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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Detector Materials Scoping Study: Research and Development Recommendations

Radiation detection materials have a unique role in many nuclear security missions. The variety of different applications requires the use of different materials whose characteristics are determined by the specific application requirements. While baseline capabilities have been established to use radiation detection in these applications, the potential to greatly advance those capabilities still exists through the development of superior radiation detection materials and/or the development of new materials that enable new technologies to be implemented more effectively. In 2024 the Defense Nuclear Nonproliferation Research and Development (DNN R&D) Near Field Detection Portfolio charged a detector materials scoping study, composed of a group of radiation-detection materials subject matter experts, to produce a community-wide consensus view of the current state of radiation materials research and to set recommendations for expanded investment over a ten-year time frame. This scoping study sought to understand and document the landscape of how radiation detection materials are used, what characteristics drive the selection of various materials, and what developments are needed to drive the state of the art. To respond to this charge, the scoping study created three working groups, each focusing on a different topical area in radiation detection materials research - Semiconductor Materials, Inorganic Scintillators and Organic Scintillators - with the task to identify the current state of the art in materials research and to provide mission-relevant research and development recommendations. Each working group was chaired by two members of the project team with widely recognized expertise in the field. Solicitations were sent out to experts at the DOE national laboratories, industry, academia and federal government agencies to participate in these working groups. The working groups met at regular intervals throughout 2024 and early 2025, each delivering a technical roadmap designed to enable the pursuit of R&D more intensely on high-impact materials challenges and detector solutions that are deemed as the greatest strategic value to DNN R&D and its stakeholders. The roadmap is also intended to help communicate recommended programmatic priorities within DNN R&D and across the nonproliferation and research communities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Quantifying uncertainty associated with the microstructure variation of a material can be a computationally daunting task, especially when dealing with advanced constitutive models and fine mesh resolutions in the crystal plasticity finite element method (CPFEM). Numerous studies have been conducted regarding the sensitivity of material properties and performance to the mesh resolution and choice of constitutive model. However, a unified approach that accounts for various fidelity parameters, such as mesh resolutions, integration time-steps and constitutive models simultaneously is currently lacking. Further, this paper proposes a novel uncertainty quantification (UQ) approach for computing the properties and performance of homogenized materials using CPFEM, that exploits a hierarchy of approximations with different levels of fidelity. In particular, we illustrate how multi-level sampling methods, such as multi-level Monte Carlo (MLMC) and multi-index Monte Carlo (MIMC), can be applied to assess the impact of variations in the microstructure of polycrystalline materials on the predictions of homogenized materials properties. We show that by adaptively exploiting the fidelity hierarchy, we can significantly reduce the number of microstructures required to reach a certain prescribed accuracy. Finally, we show how our approach can be extended to a multi-fidelity framework, where we allow the underlying constitutive model to be chosen from either a phenomenological plasticity model or a dislocation-density-based model.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Technique development for modulus, microcracking, hermeticity, and coating evaluation capability characterization of SiC/SiC tubes

Driven by the need to enlarge the safety margins of nuclear fission reactors in accident scenarios, research and development of accident-tolerant fuel has become an important topic in the nuclear engineering and materials community. A continuous-fiber SiC/SiC composite is under consideration as a replacement for traditional zirconium alloy cladding owing to its high-temperature stability, chemical inertness, and exceptional irradiation resistance. An important task is the development of characterization techniques for SiC/SiC cladding, since traditional work using rectangular bars or disks cannot directly provide useful information on the properties of SiC/SiC composite tubes for fuel cladding applications. At Oak Ridge National Laboratory, experimental capabilities are under development to characterize the modulus, microcracking, and hermeticity of as-fabricated, as-irradiated SiC/SiC composite tubes. Resonant ultrasound spectroscopy has been validated as a promising technique to evaluate the elastic properties of SiC/SiC composite tubes and microcracking within the material. A similar technique, impulse excitation, is efficient in determining the basic mechanical properties of SiC bars prepared by chemical vapor deposition; it also has potential for application in studying the mechanical properties of SiC/SiC composite tubes. Complete evaluation of the quality of the developed coatings, a major mitigation strategy against gas permeation and hydrothermal corrosion, requires the deployment of various experimental techniques, such as scratch indentation, tensile pulling-off tests, and scanning electron microscopy. In addition, a comprehensive permeation test station is being established to assess the hermeticity of SiC/SiC composite tubes and to determine the H/D/He permeability of SiC/SiC composites. This report summarizes the current status of the development of these experimental capabilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

SAM-I-Am: Semantic boosting for zero-shot atomic-scale electron micrograph segmentation

Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics — where are boundaries located? what segments are logically similar? — change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in certain domains, fine-tuning and retraining techniques are infeasible: obtaining labels is costly and time-consuming; domain images (micrographs) can be exponentially diverse; and data sharing (for third-party retraining) is restricted. To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations. Here, we apply semantic boosting to the Segment Anything Model (SAM) to obtain microstructure segmentation for transmission electron microscopy. Our booster, SAM-I-Am, serves as a post-processing engine that extracts geometric and textural features of various intermediate masks to perform mask removal and mask merging operations. We demonstrate a zero-shot performance increase of (absolute) +21.35%, +12.6%, +5.27% in mean IoU, and a -9.91%, -18.42%, -4.06% drop in mean false positive masks across images of three difficulty classes over vanilla SAM (ViT-L).

36 MATERIALS SCIENCE↗
DOE OSTI2018

X-ray magnetic circular dichroism and near-edge X-ray absorption fine structure of buried interfacial magnetism measured by using a scanning tunneling microscope tip

Magnetism at buried interfaces plays a crucial role in many emerging phenomena, but detection of interfacial magnetism in close proximity to a surface with elemental and chemical sensitivity is a challenging task. Here, we use a low temperature synchrotron x-ray scanning tunneling microscopy to investigate x-ray magnetic circular dichroism and near edge x-ray absorption fine structure of La 0.67 Sr 0.33 MnO 3 -LaNiO 3 superlattices in the absence of a magnetic field. In stark contrast to the weak magnetic signal of Mn when the La 0.67 Sr 0.33 MnO 3 layers are located on top, a robust x-ray magnetic circular dichroism signal is detected when they are buried underneath the LaNiO 3 layers. The near edge x-ray absorption fine structure reveals the valence states of the manganese while the oxygen K- edge x-ray absorption spectra show an increase in hole formation indicating a cogent charge transfer at the LaNiO 3 /La 0.67 Sr 0.33 MnO 3 interface. This work demonstrates that scanning tunneling microscopy can be extended to the synchrotron X-ray study of buried interfaces by controlling the sample-tip (e.g., detector) separation in the nanometer regime. The close sample-tip proximity further provides a path toward probing of small lateral areas with zero-field measurements in multi-domain samples.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Arc Parameter Modeling (Arc Breakdown Experiments and Modeling: Confined Geometries and Alternate Gap Materials)

Sandia Nationa.l Laboratories (SNL) and The Boeing Company (Boeing) collaborated on this PTS to develop the capability to predict thermal and electrical characteristics of short arcs that may appear in equipment subjected to transient currents of hundreds or thousands of amperes. The final tasks of this CRADA included experiments and computer simulations used to investigate the effect of an arc breakdown between two planar electrodes and confined by various potting materials. The arc is generated by a current waveform with a rise of 8 μs and a 20 μs fall time. Voltage, current and light emissions are recorded. The developing shock wave is observed with an optical fiber embedded in the potting material and recorded using a photonic Doppler velocimetry (PDV) diagnostic system. The experiments were modeled using the ALEGRA-MHD simulation code and comparisons are made between the experiments and simulations. This product contains Protected CRADA Information which was produced December 2015 under CRADA No. 1651. l 5 and is not to be further disclosed for a period of 5 years from the date it was produced except as expressly provided for in the CRADA. Further dissemination only as authorized by the originating partners: the Boeing Company and Sandia National Laboratories Radiation Sciences Center, or higher U.S. Department of Energy programmatic authority. Please note that the project leads from both SNL and Boeing have all retired or left for other reasons at the time of the creation of this document. The final project manager collected information for this document from the final proposal and the final report.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning Thermodynamics And Kinetics of Defects For Accelerated Materials Discovery

Atomistic defects play a pivotal role in functional and structural materials’ performance across a myriad of technology applications. Quantitative prediction of the thermodynamics and kinetics of defect formation and migration, respectively, typically requires accurate but expensive first-principles approaches, such as density functional theory (DFT). Their computational expense limits the throughput needed to perform high-throughput materials discovery/screening exercises or to perform materials modeling tasks relying on extensive sampling techniques. Therefore, in this Sandia National Laboratories Laboratory Directed Research and Development (LDRD) project (Project #229366), we developed a variety of machine learning techniques, trained on density functional theory calculations, to accelerate the discovery and modeling of materials in which vacancy and interstitial defects primarily dictate material performance. These include applications such as metal oxides for water-splitting or mixed ionic-electronic conduction, metal hydrides for hydrogen storage, and transition metal dichalcogenides for electronics, and the approaches developed herein can further be applied to many other domains that similarly depend on materials’ thermodynamic and kinetic defect properties for their desired functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Uncertainty quantification of graph convolution neural network models of evolving processes

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural networks have proved to be adept at modeling processes with spatial–temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial–temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar pushed forward uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance. As a result, projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2015

New Insights into the Structure of Nanoporous Carbons from NMR, Raman, and Pair Distribution Function Analysis

The structural characterization of nanoporous carbons is a challenging task as they generally lack long-range order and can exhibit diverse local structures. Such characterization represents an important step toward understanding and improving the properties and functionality of porous carbons, yet few experimental techniques have been developed for this purpose. In this paper, we demonstrate the application of nuclear magnetic resonance (NMR) spectroscopy and pair distribution function (PDF) analysis as new tools to probe the local structures of porous carbons, alongside more conventional Raman spectroscopy. Together, the PDFs and the Raman spectra allow the local chemical bonding to be probed, with the bonding becoming more ordered for carbide-derived carbons (CDCs) synthesized at higher temperatures. The ring currents induced in the NMR experiment (and thus the observed NMR chemical shifts for adsorbed species) are strongly dependent on the size of the aromatic carbon domains. We exploit this property and use computer simulations to show that the carbon domain size increases with the temperature used in the carbon synthesis. The techniques developed here are applicable to a wide range of porous carbons and offer new insights into the structures of CDCs (conventional and vacuum-annealed) and coconut shell-derived activated carbons.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green's function based many-body perturbation theory, wave-function based and stochastic electronic structure approaches, relativistic effects and semiempirical electronic structure theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the electronic structure, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for electronic structure based modeling from the vantage point of industry environments. Overall, the field of electronic structure software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Light-Induced Melting of Competing Stripe Orders without Introducing Superconductivity in La 2 − x Ba x CuO 4

The ultrafast manipulation of quantum material has led to many novel and significant discoveries. Among them, the light-induced transient superconductivity in cuprates achieved by melting competing stripe orders represents a highly appealing accomplishment. However, recent investigations have shown that the notion of photoinduced superconductivity remains a topic of controversy, and its elucidation solely through c -axis time-resolved terahertz spectroscopy remains an arduous task. Here, we measure the in-plane and out-of-plane transient terahertz responses simultaneously in the stripe-ordered nonsuperconducting La 2 − x Ba x CuO 4 after near-infrared excitations. We find that although a pump-induced reflectivity edge appears in the c -axis reflectance spectrum, the reflectivity along the CuO 2 planes decreases simultaneously, indicating an enhancement in the scattering rate of quasiparticles. This in-plane transient response is clearly distinct from the features associated with superconducting condensation. Therefore, we conclude the out-of-plane transient responses cannot be explained by an equivalent of Josephson tunneling. Notably, those pump-induced terahertz responses remain consistent even when we vary the near-infrared optical pump wavelengths and hole concentrations. Our results provide critical evidence that transient three-dimensional superconductivity cannot be induced by melting the competing stripe orders with pump pulses whose photon energy is much higher than the superconducting gap of cuprates. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
DOE OSTI2014

Report on Plasma Spraying of Full-Sized Foils (FY13)

This report is the deliverable fulfilling task B40700. The application of the Zr diffusion barrier between the U-­Mo fuel and the Al cladding has been an ongoing area of research [1-8]. The goal of the research described in this report was to demonstrate the plasma spraying technique for depositing zirconium (Zr) onto full sized U-Mo foils to demonstrate scale up of the process from the mini foil samples (1”x4”x ~0.015”) coated previously. Full size denotes one of the actual fuel foil sizes for one of the five research reactors (MURR, NIST, MIT, HFIR, ATR) to be converted to LEU fuel in the US research reactor Convert program. The smallest full sized fuel foil is for the NIST reactor with a size of 11.37”x2.436”x0.020”. The largest full sized fuel foil is for the ATR reactor with a size of 48.01”x3.43”x0.020”. The widest full sized fuel foil is for the MURR reactor with a size of 24”x4.052”x0.020”. The current LANL vacuum plasma spray system can accommodate a foil length of 24” or less. Therefore, as a demonstration of the widest foil in the program and the maximum length foil that can be processed on the current LANL system, a nominal 24”x4”x0.020” foil was chosen as the goal for scale up activities.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Radiation-Hardened Circuitry Using Mask-Programmable Analog Arrays (September 2014 Milestone Report)

This is a report of the activities involving Task 2 of the NEET 2 project Radiation Hardened Circuitry Using Mask-Programmable Analog Arrays. Using the analog blocks available in our currently pre-configured Via-Configured Array (VCA), we will perform a detailed schematic design of our system to include the signal-processing blocks for temperature, radiation and pressure. Control and data acquisition will be implemented with the Spartan-6 Field-Programmable Gate Array (FPGA) as well as wired serial communications with a remote computer to be used with this project. In addition, batteries and associated voltage regulators will be selected for powering the system. Fabrication may include a polyimide, printed circuit board (PCB) for improved radiation and temperature tolerance. The PCB will go through a layout process by one of the organizations we utilize for this function and be fabricated by another external vendor. Population of the system board will be performed using one of our in-house technicians or an outside vendor we commonly use. Five prototype systems (sensors, electronics board, battery power supply, and personal computer (PC) serial communications port) will be constructed to support the testing objectives of this work. Quality assurance will be performed at each step by both the submitters and the vendors.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Deformation mechanisms of nanotwinned Al

The objective of this project is to investigate the role of different types of layer interfaces on the formation of high density stacking fault (SF) in Al in Al/fcc multilayers, and understand the corresponding deformation mechanisms of the films. Stacking faults or twins can be intentionally introduced (via growth) into certain fcc metals with low stacking fault energy (such as Cu, Ag and 330 stainless steels) to achieve high strength, high ductility, superior thermal stability and good electrical conductivity. However it is still a major challenge to synthesize these types of defects into metals with high stacking fault energy, such as Al. Although deformation twins have been observed in some nanocrystalline Al powders by low temperature, high strain rate cryomilling or in Al at the edge of crack tip or indentation (with the assistance of high stress intensity factor), these deformation techniques typically introduce twins sporadically and the control of deformation twin density in Al is still not feasible. This project is designed to test the following hypotheses: (1) Certain type of layer interfaces may assist the formation of SF in Al, (2) Al with high density SF may have deformation mechanisms drastically different from those of coarse-grained Al and nanotwinned Cu. To test these hypotheses, we have performed the following tasks: (i) Investigate the influence of layer interfaces, stresses and deposition parameters on the formation and density of SF in Al. (ii) Understand the role of SF on the deformation behavior of Al. In situ nanoindentation experiments will be performed to probe deformation mechanisms in Al. The major findings related to the formation mechanism of twins and mechanical behavior of nanotwinned metals include the followings: 1) Our studies show that nanotwins can be introduced into metals with high stacking fault energy, in drastic contrast to the general anticipation. 2) We show two strategies that can effectively introduce growth twins in high-stacking-energy metals: use Ag as a template to introduce high density growth twins in epitaxial Al; and the film thickness is important in determination of volume fraction of growth twins. 3) We prove that high density twin boundaries can lead to significant work hardening capability in nanotwinned Al. We have published 13 articles, including Nature Communications, Nano Letters, and two review articles, one in Annual Review of Materials Research; and one in MRS Bulletin. Two postdocs and three graduate students have worked on the project. Two of them have become postdoc at Sandia National Laboratory and Los Alamos National Laboratory. One of the postdoc has become a faculty at a University. One patent has been filed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Novel ceramic capacitors with ultrahigh energy density and efficiency (Final Technical Report)

Antiferroelectric ceramics are a special class of material that have shown great potential as the dielectric in electrical capacitors due to their high energy- and power-density. During each charge-discharge cycle, the ceramic undergoes transformation to a ferroelectric phase and resumes its antiferroelectric phase. The hysteresis associated with the transitions leads to a mediocre energy efficiency and service lifetime of antiferroelectric capacitors and, hence, their almost absence in commercial products. Under the support of this research project, we first formulated a universal lattice-compatibility theory that included electrostatic polarization energy along with elastic energy and thermal energy to understand the origin of the hysteresis in antiferroelectric oxides. Guided by this compatibility theory, we conducted high-throughput density functional theory (DFT) calculations to assess chemical modifiers and their effect on crystal structures of 400+ PbZrO 3 -based compositions. Down-selected compositions were experimentally validated for their suppressed hysteresis and higher energy efficiency. The verified low-hysteresis compositions were then expanded to an antiferroelectric ceramic library with nearly 500 new compositions (more than 1,500 samples) using high-throughput experiments involving ceramic synthesis and property screening. The large quantity of data generated (theory and experimental) in these tasks were processed by machine-learning techniques and identified trends were fed to the next iteration. In the end, we successfully discovered four compositions with near-zero hysteresis, yielding a world-record energy efficiency of 98.2% at an energy density of 3.0 J/cm 3 . Furthermore, our antiferroelectric ceramic capacitor reaches 79.5 million charge-discharge cycles lifetime, a factor of 80 enhancement over previous antiferroelectric ceramics with large hysteresis. These research accomplishments have not only met the milestones set in the SOPO, but also led to two patent filings, three journal publications (one of them was in Advanced Materials, impact factor 29.4), and nine oral presentations at various venues. Through the course of the project, three postdocs, four Ph.D. students, and one M.S. student were trained. In short, our project established a new methodology in searching next-generation functional ceramics on the fundamental side and discovered several high-efficiency antiferroelectric compositions for capacitors on the applied side. Once fabricated into the multilayer form for commercial applications, these ceramic capacitors can potentially enable the high temperature high power density DC-link capacitors that are critical for the next generation inverters in electric vehicles. The project also significantly contributed to the nation’s workforce development in the STEM fields.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Application of distance correction to ChemCam laser-induced breakdown spectroscopy measurements

Laser-induced breakdown spectroscopy (LIBS) provides chemical information from atomic, ionic, and molecular emissions from which geochemical composition can be deciphered. Analysis of LIBS spectra in cases where targets are observed at different distances, as is the case for the ChemCam instrument on the Mars rover Curiosity, which performs analyses at distances between 2 and 7.4 m is not a simple task. Previously, we showed that spectral distance correction based on a proxy spectroscopic standard created from first-shot dust observations on Mars targets ameliorates the distance bias in multivariate-based elemental-composition predictions of laboratory data. In this work, we correct an expanded set of neutral and ionic spectral emissions for distance bias in the ChemCam data set. By using and testing different selection criteria to generate multiple proxy standards, we find a correction that minimizes the difference in spectral intensity measured at two different distances and increases spectral reproducibility. When the quantitative performance of distance correction is assessed, there is improvement for SiO 2 , Al 2 O 3 , CaO, FeOT, Na 2 O, K 2 O, that is, for most of the major rock forming elements, and for the total major-element weight percent predicted. But, for MgO the method does not provide improvements while for TiO 2 , it yields inconsistent results. Additionally, we observed that many emission lines do not behave consistently with distance, evidenced from laboratory analogue measurements and ChemCam data. This limits the effectiveness of the method.

36 MATERIALS SCIENCE↗