterns, yielding the object image at each scan point. The pairwise offsets among these images are then found using a robust image registration method, and the results are combined to yield the complete scan path by constructing and solving a linear equation. We show that our method can achieve good position prediction accuracy for data with large and accumulating errors on the order of 10 2 pixels, a magnitude that often makes optimization-based algorithms fail to converge. For ptychography instruments without sophisticated position control equipment such as interferometers, our method is of significant practical potential.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing

Integrated computational materials engineering (ICME) methods combining CALPHAD with process-based simulations can produce rich, high-dimensional data for alloy and process design. In ICME methods for metallurgical applications, the visualization and interpretation of such high-dimensional data has previously been through heat maps represented in 2 or 3 dimensions. While such an approach is ideal when one variable is varied at a time, in the case of high-dimensional data with multiple variables varied simultaneously, as is the case in additive manufacturing, interpreting the trends through two- or three-dimensional heat maps becomes challenging. Here, we propose a strategy of mixed visual data mining and quantitative analysis for high-dimensional metallurgical and process data using high-throughput thermodynamic calculations. Two case studies show the application of the proposed approach. The first case study investigated the effects of feedstock chemistry on the δ ferrite formation in 316L stainless steel powders used for binder jet additive manufacturing. The second case study linked Scheil–Gulliver calculations to a process model for dissimilar joining of aluminum alloys 5356 and 6111 during laser hot-wire additive manufacturing. Both cases contained thousands of calculated data points, showcasing the utility of visual data analysis through parallel coordinate plotting, Pearson correlation coefficient matrices, and scatter matrices compared to traditional process maps. These visualization techniques can be extended to many additive manufacturing problems to capture process–structure–property relationships for additively manufactured components.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Interlaboratory Reproducibility of Contour Method Data in a High Strength Aluminum Alloy

The contour method for residual stress measurement has seen significant development, but an experimental reproducibility study utilizing physical samples has not been published. A double-blind reproducibly study is reported, having scope beginning with EDM cutting and ending with residual stress calculation. A reinforced I-beam sample geometry is identified for its unique residual stress profile when extracted from residual stress bearing quenched aluminum bar (7050-T74). Contour measurements are prescribed on a midplane of symmetry with dimensions 24.0 mm by 50.0 mm. Fourteen identically prepared samples are fabricated from a single long bar with well characterized and uniform residual stress. Five samples throughout the bar are identified for planning measurements to validate sample uniformity and overall suitability of the residual stress field. The planning measurements employ a range of techniques: contour method, neutron diffraction, and hole-drilling. Eight samples are distributed to an international group of participants to execute their standard measurement practice. A double-blind process is followed to provide anonymity. Results are provided by eight participants: six being self-similar and two being quite different, the latter set aside as outliers. An average residual stress field is established from non-outlying results and the spatial distribution of reproducibility standard deviation is determined. The average stress field ranges from -60 to 70 MPa and the reproducibility standard deviation averages 8.1 MPa on the measurement plane. The average reproducibility standard deviation is about 3 × larger for points within 1.0 mm of plane boundaries (17.6 MPa) than for the remaining points (6.1 MPa). Reproducibility standard deviation (among different labs) for contour method residual stress measurement is found to be very similar to repeatability standard deviation (in a single lab) reported in prior work. The reproducibility observed here, for the entire measurement process, is also similar to that found in a prior reproducibility study limited to contour method data analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Synthesis of bulk reactive Ni–Al composites using high pressure torsion

Self-propagating exothermic reactions, for instance in the nickel-aluminum (Ni-Al) system, have been widely studied to create high performance intermetallic compounds or for in-situ welding. Their easy ignition once the phase spacing is reduced below the micron scale, makes top-down methods like high-energy ball milling, ideal to fabricate such reactive nanostructures. A major drawback of ball milling is the need of a sintering step to form bulk pieces of the reactive material. However, this is not possible, as the targeted reactions would already proceed. Therefore, we investigate the ability of high pressure torsion as an alternative process, capable to produce bulk nanocomposites from powder mixtures. Severe straining of powder mixtures with a composition of 50 wt% Ni and 50 wt% Al enables fabrication of self-reactive bulk samples with microstructures similar to those obtained from ball milling or magnetron sputtering. Additionally, samples deformed at ambient temperature are highly reactive and can be ignited significantly below the Al melting point, finally predominantly consisting of Al 3 Ni 2 and Al 3 Ni, independent of the applied strain. Although the reaction proceeds first at the edge of the disk, the strain gradient present in the disks does not prevent reaction of the whole sample.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Cracked Gridline Wear Out Follows a Power Law

Cracks can form in Silicon solar cells in photovoltaic modules due to mechanical stresses arising from various extrinsic factors like handling and weather. While the immediate performance degradation may be minor, continuous loading overtime will degrade module performance. One probable reason is gridline surface wear across the cracked silicon with increased cyclic loading. In this work we propose a method to correlate gridline wear to module electrical degradation. We begin by conducting cyclic four-point bending tests on laminated silicon solar cells with a single crack and 22 intact gridlines for 10,000 cycles. We measure the progressive change in resistance during each loading cycle. We correlate it to a length scale called critical crack opening displacement (CCOD) that signifies failure of individual gridlines. By employing Weibull analysis, we determine the characteristic CCOD for all cycles and fit this data to a modified version of a wear power law. We observe that this i ts the data well. We also propose to study the effect of individual parameters in the power law equation and extend the equation to include material properties.

bending↗
DOE OSTI2023

Disentangling Electronic Transport and Hysteresis at Individual Grain Boundaries in Hybrid Perovskites via Automated Scanning Probe Microscopy

Underlying the rapidly increasing photovoltaic efficiency and stability of metal halide perovskites (MHPs) is the advancement in the understanding of the microstructure of polycrystalline MHP thin film. Over the past decade, intense efforts have been aimed at understanding the effect of microstructures on MHP properties, including chemical heterogeneity, strain disorder, phase impurity, etc. It has been found that grain and grain boundary (GB) are tightly related to lots of microscale and nanoscale behavior in MHP thin films. Atomic force microscopy (AFM) is widely used to observe grain and boundary structures in topography and subsequently to study the correlative surface potential and conductivity of these structures. For now, most AFM measurements have been performed in imaging mode to study the static behavior; in contrast, AFM spectroscopy mode allows us to investigate the dynamic behavior of materials, e.g., conductivity under sweeping voltage. However, a major limitation of AFM spectroscopy measurements is that they require manual operation by human operators, and as such only limited data can be obtained, hindering systematic investigations of these microstructures. Here, we designed a workflow combining the conductive AFM measurement with a machine learning (ML) algorithm to systematically investigate grain boundaries in MHPs. The trained ML model can extract GBs locations from the topography image, and the workflow drives the AFM probe to each GB location to perform a current–voltage (IV) curve automatically. Then, we are able to have IV curves at all GB locations, allowing us to systematically understand the property of GBs. Using this method, we discovered that the GB junction points are less conductive, potentially more photoactive, and can play critical roles in MHP stability, while most previous works only focused on the difference between GB and grains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Future of plasma etching for microelectronics: Challenges and opportunities

Plasma etching is an essential semiconductor manufacturing technology required to enable the current microelectronics industry. Along with lithographic patterning, thin-film formation methods, and others, plasma etching has dynamically evolved to meet the exponentially growing demands of the microelectronics industry that enables modern society. At this time, plasma etching faces a period of unprecedented changes owing to numerous factors, including aggressive transition to three-dimensional (3D) device architectures, process precision approaching atomic-scale critical dimensions, introduction of new materials, fundamental silicon device limits, and parallel evolution of post-CMOS approaches. The vast growth of the microelectronics industry has emphasized its role in addressing major societal challenges, including questions on the sustainability of the associated energy use, semiconductor manufacturing related emissions of greenhouse gases, and others. The goal of this article is to help both define the challenges for plasma etching and point out effective plasma etching technology options that may play essential roles in defining microelectronics manufacturing in the future. The challenges are accompanied by significant new opportunities, including integrating experiments with various computational approaches such as machine learning/artificial intelligence and progress in computational approaches, including the realization of digital twins of physical etch chambers through hybrid/coupled models. These prospects can enable innovative solutions to problems that were not available during the past 50 years of plasma etch development in the microelectronics industry. To elaborate on these perspectives, the present article brings together the views of various experts on the different topics that will shape plasma etching for microelectronics manufacturing of the future.

Engineering↗
DOE OSTI2022

Rotational flexural strength of cylindrical brittle specimens

Conventional static flexural strength testing of brittle cylindrical rods only subjects a small fraction of the entire specimen's area or volume to the maximum tensile stress. Thus, a nonconservative measured strength likely results since most flaws on the surface or in the bulk are not subjected to a sufficiently high tensile stress that can cause fracture. Here, to mitigate this, a rotational flexural tester and corresponding test method were developed whereby rotation and monotonically increasing three-point flexure were superimposed to investigate fracture response of solid glass cylinders. This combination of rotation and flexure subjects more area and volume of a cylindrical test specimen to tensile stress than a standard static (nonrotating) flexural test. As anticipated, failure stresses were lower for the rotational flexural test. Expressions for effective area and volume are provided for rotating solid rods and tubes subjected to three-point, four-point, uniform, and uniformly distributed load bending configurations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Field controlled direct-write electrospinning

The invention provides a method for producing fibers, the method having the steps of producing electrospun fiber; and directing the fiber to a plurality of targets, whereby only one of the targets is grounded at any one time. Also provided is a system for producing anisotropic fibers having a reservoir of electrospun fiber constituent' wherein an egress point of the reservoir is electrically charged; a plurality of electrodes opposed to the egress point, wherein only one of the electrodes is grounded at any one time while the remaining electrodes are at an initial floating voltage state; and a target substrate disposed between the egress point and the plurality of electrodes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ab Initio Phase Diagram of Chromium to 2.5 TPa

Chromium possesses remarkable physical properties such as hardness and corrosion resistance. Chromium is also a very important geophysical material as it is assumed that lighter Cr isotopes were dissolved in the Earth’s molten core during the planet’s formation, which makes Cr one of the main constituents of the Earth’s core. Unfortunately, Cr has remained one of the least studied 3d transition metals. In a very recent combined experimental and theoretical study (Anzellini et al., Scientific Reports, 2022), the equation of state and melting curve of chromium were studied to 150 GPa, and it was determined that the ambient body-centered cubic (bcc) phase of crystalline Cr remains stable in the whole pressure range considered. However, the importance of the knowledge of the physical properties of Cr, specifically its phase diagram, necessitates further study of Cr to higher pressure. In this work, using a suite of ab initio quantum molecular dynamics (QMD) simulations based on the Z methodology which combines both direct Z method for the simulation of melting curves and inverse Z method for the calculation of solid–solid phase transition boundaries, we obtain the theoretical phase diagram of Cr to 2.5 TPa. We calculate the melting curves of the two solid phases that are present on its phase diagram, namely, the lower-pressure bcc and the higher-pressure hexagonal close-packed (hcp) ones, and obtain the equation for the bcc-hcp solid–solid phase transition boundary. We also obtain the thermal equations of state of both bcc-Cr and hcp-Cr, which are in excellent agreement with both experimental data and QMD simulations. We argue that 2180 K as the value of the ambient melting point of Cr which is offered by several public web resources (“Wikipedia,” “WebElements,” “It’s Elemental,” etc.) is most likely incorrect and should be replaced with 2135 K, found in most experimental studies as well as in the present theoretical work.

equation of state↗