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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Visualizing Crystallization Dynamics and Transformation Pathways of Disordered Rocksalt Oxides During Thermally Activated Sol–Gel Synthesis

Sol–gel synthesis is a wet-chemical processing route for fabricating functional materials with control over composition and microstructure at relatively low temperatures compared to conventional solid-state synthesis. While sol–gel process initiates with intermixed molecular precursors, the early-stage nucleation pathways are insufficiently understood. Here, in this study, the chemical and structural transformation of ion disordered rocksalt (DRX) Li 1.2 Mn 0.4 Ti 0.4 O 2 (LMTO), a promising cathode material for lithium batteries, is studied by multiscale characterizations. In situ heating transmission electron microscopy (TEM) using a liquid cell visualizes and identifies crystallization pathways at the nanoscale. While some regions follow a classical multi-step transition through thermodynamically stable intermediates, others exhibit a kinetic shortcut via a localized amorphous matrix to directly form the DRX structure. Macroscale Fourier transform infrared spectroscopy corroborates the findings and reveals that transition metal ions are more strongly incorporated into the acetate-coordinated network than lithium. Although in situ heating TEM captures diverse local transformation pathways, in situ synchrotron X-ray diffraction indicates that the macroscopic transformation proceeds predominantly through spinel LMTO and lithium titanates toward DRX-LMTO. The findings uncover the spatiotemporal chemical and structural transformations in sol–gel derived DRX-LMTO materials, and call for fine-tuning of such sol–gel chemistries to manipulate the crystallization pathways and achieve target material homogeneity more efficiently.

cathode material↗
DOE OSTI2023

Technology Maturation of Wireless Harsh Environment Sensors For Improved Condition Based Monitoring Of Coal Fired Power Generation

The overall goal of this project was to demonstrate and develop the usage of high-temperature (HT) harsh-environment (HE) wireless surface acoustic wave resonator (SAWR) sensor technology to promote reliable maintenance through condition-based maintenance (CBM) for field applications in harsh service conditions associated with power plant environments. The project aimed to advance the HT HE wireless SAWR sensor technology from TRL 5 to TRL 7. In addition to HT HE wireless temperature sensing, efforts were dedicated during this project to investigate, develop and increase the TRL from 3 to 5 for the following technologies: (a) HT HE strain sensors to address additional CBM monitoring needs, such as boiler tube mechanical / thermal stresses, which can provide early indications for boiler tube cracking and failure; and (b) HT aluminum nitride (AlN) and scandium aluminum nitride (ScAlN) based piezoelectric thin film fabrication and implementation of SAW sensors, with the goal of releasing the need to use single crystal piezoelectric materials for SAWRs and thus broaden possible technology applications to non-planar and harder to modify surfaces. To achieve the goals mentioned above, UMaine and its partner, Environetix Technologies Corporation, established partnerships with the following power plants: Longview Power (Maidsville, WV), a coal-fired power plant; Penobscot Energy Recovery Corp (PERC, Orrington, ME), a waste-to-energy power plant; and the UMaine Steam Plant (Orono, ME), an oil / natural gas power plant. To realize wireless HT HE SAWR sensor systems in these harsh service conditions, the University of Maine research team worked with Environetix and these power plants to define, design, fabricate, test and validate a mature prototype wireless temperature SAWR sensor system for boiler tube applications within the HT HE of the reheater pass damper chamber to directly and wirelessly monitor the temperature at eighteen independent boiler tube locations. The system included three levels, or “tiers”, of wireless communication to enable remote monitoring: Tier 1, the wireless link in the reheater pass damper chamber directly accessing the sensors on the boilers; Tier 2, the wireless local area network link, transmitting processed sensor information within the power plant to the Tier 3, a commercial wireless signal carrier company for secure remote data monitoring outside of the power plant. Regarding the wireless sensor system installed at Longview Power, temperature information from the boilers was continuously transmitted from the Longview boilers at Maidsville, WV, to Environetix headquarters, Orono, ME, over a 34 month period, when the system was finally decommissioned. Strain sensors and piezoelectric ScAlN thin film sensors were successfully installed on the exhaust duct at the UMaine Steam Power plant. The advances in wireless strain sensors and thin film piezoelectric film fabrication and testing were performed mostly in UMaine laboratories and field tested at the UMaine Steam Plant, due to its close proximity to UMaine/Environetix, access to the plant facility, and due to difficulties in accessing the other power plants during the COVID shut-down period. The project accomplished the TRL level increase of the targeted CBM technologies through the successful fabrication, installation, test, and validation of dedicated and commercial wireless sensor systems, utilizing the three different power plants. The outcomes of this project, including the wireless sensor data capability, are expected to yield an advance for CBM in harsh power plant environments. The reduction of maintenance costs, improved safety during plant operation, and increased power plant efficiency will lead to increased revenues (i.e., fewer forced outages) due to better process monitoring enabled by the wireless HT HE SAWR temperature sensor technology.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Epoxy-based vitrimeric semi-interpenetrating network/MXene nanocomposites for hydrogen gas barrier applications

Herein, we report MXene-filled epoxy-based vitrimeric nanocomposites featuring a semi-interpenetrating network (S-IPN) to develop a hydrogen gas (H 2 ) barrier coating with self-healing characteristics for compressed H 2 storage applications. The reversible epoxy network was formed by synthesizing linear epoxy chains with pendent bis-hydroxyl groups using amino diol, which were then crosslinked with 1,4-benzenediboronic acid to generate dynamic boronic ester linkages. To achieve the S-IPN-type molecular arrangement, the epoxy chains were in situ crosslinked in the presence of poly(ethylene-co-vinyl alcohol) (EVOH), giving rise to a self-healing network (EEP) with a healing efficiency of 87%. Into the S-IPN vitrimer (EEP), a 2D platelet-type nanofiller MXene was incorporated to introduce a tortuous path for H 2 gas diffusion along with improved mechanical properties. The nanocomposite coating was applied to nylon 6 liner material, which is conventionally used in all-composite H 2 storage vessels. Further, the application of a 2 wt% MXene/EEP nanocomposite coating showed a permeability coefficient of 0.062 cm 3 mm m -2 d -1 atm -1 exhibiting ~96% reduction in gas permeability compared to uncoated nylon 6. The same nanocomposite exhibited a healing efficiency of 79%. Increasing the MXene loading to 10 wt% further reduced the permeability coefficient to 0.002 cm 3 mm m -2 d -1 atm -1 ; however, the healing efficiency decreased due to restricted chain mobility. In essence, the current work highlights the potential of vitrimeric S-IPN nanocomposite coatings for H 2 gas-barrier applications, enhancing safety and performance.

42 ENGINEERING↗
DOE OSTI2021

Two-dimensional overdamped fluctuations of the soft perovskite lattice in CsPbBr 3

Lead halide perovskites exhibit structural instabilities and large atomic fluctuations thought to impact their optical and thermal properties, yet detailed structural and temporal correlations of their atomic motions remain poorly understood. Here, these correlations are resolved in CsPbBr 3 crystals using momentum-resolved neutron and X-ray scattering measurements as a function of temperature, complemented with first-principles simulations. We uncover a striking network of diffuse scattering rods, arising from the liquid-like damping of low-energy Br-dominated phonons, reproduced in our simulations of the anharmonic phonon self-energy. Here, these overdamped modes cover a continuum of wave vectors along the edges of the cubic Brillouin zone, corresponding to two-dimensional sheets of correlated rotations in real space, and could represent precursors to proposed two-dimensional polarons. Further, these motions directly impact the electronic gap edge states, linking soft anharmonic lattice dynamics and optoelectronic properties. These results provide insights into the highly unusual atomic dynamics of halide perovskites, relevant to further optimization of their optical and thermal properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Emerging applications: Neuromorphic computing and reservoir computing

The emergence of doped hafnium oxide (HfO 2 )-based ferroelectric films has enabled highly scalable and silicon-compatible ferroelectric devices, opening new frontiers in neuromorphic and reservoir computing. Among these, ferroelectric field-effect transistors (FeFETs) are particularly promising due to their analog memory characteristics and unique polarization dynamics. These properties make FeFETs ideal candidates for artificial synapses in neuromorphic architectures, supporting deep neural networks and spiking neural networks based on leaky-integrate-and-fire (LIF) mechanisms. Beyond neuromorphic computing, FeFETs also play a crucial role in physical reservoir computing, leveraging their intrinsic nonlinear and history-dependent behavior for efficient real-time learning. This approach offers significant advantages for time-series processing and edge artificial intelligence (AI) applications, addressing the growing need for energy-efficient computing. As a result, this article explores the principles, key demonstrations, and future potential of FeFET-based neuromorphic and reservoir computing, highlighting their impact on next-generation AI hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2022

LaserNetUS Collaboration Network—University of Rochester (Final Report)

LaserNetUS Collaborative Network established in 2018 is a network of high-power laser facilities supported by the Department of Energy (DOE) Office of Fusion Energy Sciences (FES) and operating effectively as a user facility. Its mission is to advance and promote intense laser science and applications by providing scientists and students with broad access to unique facilities and enabling technologies, advancing the frontiers of laser-science research, and fostering collaboration among researchers and networks from around the world. Users who submit proposals through an annual call are selected by an external and independent proposal review panel (PRP) not involving personnel from any of the facilities. Besides the Omega Laser Facility at the University of Rochester’s Laboratory for Laser Energetics (UR/LLE), the network during this project period includes high-intensity laser facilities from six other universities and three national laboratories, namely, the Colorado State University (CSU), the University of Michigan (UM), the University of Nebraska at Lincoln (UNL), The Ohio State University (OSU), Université du Québec, the University of Texas at Austin (UT Austin), Lawrence Berkeley National Laboratory (LBNL), SLAC National Accelerator Laboratory (SLAC) and Lawrence Livermore National Laboratory (LLNL), respectively. The network facilities span a wide range in laser pulse energy, pulse duration, repetition rate, and experimental diagnostic equipment enabling innovative research in a variety of exciting areas. Details of the LaserNetUS facilities, organization and committees, events, and accomplishments can be found at the network website (https://lasernetus.org/). A very important role that the LaserNetUS fulfills is the training of students and young scientists who will be key for the future development of laser-plasma science and high-power laser technology itself. The network provides these students not only with access to the most advanced instrumentation and laser facilities, but also the opportunities to interact and collaborate with students from other institutions and with a large group of experienced scientists. As the largest university-based laser users’ facility in the world, the Omega Laser Facility at the UR/LLE has served the high-energy-density physics (HEDP) and inertial fusion science community for nearly 40 years. The multi-beam multi-kJ OMEGA EP Laser System brings unique capabilities to the LaserNetUS network. The combination of high intensity and high energy in short- and long-pulse operation together with solid or gas-jet targets and externally applied magnetic fields provides users a wide domain of experimental conditions. This award provides a total of eight shot days on OMEGA EP for LaserNetUS users. During the award period of performance (June 2019–November 2021), seven teams have fully utilized the eight shot days for their unique science experiments on OMEGA EP with a total of 83 target shots. These experiments involve 13 graduate students, two undergraduate students and six postdoctoral researchers. Results have been widely disseminated at international conferences including LaserNetUS annual meeting (~20 presentations including three invited), and in peer-reviewed journal publications (three published with several manuscripts in preparation).

36 MATERIALS SCIENCE↗
NASA NTRS2016

Formation and Recondensation of Complex Organic Molecules During Protostellar Luminosity Outbursts

During the formation of stars, the accretion of surrounding material toward the central object is thought to undergo strong luminosity outbursts followed by long periods of relative quiescence, even at the early stages of star formation when the protostar is still embedded in a large envelope. We investigated the gas-phase formation and recondensation of the complex organic molecules (COMs) di-methyl ether and methyl formate, induced by sudden ice evaporation processes occurring during luminosity outbursts of different amplitudes in protostellar envelopes. For this purpose, we updated a gas-phase chemical network forming COMs in which ammonia plays a key role. The model calculations presented here demonstrate that ion-molecule reactions alone could account for the observed presence of di-methyl ether and methyl formate in a large fraction of protostellar cores without recourse to grain-surface chemistry, although they depend on uncertain ice abundances and gas-phase reaction branching ratios. In spite of the short outburst timescales of about 100 years, abundance ratios of the considered species higher than 10% with respect to methanol are predicted during outbursts due to their low binding energies relative to water and methanol which delay their recondensation during cooling. Although the current luminosity of most embedded protostars would be too low to produce complex organics in the hot-core regions that are observable with current sub-millimetric interferometers, previous luminosity outburst events would induce the formation of COMs in extended regions of protostellar envelopes with sizes increasing by up to one order of magnitude.

astrochemistry↗
DOE OSTI2021

Comparative Studies of the Structural and Transport Properties of Molten Salt FLiNaK Using the Machine-Learned Neural Network and Reparametrized Classical Forcefields

Despite surging interest in molten salt reactors and thermal storage systems, knowledge of the physicochemical properties of molten salts are still inadequate due to demanding experiments that require high temperature, impurity control, and corrosion mitigation. Therefore, the ability to predict these properties for molten salts from first-principles computations is urgently needed. Herein, we developed and compared a machine-learned neural network force field (NNFF) and a reparametrized rigid ion model (RIM) for a prototypical molten salt LiF–NaF–KF (FLiNaK). We found that NNFF was able to reproduce both the structural and transport properties of the molten salt with first-principles accuracy and classical-MD computational efficiency. Furthermore, the correlation between the local atomic structures and the dynamics was identified by comparing with RIMs, suggesting the significance of polarization of anions implicitly embedded in the NNFF. Furthermore, this work demonstrated a computational framework that can facilitate the screening of molten salts with different chemical compositions, impurities, and additives, and at different thermodynamic conditions suitable for the next-generation nuclear reactors and thermal energy storage facilities.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mechanical Behavior of Hybrids with Hyper-Connected Molecular Networks

Our research is focused on fundamental studies related to the molecular design, synthesis, characterization and modeling of molecular-reinforced hybrid glass films for superior mechanical and fracture properties. Molecular-reinforced hybrid glass films exhibit unique electro-optical properties while maintaining excellent thermal stability. They have important technological application for emerging nanoscience and energy technologies including anti-reflective and ultra-barrier layers in photovoltaics, display technologies, as well as quantum technologies. However, they are often inherently brittle in nature, do not adhere well to adjacent substrates, and exhibit poor mechanical properties. Furthermore, a robust understanding of the underlying mechanisms of deformation, fracture and fatigue has been lacking particularly given their mechanically fragile nature. The related lack of structure-property relationships between molecular structure, mechanical properties and processing represents a fundamental challenge to their integration into device technologies, compromises their thermomechanical reliability and has thwarted efforts to improve mechanical and, particularly, fracture properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Design of functional materials based on new principles of disorder. Final Report

Disorder is present to some extent in every material. The disorder can be sparse and localized – individual defects – or globally such as in a glass. Normally disorder is considered to be detrimental to function. However, this is not necessarily always the case. This project addressed the question: How does disorder provide new potentialities for the behavior of a material that would be absent in a crystalline material of the same composition? We have discovered a new principle for disordered matter: Independence of bond-level response. This work has introduced the possibility of creating new classes of mechanical meta-materials with applications everywhere from microscopic biological molecules (allostery) to large scale architectural disordered networks. During the course of this grant, we have subsequently elaborated on these novel ideas about bond-level independence of properties in disordered materials. In particular, this study has required that we understand the limits of disorder. That is, if crystals are perfect order, is there another diametrically opposed pole which is the most disordered state? We have shown that, in a well-defined way, jamming is one extreme disordered pole of rigid matter. Jamming is a far-from-equilibrium way of creating a rigid solid by rapidly quenching a random distribution of particles to a mechanically stable system at zero temperature. To understand the jammed state of matter, we have elucidated the low-frequency excitation of these systems. This has allowed a new way of understanding materials that cannot be easily classified as crystals or glasses. These ideas can be extended to address the mechanical properties of polycrystalline matter. Memory formation in matter can take inspiration from memory that occurs in a biological context. The issue of memory formation raises another set of questions at the heart of how the energy landscape is organized and how it can be manipulated to store information in a solid. This project has explored how to understand the degree to which multiple memories can be stored in a single system and has created a holistic understanding of how inputs can be used to create a desired response.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of an Enhanced Radiation Physics Toolset for Modeling Spectral and Imaging Signatures in the Warm Dense Matter Experiments

Radiative and atomic processes in plasmas play a critical role in a wide variety of high energy density laboratory plasma (HEDLP) experiments. The emission, absorption, and transport of radiation can strongly affect the overall energetics and evolution of such plasmas. In addition, radiation-based diagnostics – including imaging, spectroscopy, and absolute flux measurements – are widely used to determine key features of HEDLPs. To advance our understanding of HEDLP science, it is vital to have high-fidelity computational physics tools that have well-tested radiation physics modeling, and that are readily accessible to researchers in the HEDLP community. Simulations play an extremely important role for planning and designing the experiments, as well as for post-experiment data analysis. Prism Computational Sciences develops software that is used by National Laboratories and universities (including five members of LaserNetUS network). Prominent examples of such research efforts include z-pinch and short-pulse laser experiments designed to study the basic physics of photoionized plasmas and photoionization fronts, as well as their application to astrophysical plasmas. The main effort was dedicated to the development of non-equilibrium equation-of-state (EOS) models within the HELIOS-CR code, a hydrodynamics code with inline collisional-radiative atomic kinetics. Gas cell experiments on Z and Omega demonstrated the importance of non-equilibrium effects on atomic kinetics in photoionized plasmas. Recent proof-of-principle experiments on Omega EP confirmed the advantages of using a short-pulse laser to create an intense radiation drive, leading to additional experiments being proposed. Photoionization front experiments at LLE also emphasizes the importance of radiation and atomic physics. In both studies, HELIOS-CR simulations played a crucial role in computing non-equilibrium opacities and ionization distributions. A newly developed non-LTE EOS model will help addressing possible non-equilibrium effects, on for example specific heat, and their influence in plasma evolution. Prism also implemented support for open-source atomic data generated by the Flexible Atomic Code. This allows researchers to generate custom atomic tables and use them within the complex framework of simulation tools developed by Prism. The ability to use open-source atomic data would be extremely valuable for hydrodynamics and spectroscopic simulations that include high-Z materials, e.g., picosecond x-ray pulse generation experiments. Support for new atomic structures was fully implemented, and the data can be used by all simulation tools developed at Prism: radiation-hydrodynamics, imaging and spectroscopy, EOS and opacity. The development resulted in a significant fidelity enhancement to the simulations tools developed by Prism that are currently used in other cutting-edge experiments including: opacity measurement experiments performed to both understand the basic radiative and atomic properties of plasmas as well as provide data for more accurately modeling the internal structure of the Sun and other stars, high-intensity short-pulse laser experiments performed to develop short-wavelength light sources for use as backlighters and to investigate fast ignition concepts for inertial fusion energy; capsule implosion experiments designed to develop inertial fusion as an energy source, etc.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Machine Learning‐Guided Discovery of High‐Entropy Perovskite Oxide Electrocatalysts via Oxygen Vacancy Engineering

Abstract High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

Chemistry↗
DOE OSTI2025

High‐Speed Slot‐Die Coating with Donor‐Priority Rapid Aggregation Kinetics for Improved Morphology and Efficiency in Ecofriendly Organic Solar Cells

Abstract Solution‐processable organic solar cells (OSCs) represent a promising renewable photovoltaic technology with significant potential for eco‐compatible production. While high power conversion efficiencies (PCEs) have been achieved in OSCs, scaling this technology for high‐throughput manufacturing remains challenging. Key reason lies in the lack of efficient control strategies for the complex and long‐duration morphology evolution during high‐speed coating process with ecofriendly solvents. Here, a donor‐priority rapid aggregation process (DP‐RAP) scheme is proposed to solve this issue by adjusting the aggregation kinetics of donor and acceptor components. DP‐RAP enables blends with a nanoscale fiber network structure and favorable crystallinity, which contributes to balanced carrier transport and reduced recombination losses. As a result, the PCE is improved from 14.3% (reference) to 17.4% (DP‐RAP) for ultra‐high speed coated PM6:BTP‐eC9 devices in atmosphere, which is one of the highest values for non‐halogenated solvent‐processed solar cells at coating speeds of 500 mm s −1 . Moreover, the DP‐RAP based devices remain a stable PCE of approximately 17.4% across a broad range of coating speeds (20–500 mm s −1 ), illustrating its tolerance to the varied manufacturing conditions. This work highlights a promising avenue for the high‐speed, ecofriendly production of efficient OSCs, pushing the boundaries of practical manufacturing in renewable energy technologies.

Chemistry↗
DOE OSTI2024

Self-Consistent Theory for Structural Relaxation, Dynamic Bond Exchange Times, and the Glass Transition in Polymeric Vitrimers

Here, we formulate a statistical mechanical theory for how dynamic bond exchange influences the activated hopping-driven relaxation of Kuhn segments in dynamically cross-linked networks or vitrimers over a wide range of temperatures and cross-link densities. The key new methodological aspect is to address in a self-consistent manner the dynamic consequences of bond exchange on the Kuhn segmental alpha relaxation, and vice versa. The predicted temperature dependence of the segmental alpha time of vitrimers at high temperatures remains the same as that of permanent networks, but at lower temperatures, a significant acceleration of relaxation occurs due to bond exchanges. From a mechanistic perspective, the vitrimer local cage barrier is very weakly affected by bond exchange, while the collective elastic barrier contribution decreases significantly in the deeply supercooled regime. The vitrimer glass transition temperature is predicted to grow linearly with the square root of the cross-link density, as previously found for permanent networks. Material-specific chemical effects such as cross-linker size relative to that of the normal Kuhn segment or special attraction of a cross-linker with polymers are crudely considered based on model calculations. The theory is quantitatively applied to recent experiments on dry ethylene vitrimers. Good agreements are found including that the bond exchange time follows an Arrhenius law at high enough temperatures but upward non-Arrhenius deviations emerge in the deeply supercooled regime, and a collapsed master curve of the segment alpha time exists over a wide range of cross-link densities and temperatures. Possible extensions to treat dynamic heterogeneity effects and penetrant transport in vitrimers are briefly discussed.

chemical calculations↗
DOE OSTI2021

Formation and surface melting of nanoparticle superlattices in a solution

The wisdom in the saying of “There are no two snowflakes alike” lies in the importance of history or kinetic pathways in the phase transitions of solids. Likewise, “artificial solids,” namely superlattices consisting of functional nanoparticles, have lattice size, surface morphology, crystallinity, symmetry, and structural reconfiguration (for example, transition into a disordered state) highly dependent on the kinetic pathways as the nanoparticles interact with each other in solution [1]. Great progresses have been made in understanding the formation pathways of superlattices using liquid-phase transmission electron microscopy (TEM) [2-4]. For example, by tracking single nanoparticle’s trajectories, especially aided by U-net neural network-based machine learning, previous studies mapped the fundamental nanoparticle interactions at nanometer resolution [5]. Nonclassical, two-step nucleation pathway has also been elucidated in the system of nanoprisms, by optimizing protocols such as loading nanoparticle suspensions over the supersaturation threshold and minimizing particle‒substrate interaction [2]. Surface morphologies or exposed facets of superlattices have been shown to follow the principles of Wulff construction rule, where the facet-dependent surface energy can be measured based on the capillary wave theory [4]. However, the reverse process of crystallization of superlattices, the conversion from crystalline to disordered state, has been much less explored. On one hand, the melting of nanoparticle superlattices can provide a preferred pathway to induce structural reorganization or shuffling of building blocks for them to transform into different types of crystal structures. On the other hand, understanding nanoscale superlattice melting and comparing such behaviors with the prevailing surface melting theories developed for atomic/molecular solids can provide a potent way to engineer phase transitions of supra- and hierarchical structures constructed from nanoscale entities (e.g., DNA-coated nanoparticles, proteins), for their applications in reprogrammable and switchable materials with multifunctional properties [6, 7]. The experimental challenges to observe melting of superlattices are twofold. Practically it is difficult to load the initial superlattice form, in an intact manner, into the highly confined liquid-phase TEM chamber for in-situ observation. Here, the triggering of melting also needs meticulous manipulation of nanoparticle concentration, interparticle interaction, and solution environment.

Kim, Ahyoung↗
DOE OSTI2022

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A practical guide to characterizing irradiated nuclear fuels using FIB tomography

We report focused ion beam (FIB) tomography with combined electron backscatter diffraction (EBSD) and energy dispersive x-ray spectroscopy (EDS) is a technique capable of statistically characterizing the microstructure and spatial compositional variation of nuclear fuel in three-dimensions (3D). The 3D visualization from FIB tomography provides a comprehensive picture of the interconnected microstructural and compositional features that can impact fuel performance. While these features are often characterized with surface examination, the complexity and relationship of fission products and grain boundary networks may not fully be captured by these 2D methods. This work presents a practical guide to FIB tomography that is tailored to nuclear fuel characterization. The steps used to collect and process the data are provided along with the scripts used to process the data. Additionally, suggestions for future characterization efforts utilizing this approach are given.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Preferential hole defect formation in monolayer W Se 2 by electron-beam irradiation

Monolayer transition-metal dichalcogenides (TMDCs) have been extensively studied due to their wide range of physical properties and applications. It has been demonstrated that the electron beam in a transmission electron microscope (TEM) or scanning TEM (STEM) generates chalcogen vacancies that agglomerate into dispersed linelike or holelike defects. Here we employ a STEM electron beam and demonstrate that, in WSe 2 , beam-induced chalcogen vacancies initially form certain dispersed multivacancy structures, as seen in TMDCs in prior work. However, with suitable control of the STEM focused electron beam, these multivacancies gradually evolve into a dense network of ten-, 12-, 14-, and 16-member ring round holes, whereas the same process leads predominantly to chalcogen-vacancy line defects in other trigonal-prismatic TMDCs. Density functional theory calculations find that the underlying atomic-scale processes lead preferentially to defect structures that lower the total energy so that we are able to track the formation of the observed multivacancy complexes, which then lead to the formation of dense large round holes in WSe 2 . The same processes in WS 2 , however, are energetically unfavorable, while linear multivacancy defects are preferred, as observed. The demonstrated control of the formation of unique high-density round holes in WSe 2 has potential for applications such as atomic and molecular sieving.

36 MATERIALS SCIENCE↗