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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Tuning the Mechanical Properties of Shape Memory Metallic Glass Composites with Brick and Mortar Designs

We use molecular dynamics simulations to characterize the tensile deformation and failure of shape memory alloy-bulk metallic glass composites (BMGCs). As the failure in BMGCs shifts from the MG matrix to the second phase different strategies are required to further improve their mechanical properties. Results show that a staggered brick BMGC displays enhanced ductility with mild strength loss following a homogeneously distributed plasticity. This demonstrates that judiciously designed BMGCs can effectively synergize the mechanical properties of metallic glasses and shape memory alloys by effectively distributing stress and preventing the generation of critical shear bands and premature failure while preserving strength.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Dual Approach in Direct Ink Writing of Thermally Cured Shape Memory Rubber Toughened Epoxy

Bisphenol A-based epoxies are much used in a wide range of composite and coating applications due to their excellent thermomechanical properties. However, their 3D printability remains a challenge with most reported materials suffering from high brittleness and low toughness. In this work, we have described especially modified epoxy resins that enable 3D printing with both fast and slow curing rates. These materials exhibit greatly enhanced toughness, tunable thermomechanical properties, and excellent shape memory behavior. Two different printing systems, including a two-part static mixing printhead and a single extrusion printhead, were developed for fast- and slow-curing epoxies, respectively. The rheology of inks in both systems has been modified into printable thixotropic fluids with the aid of silica nanoparticles and other additives. Epoxide-functionalized telechelic polybutadiene was added into the resins, which are then introduced inside the epoxy network after cross-linking. The addition of polybutadiene rubber significantly improves the toughness (over 135%), fracture strain (over 200%), and shape memory behavior. By adding different amounts of the rubber telechelic, thermomechanical properties, including modulus, elongation, and Tg of epoxy, can be well controlled in a wide range to satisfy different applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Mechanisms enabling reconfigurability and long-term retention in vanadium oxide electrochemical memory

Phase coexistence in nanoscale electrochemical random-access memory (ECRAM) has recently been demonstrated to enable both information storage and extraordinary reconfigurability. These proof-of-principle demonstrations have left the mechanistic details of such a process unresolved. Particularly, the mechanisms that stabilize the multiple phases, and the underlying processes behind sustained memory retention, remain unclear, and are necessary to design such devices. Here we report microscale ECRAM devices composed of V⁢O𝑥, which enables us to directly probe the active region in an operando fashion using optical techniques. Using Raman mapping, we show the phase coexistence driven by the electrochemical injection of O vacancies to be spatially uniform (i.e., with no filaments). The stability was observed to be unusually long, with 1% loss over 14 years in ambient conditions. First-principles calculations of the oxygen vacancy formation energies in V⁢O 𝑥 further support the thermodynamic coexistence of multiple V⁢O 𝑥 phases and clarify the origin of the observed long-term retention in the ECRAM devices. Further, we demonstrate single devices that can be voltage programmed to exhibit synaptic, neuronal, and reconfigurable logic gate functionalities. Furthermore, we not only uncover the phase coexistence mechanism that may help device design, but also demonstrate the circuit-level applications of reconfigurability.

Electrical conductivity↗
DOE OSTI2025

Enhancing quantum memory lifetime with measurement-free local error correction and reinforcement learning

Reliable quantum computation requires systematic identification and correction of errors that occur and accumulate in quantum hardware. To diagnose and correct such errors, standard quantum error-correcting protocols utilize global error information across the system obtained by mid-circuit readout of ancillary qubits. We investigate circuit-level error-correcting protocols that are measurement-free and based on local error information. Such a local error correction (LEC) circuit consists of faulty multi-qubit gates to perform both syndrome extraction and ancilla-controlled error removal. We develop and implement a reinforcement learning framework that takes a fixed set of faulty gates as inputs and outputs an optimized LEC circuit. To evaluate this approach, we quantitatively characterize an extension of logical qubit lifetime by a noisy LEC circuit. For the two-dimensional (2D) classical Ising model and four-dimensional toric code, our optimized LEC circuit performs better at extending a memory lifetime compared with a conventional LEC circuit based on Toom's rule in a subthreshold gate error regime. We further show that such circuits can be used to reduce the rate of mid-circuit readouts to preserve a 2D toric code memory. Lastly, we discuss the application of the LEC protocol on dissipative preparation of quantum states with topological phases.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Quantifying Memory in Spin Glasses

Rejuvenation and memory, long considered the distinguishing features of spin glasses, have recently been proven to result from the growth of multiple length scales. This insight, enabled by simulations on the Janus II supercomputer, has opened the door to a quantitative analysis. We combine numerical simulations with comparable experiments to introduce two coefficients that quantify memory. A third coefficient has been recently presented by Freedberg et al. Here, we show that these coefficients are physically equivalent by studying their temperature and waiting-time dependence.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Compact Ferroelectric Programmable Majority Gate for Compute-in-Memory Applications

In this study, a compact and novel ferroelectric (FE) programmable majority gate is proposed and its novel application in Binary Neural Network (BNNs) is investigated. We demonstrate: i) by integrating N metal-ferroelectric-metal (MFM) capacitors on the gate of a transistor (1T-N-MFM structure), a nonvolatile and programmable majority (MAJ) gate that performs MAJ of AND between the gate input and polarization is realized; ii) validation the functionality of our 3-input MAJ of AND gate through comprehensive theoretical and experimental investigations; iii) a compact implementation of 3-input MAJ of XNOR gate that leverages only five of our 3-input MAJ of AND gates connected in parallel; iv) application of MAJ of XNOR gates to replace the XNOR gates and the first layer of the adder tree in the BNNs for up to 21x area saving on top of eliminating the energy-hungry memory accesses due to the compute-in-memory nature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

On the Feasibility of 1T Ferroelectric FET Memory Array

To fully exploit the ferroelectric field effect transistor (FeFET) as compact embedded nonvolatile memory for various computing and storage applications, it is desirable to use a single FeFET (1T) as a unit cell and arrange the cells into an array. However, many write mechanisms for an 1T FeFET array reported in the literature are yet to be validated experimentally. In this work, we performed a comprehensive experimental characterization on the write operations in an 1T- NOR and 1T- AND array using n-channel bulk FeFETs. We discovered that: 1) the source/drain contact can only supply minority carriers (i.e., electrons) to the channel for polarization screening during the low- V TH state programming; 2) the body contact can not only supply majority carriers (i.e., holes) for efficient high- V TH state write, but also depletion charge for low- V TH state programming, though with lower efficiency; and 3) during the low/high- V TH programming, only the path that can supply negative/positive screening charges, respectively, need to respond, which necessitates the application of proper write biases on the corresponding terminal. Based on the understanding of these write mechanisms, we show the importance of localized body contact or column-wise body contact for the successful high- V TH state programming. We also show that the previously proposed C- AND write scheme fails to program the FeFET to the low- V TH state for our devices. Finally, we propose several write schemes for both 1T- AND and 1T- NOR arrays for various scenarios providing insights for choosing the appropriate write scheme, which will facilitate the adoption of 1T FeFET memory arrays for emerging applications.

FeFET↗
DOE OSTI2025

Analog In-Memory Computing for the Synthetic Aperture Radar Polar Format Algorithm

As the utility of synthetic aperture radar (SAR) systems increases in autonomous vehicles, satellites, and other power- and space-constrained edge applications, there is a growing need for processors that can form SAR images at low power. In recent years, analog in-memory compute (AIMC) has shown immense promise for accelerating neural networks and other matrix-vector multiplication (MVM) heavy workloads at the edge. Here, in this work, we examine how the polar format algorithm (PFA), a popular SAR image formation algorithm, can be mapped to these AIMC systems. The PFA maps readily onto analog MVMs because it primarily consists of two linear operations: interpolation of frequency-domain data to a Cartesian grid, followed by a 2-D Fourier transform. This work presents two approaches to map the interpolation operation onto MVMs in analog hardware: a chirp transform and a modified form of sinc interpolation. These mappings introduce algorithmic errors, and their effect on the quality of SAR image formation is examined, both quantitatively and qualitatively. In addition, the impact of errors introduced by the analog hardware is explored to determine which approach is optimal under varying assumptions about the underlying analog memory devices and circuits.

Analog computing↗
DOE OSTI2023

Improving the Estimation of the Atmospheric Water Vapor Pressure Using Interpretable Long Short-Term Memory Networks: Dataset, Python code, and trained models

Atmospheric water vapor pressure is an essential meteorological control on land surface and hydrologic processes. It is not as frequently observed as other meteorologic conditions, but often inferred through the August–Roche–Magnus formula by simply assuming dew point and daily minimum temperatures are equivalent or by empirically correlating the two temperatures using an aridity correction. The performance of both methods varies considerably across different regions and during different time periods; obtaining consistently accurate estimates across space and time remains a great challenge. We applied an interpretable Long Short-Term Memory (iLSTM) network conditioned on static, location specific attributes to estimate daily vapor pressure for 83 FLUXNET sites in the United States and Canada. This data package includes all raw data of the 83 FLUXNET sites, input data for model training/validation/test, trained models and results, and python codes for the manuscript "Improving the Estimation of the Atmospheric Water Vapor Pressure Using an Interpretable Long Short-term Memory Network". Specifically, it consists of five parts. - First, "1_Daymet_data_83sites.zip" includes raw data downloaded from Daymet for the 83 sites used in the paper according to their longitude and latitude, in which vapor pressure is used. It also includes a pre-processed CSV data file combining all data from the 83 sites which is specifically used for the paper. - Second, "2_Fluxnet2015_data_83sites.zip" includes raw half hourly data of the 83 sites downloaded from FLUXNET2015 data portal, pre-processed daily data of the 83 sites, a CSV file including combined pre-processed daily data of the 83 sites, and a CSV file including the information (site ID, site name, latitude, longitude, data available period) of the 83 sites. - Third, "3_MODIS_LAI_data_83sites_raw.zip" includes raw leaf area index (LAI) data downloaded from the AppEEARs data portal. - Fourth, "4_Scripts.zip" includes all scripts related to model training and post-processing of a trained model, and a jupyter notebook showing an example for model post-processing. Two typo errors in files titled "run2get_args.py" and "postprocess.py" were corrected on March 27, 2024 to avoid confusions. - Finally, "Trained_models_and_results.zip" includes three folders and three files with suffix ".npy", and each folder corresponds to one file with suffix ".npy" with the same title. Each of the three folders include all trained models associated with one iLSTM model configuration (35 models for each configuration, details are described in the paper). Each file with suffix ".npy" includes the post-processed results of the corresponding 35 models under one iLSTM model configuration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A new era of ferroelectric thin films for nonvolatile memories

Ferroelectric films have potential applications in nonvolatile memory devices. In addition to the well-established perovskite-structure oxide ferroelectrics, hafnia- and wurtzite-based ferroelectrics have recently attracted considerable attention because of the improved scalability of the ferroelectric response (down to nanometer thicknesses), their compatibility with silicon fabrication processes, and the availability of deposition methods that realize three-dimensional structures. Furthermore, due to their high compatibility with silicon processes, these are also expected to be used in emerging energy-efficient applications, such as neuromorphic computing and reservoir computing. In this article, we review the fundamentals of hafnia- and wurtzite-based ferroelectrics and their advantages and issues for developing in nonvolatile memory devices. Then, possible emerging applications are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and in-device computing with spike-based neuromorphic architectures.

42 ENGINEERING↗
DOE OSTI2021

Nonvolatile Voltage Controlled Molecular Spin-State Switching for Memory Applications

Nonvolatile, molecular multiferroic devices have now been demonstrated, but it is worth giving some consideration to the issue of whether such devices could be a competitive alternative for solid-state nonvolatile memory. For the Fe (II) spin crossover complex [Fe{H2B(pz)2}2(bipy)], where pz = tris(pyrazol-1-yl)-borohydride and bipy = 2,2′-bipyridine, voltage-controlled isothermal changes in the electronic structure and spin state have been demonstrated and are accompanied by changes in conductance. Higher conductance is seen with [Fe{H2B(pz)2}2(bipy)] in the high spin state, while lower conductance occurs for the low spin state. Plausibly, there is the potential here for low-cost molecular solid-state memory because the essential molecular thin films are easily fabricated. However, successful device fabrication does not mean a device that has a practical value. Here, we discuss the progress and challenges yet facing the fabrication of molecular multiferroic devices, which could be considered competitive to silicon.

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

Nanoengineered Shape-Memory Hemostat

Uncontrolled hemorrhage is the predominant cause of preventable combat deaths. Various biomaterials serve as hemostatic agents due to their procoagulant or absorptive activity. However, these biomaterials often lack expansion capabilities, which severely limits use in noncompressible wounds. This study combines a hemostatic nanocomposite with a shape-memory polymer foam to design a composite material with both hemostatic and physical expansion properties. This composite is fabricated in two formulations: a foam externally coated in a highly concentrated nanocomposite (“coated composite”) and a foam containing a diluted nanocomposite infused throughout its pores (“infused composite”). Both formulations retain the shape-memory foam's expansion property. Further, the coated composite shows improved fluid uptake (>2-fold) versus infused composites or foam. The nanocomposite component dissociates from the foam under degradative conditions, with the foam remaining stable for 30 days. Hemostatic studies illustrate that the coated composite reduces the clotting time by ≈20%. Alternatively, the infused composite improves clotting over a larger distance (up to ≈2× distance from the composite). These results signify a modular hemostatic ability: the coated composite reduces clotting and improves fluid uptake, while the infused composite achieves diffuse clotting and maintains mechanical properties. Thus, these materials pose a strong potential for use in noncompressible wounds.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Toward memory-efficient melt pool monitoring: a classification framework using event-based imaging and sparse sensing technique

Vision sensors like CMOS and CCD cameras are often used for in-process monitoring of melt pools in laser-based additive and welding processes, but they require transferring large amounts of data and computational processing resources. Event-based neuromorphic imagery, on the other hand, detects only the change in pixel intensity, thus potentially reducing the data amount and latency. With an event imager, this study develops a framework for melt pool condition classification, including image construction, time scale selection, optimal pixel selection, and sparse classification, to achieve a highly memory-efficient scheme. These are based on sparse sensing techniques with singular value decomposition (SVD) and QR pivoting, the two fundamental matrix transformations for linear dimensionality reduction. The framework is then validated by classifying a controlled experiment by exciting various mode shapes of liquid gallium pools of varying depths (3, 6, and 8 mm). At 200 pixels, the classifier can reach overall accuracy of 75%, while at 2000 pixels (0.013% of the total possible pixels), the accuracy is nearly 90% (89.86%). At the same number of pixels, random selection can only achieve 46% and 67%, respectively. The memory savings of the sparsely sampled event data compared to a conventional imager is about 500 times. In addition to performance, implementation and limitations of the framework are also discussed.

42 ENGINEERING↗
DOE OSTI2026

Design, characterization and shape recovery behavior of 3D/4D printed shape memory polymers (SMPs)

Shape memory polymers (SMPs) represent a paradigm shift in material science, uniquely capable of undergoing reversible shape transformations triggered by external stimuli, positioning them as pivotal in developing next-generation biomedical devices, aerospace components, and adaptive structures. Extensive research has been done on SMPs with a major focus on high-temperature programming methods, which can limit energy efficiency and applicability with temperature-sensitive materials. Additionally, while various SMP blends have demonstrated great potential, limited work has been done on the suitability for 3D printing these materials, particularly under high-strain and ambient temperature programming conditions. In this study, a three-component optimized SMP composition was evaluated by 3D printing via the Material Extrusion (MEX) technique and investigating its ambient temperature-programming behavior at high strains. The SMP formulation studied was a tailored blend of thermoplastic polyurethane (TPU), polycaprolactone (PCL), and an octadecane diol-based copolymer (OBC) that exhibits robust shape memory behavior, high strain tolerance, and efficient force generation. Rigorous thermal, mechanical, and shape recovery analyses, along with optimized printing parameters and consistent shape recovery rates of up to 90%, were achieved under dynamic mechanical analysis (DMA), even under ambient programming conditions. This work demonstrates the SMP composition’s potential for adaptive, self-deployable systems with 4D printing characteristics ideal for bio-inspired structures and artificial muscle fibers.

Sudan, Kavish [University of Louisville, KY]↗
DOE OSTI2022

Memory-efficient emulation of physical tabular data using quadtree decomposition

Computationally expensive functions are sometimes replaced in simulations with an emulator that approx-imates the true function (e.g., equations of state, wavelength-dependent opacity, or composition-dependent materials properties). For functions that have a constrained domain of interest, this can be done by discretizing the domain and performing a local interpolation on the tabulated function values of each local domain. For these so-called tabular data methods, the method of discretizing the domain and mapping the input space to each subdomain can drastically influence the memory and computational costs of the emulator. This is especially true for functions that vary drastically in different regions. We present a method for domain discretization and mapping that utilizes quadtrees, which results in significant reductions in the size of the emulator with minimal increases to computational costs or loss of global accuracy. We apply our method to the electron-positron Helmholtz free energy equation of state and show over an order of magnitude reduction in memory costs for reasonable levels of numerical accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Relaxor Ferroelectric-Like Spatiotemporal Memory in Field-Driven Lipid Bilayers

Lipid membranes are often regarded as passive barriers, yet their nonlinear dielectric response remains poorly understood. Using all-atom molecular dynamics, we show that fully hydrated dipalmitoylphosphatidylcholine bilayers exhibit relaxor ferroelectric-like behavior under time-dependent electric fields. Unlike crystalline relaxors, which are bipolar and display little remanent polarization, lipid bilayers exhibit a unipolar polarization response: even an alternating current field produces persistent, asymmetric polarization. Furthermore, the underlying free-energy landscape contains two distinct minima, a nonpolarized state and a unipolarly polarized state, between which stochastic thermally activated transitions occur. Directionally resolved Van Hove analysis reveals pronounced anisotropy arising from out-of-plane electric dipole alignment, interleaflet coupling, and lateral polarization domains. Each field cycle nucleates polarization at distinct sites and monitors their relaxation, marking a crossover from thermal fluctuations to field-sustained polarization. Remarkably, these polarized domains persist after field removal, generating long-lived, spatially coherent dipolar patterns that encode nanoscale polarization memory. Potassium chloride amplifies these effects via dielectric screening and a modified hydration structure, enhancing electric dipole flexibility and cooperativity. Together, these results establish protein-free bilayers as nonlinear, history-dependent dielectrics capable of sustaining field-tunable electromechanical coupling, providing an emergent physical foundation for nanoscale information storage and memory phenomena reminiscent of short- and long-term plasticity in soft neuromorphic systems.

Insulators↗
DOE OSTI2022

On-Surface Single-Molecule Identification of Mass-Selected Cyclodextrin-Supported Polyoxovanadates for Multistate Resistive-Switching Memory Applications

Polyoxovanadate anions immobilized on conducting substrates are promising candidates for molecule-based memory technologies. However, strong electronic interactions with the substrate and the formation of larger agglomerates remain bottlenecks on the way to their technical application. We demonstrate that ion soft-landing of mass selected cyclodextrin- functionalized hexavanadates enables to deposit these host-guest complexes as single memory units on surfaces without their quaternary ammonium countercations from solution. The cyclodextrin provides a stabilizing “shell” for the dianions. Further, multistate switching evidenced by the change in molecular resistance has been detected using scanning tunneling microscopy and spectroscopy and attributed to reduction of individual vanadium (+V) centers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗