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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Influences of granular constraints and surface effects on the heterogeneity of elastic, superelastic, and plastic responses of polycrystalline shape memory alloys

Deformation heterogeneities at the microstructural length-scale developed in polycrystalline shape memory alloys (SMAs) during superelastic loading are studied using both experiments and simulations. In situ X-ray diffraction, specifically the far-field high energy diffraction microscopy (ff-HEDM) technique, was used to non-destructively measure the grain-averaged statistics of position, crystal orientation, elastic strain tensor, and volume for hundreds of austenite grains in a superelastically loaded nickel-titanium (NiTi) SMA. These experimental data were also used to create a synthetic microstructure within a finite element model. The development of intragranular stresses were then simulated during tensile loading of the model using anisotropic elasticity. Driving forces for phase transformation and slip were calculated from these stresses. The grain-average responses of individual austenite crystals examined before and after multiple stress-induced transformation events showed that grains in the specimen interior carry more axial stress than the surface grains as the superelastic response "shakes down". Examination of the heterogeneity within individual grains showed that regions near grain boundaries exhibit larger stress variation compared to the grain interiors. As a result, this intragranular heterogeneity is more strongly driven by the constraints of neighboring grains than the initial stress state and orientation of the individual grains.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Here, data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.

42 ENGINEERING↗
DOE OSTI2016

Annealing effects on the structural and magnetic properties of off-stoichiometric Fe-Mn-Ga ferromagnetic shape memory alloys

Annealing plays an important role in modifying structures and properties of ferromagnetic shape memory alloys (FSMAs). The annealing effect on the structures and magnetic properties of off-stoichiometric Fe 45 Mn 26 Ga 29 FSMA has been investigated at different elevated temperatures. Rietveld refinements of neutron diffraction patterns display that the formation of the γ phase in Fe 45 Mn 26 Ga 29 annealed at 1073 K increases the martensitic transformation temperature and reduces the thermal hysteresis in comparison to the homogenized sample. The phase segregation of a Fe-rich cubic phase and a Ga-rich cubic phase occurs at the annealing temperature of 773 K. The atomic occupancies of the alloys are determined thanks to the neutron's capability of differentiating transition metals. The annealing effects at different temperatures introduce a different magnetic characteristic that is associated with distinctive structural changes in the crystal.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Backpropagation-based learning with local derivative approximation and memory replay in biologically plausible neural systems

When learning, the brain modifies individual synaptic connections to reach a desired behavior. Animal and human brains have been shown to be incredibly capable of learning complex and varied functions across a wide variety of tasks. In recent years, artificial neural networks, inspired by human and animal brains, have shown great capabilities in learning a wide variety of difficult tasks. However, artificial neural networks primarily teach themselves through the use of backpropagation, a learning method which has no clear analogue within the brain. Additionally, Artificial Neural Networks primarily use continuous activation functions, which differ significantly from the spiking neuronal behavior present in the brain. In this paper, we discuss and demonstrate a biologically plausible learning method that approximates backpropagation through two techniques on Spiking Neural Networks. First, we show that the local temporal derivatives that are necessary for backpropagation can be approximately recovered through reconstruction using spike timings. Second, we show that through learning during a sleep phase, inspired by neuroscience research into memory replay, the localized parallel feedback path can learn to approximate the derivative through the forward path weight matrix, thus solving the weight transport problem. Lastly, we demonstrate that the combination of these two methods can approach or exceed the accuracy of backpropagation-based methods for a variety of neuromorphic vision tasks while maintaining biological plausibility.

42 ENGINEERING↗
DOE OSTI2022

How arsenic makes amorphous GeSe a robust chalcogenide glass for advanced memory integration

The 3D integration technology in semiconductor fabrication requires a key component, the ovonic threshold switching (OTS) selector, to suppress the current leakage. The As doped amorphous (a-) GeSe glass is a commercialized OTS material in 3D phase-change memory, but the understanding of such a doping mechanism is still inadequate. Here we systematically explore the effect of As doping on the structural, bonding, and dynamics properties of a-GeAsSe using ab initio molecular dynamics simulations. The results reveal that As atoms form strong bonds with both Ge and Se atoms. The distorted octahedral structures and the 5-fold rings linked by atoms are increased. All of these structural features lead to a more disordered configuration. Moreover, as atoms have notably slowed down the atomic mobility, rendering a-GeAsSe a high stability. Overall, our studies offer insightful understanding of As-doping in OTS materials, paving the way for the design and application of advanced selector devices.

36 MATERIALS SCIENCE↗
DOE OSTI2016

High-energy synchrotron X-ray diffraction measurements of simple bending of pseudoelastic NiTi shape memory alloy wires

Many technological applications of austenitic shape memory alloys (SMAs) involve cyclical mechanical loading and unloading in order to take advantage of pseudoelasticity. In this paper, we investigated the effect of mechanical bending of pseudoelastic NiTi SMA wires using high-energy synchrotron radiation X-ray diffraction (SR-XRD). Differential scanning calorimetry was performed to identify the phase transformation temperatures. Scanning electron microscopy images show that micro-cracks in compressive regions of the wire propagate with increasing bend angle, while tensile regions tend not to exhibit crack propagation. SR-XRD patterns were analyzed to study the phase transformation and investigate micromechanical properties. By observing the various diffraction peaks such as the austenite (200) and the martensite ( ${\bar 1}12$ ), ( ${\bar 1}03$ ), ( ${\bar 1}11$ ), and (101) planes, intensities and residual strain values exhibit strong anisotropy, depending upon whether the sample is in compression or tension during bending.

Zhang, Baozhuo↗
DOE OSTI2024

Memory Effect on the Synthesis of Perovskite-Type Li-Ion Conductor Li x La 2/3– x /3 TiO 3 (LLTO)

The structural chemistry of the solid ion-conducting Li x La 2/3–x/3 TiO 3 (LLTO) is rich with various polymorphs related to atomic segregation. Here, we explored the LLTO reaction pathway from various structurally related precursors (La 2 LiO 3 H, Li 2 TiO 3 , and Li 4 Ti 5 O 12 ), focusing on the effects of LLTO-like structural motifs in precursors using a combination of experimental and computational techniques. Density functional theory (DFT) calculations revealed that the failure of syntheses to produce LLTO below 1300 °C is due to the presence of multiple competing low-energy phases that result in competitive byproduct formation. In all syntheses where T = 1300 °C, LLTO was the sole product; however, varying phase fractions of I4/mcm and P4/nbm polymorphs and double-perovskite P4/mmm can be obtained depending on the synthesis route. This is an unusual result as at 1300 °C, LLTO should only be the ideal cubic Pm-3m perovskite structure, yet there appears to be a memory effect from the different precursors resulting in the unique phase selectivity and stabilization.

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

Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities

Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. Further, in this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Dynamic Heterogeneity and Kovacs’ Memory Effects in Supercooled Water

Understanding the properties of supercooled water is important for developing a comprehensive theory for liquid water and amorphous ices. Because of rapid crystallization for deeply supercooled water, experiments on it are typically carried out under conditions in which the temperature and/or pressure are rapidly changing. As a result, information on the structural relaxation kinetics of supercooled water as it approaches (metastable) equilibrium is useful for interpreting results obtained in this experimentally challenging region of phase space. Here, we used infrared spectroscopy and the fast time resolution obtained by transiently heating nanoscale water films to investigate relaxation kinetics (aging) in supercooled water. When the structural relaxation of the water films was followed using a temperature jump protocol analogous to the classic experiments of Kovacs, similar memory effects were observed. In particular, after suitable aging at one temperature, water’s structure displayed an extremum versus the number of heat pulses upon changing to a second temperature before eventually relaxing to a steady-state structure characteristic of that temperature. A random double well model based on the idea of dynamic heterogeneity in supercooled water accounts for the observations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Predictive Materials Design of Magnetic Random-Access Memory Based on Nanoscale Atomic Structure and Element Distribution

Magnetic tunnel junctions (MTJs) capable of electrical read and write operations have emerged as a canonical building block for non-volatile memory and logic. However, the cause of the wide-spread device properties found experimentally in various MTJ stacks, including tunneling magnetoresistance (TMR), perpendicular magnetic anisotropy (PMA), and voltage-controlled magnetic anisotropy (VCMA), remains elusive. Here, using high-resolution transmission electron microscopy and energy-dispersive X-ray spectroscopy, we found that the MTJ crystallization quality, boron diffusion out of the CoFeB fixed layer, and minimal CoFe oxidation correlate with the TMR. As with the CoFeB free layer, seed layer diffusion into the free layer/MgO interface is negatively correlated with the interfacial PMA, while the metal-oxides concentrations in the free layer correlate with the VCMA. Combined with formation enthalpy and thermal diffusion analysis, we further established a predictive materials design framework to guide the complex design space explorations for high-performance MTJs. In this paper, we demonstrate experimentally high PMA and VCMA values of 1.74 mJ/m2 and 115 fJ/V-m with annealing stability above 400 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nanoscale Probing of Electrical Memory Effects in van der Waals Layered PdSe 2

Tunable electronic materials that can be switched between different impedance states are fundamental to the hardware elements for neuromorphic computing architectures. This “brain-like” computing paradigm uses highly paralleled and colocated data processing, leading to greatly improved energy efficiency and performance compared to traditional architectures in which data have to be frequently transferred between processor and memory. In this work, we use scanning microwave impedance microscopy for nanoscale electrical and electronic characterization of two-dimensional layered semiconductor PdSe 2 to probe neuromorphic properties. The local resolution of tens of nanometers reveals significant differences in electronic behavior between and within PdSe 2 nanosheets (NSs). In particular, we detected both n-type and p-type behaviors, although previous reports only point to ambipolar n-type dominating characteristics. Nanoscale capacitance–voltage curves and subsequent calculation of characteristic maps revealed a hysteretic behavior originating from the creation and erasure of Se vacancies as well as the switching of defect charge states. In addition, stacks consisting of two NSs show enhanced resistive and capacitive switching, which is attributed to trapped charge carriers at the interfaces between the stacked NSs. Stacking n- and p-type NSs results in a combined behavior that allows one to tune electrical characteristics. In conclusion, as local inhomogeneities of electrical and electronic behavior can have a significant impact on the overall device performance, the demonstrated nanoscale characterization and analysis will be applicable to a wide range of semiconducting materials.

2D material↗
DOE OSTI2019

Activating Layered Double Hydroxide with Multivacancies by Memory Effect for Energy-Efficient Hydrogen Production at Neutral pH

Sustainable water-splitting hydrogen production has long been considered one of the most promising energy conversion technologies, but enormous challenges remain: for instance, water electrolysis suffers from high overpotential and over energy consumption under neutral pH conditions. Furthermore, taking advantage of the memory effect of layered double hydroxide (LDH), we report an energy-efficient neutral water electrolyzer material based on LDH with multiple vacancy defects. Benefiting from the improved electrical conductivity, larger electrochemical surface area (ECSA), and faster charge transfer, the NiFe LDH with O, Ni, and Fe vacancies exhibits a low overpotential of 87 mV at 10 mA/cm 2 for hydrogen evolution reaction (HER) in a pH 7 buffer electrolyte. Impressively, the as-fabricated vacancy-containing NiFe LDH (v-NiFe LDH) splits water with a current density of 10 mA/cm 2 at ~1.60 V in a two-electrode device, outperforming most other water-splitting catalysts in neutral media. Such an electrolyzer setup could be powered by a commercial 2.0 V solar cell, producing hydrogen at a current density as high as 100 mA/cm 2 .

25 ENERGY STORAGE↗
DOE OSTI2020

Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Shaping nanoscale magnetic domain memory in exchange-coupled ferromagnets by field cooling

The advance of magnetic nanotechnologies relies on detailed understanding of nanoscale magnetic mechanisms in materials. Magnetic domain memory (MDM), that is, the tendency for magnetic domains to repeat the same pattern during field cycling, is important for magnetic recording technologies. Here we demonstrate MDM in [Co/Pd]/IrMn films, using coherent X-ray scattering. Under illumination, the magnetic domains in [Co/Pd] produce a speckle pattern, a unique fingerprint of their nanoscale configuration. We measure MDM by cross-correlating speckle patterns throughout magnetization processes. When cooled below its blocking temperature, the film exhibits up to 100% MDM, induced by exchange-coupling with the underlying IrMn layer. The degree of MDM drastically depends on cooling conditions. If the film is cooled under moderate fields, MDM is high throughout the entire magnetization loop. Lastly, if the film is cooled under nearly saturating field, MDM vanishes, except at nucleation and saturation. Our findings show how to fully control the occurrence of MDM by field cooling.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Memory-dictated dynamics of single-atom Pt on CeO2 for CO oxidation

Abstract Single atoms of platinum group metals on CeO 2 represent a potential approach to lower precious metal requirements for automobile exhaust treatment catalysts. Here we show the dynamic evolution of two types of single-atom Pt (Pt 1 ) on CeO 2 , i.e., adsorbed Pt 1 in Pt/CeO 2 and square planar Pt 1 in Pt AT CeO 2 , fabricated at 500 °C and by atom-trapping method at 800 °C, respectively. Adsorbed Pt 1 in Pt/CeO 2 is mobile with the in situ formation of few-atom Pt clusters during CO oxidation, contributing to high reactivity with near-zero reaction order in CO. In contrast, square planar Pt 1 in Pt AT CeO 2 is strongly anchored to the support during CO oxidation leading to relatively low reactivity with a positive reaction order in CO. Reduction of both Pt/CeO 2 and Pt AT CeO 2 in CO transforms Pt 1 to Pt nanoparticles. However, both catalysts retain the memory of their initial Pt 1 state after reoxidative treatments, which illustrates the importance of the initial single-atom structure in practical applications.

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

Emergent disorder and mechanical memory in periodic metamaterials

Ordered mechanical systems typically have one or only a few stable rest configurations, and hence are not considered useful for encoding memory. Multistable and history-dependent responses usually emerge from quenched disorder, for example in amorphous solids or crumpled sheets. In contrast, due to geometric frustration, periodic magnetic systems can create their own disorder and espouse an extensive manifold of quasi-degenerate configurations. Inspired by the topological structure of frustrated artificial spin ices, we introduce an approach to design ordered, periodic mechanical metamaterials that exhibit an extensive set of spatially disordered states. While our design exploits the correspondence between frustration in magnetism and incompatibility in meta-mechanics, our mechanical systems encompass continuous degrees of freedom, and thus generalize their magnetic counterparts. We show how such systems exhibit non-Abelian and history-dependent responses, as their state can depend on the order in which external manipulations were applied. We demonstrate how this richness of the dynamics enables to recognize, from a static measurement of the final state, the sequence of operations that an extended system underwent. Thus, multistability and potential to perform computation emerge from geometric frustration in ordered mechanical lattices that create their own disorder.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Surpassing millisecond coherence in on chip superconducting quantum memories by optimizing materials and circuit design

The performance of superconducting quantum circuits for quantum computing has advanced tremendously in recent decades; however, a comprehensive understanding of relaxation mechanisms does not yet exist. In this work, we utilize a multimode approach to characterizing energy losses in superconducting quantum circuits, with the goals of predicting device performance and improving coherence through materials, process, and circuit design optimization. Using this approach, we measure significant reductions in surface and bulk dielectric losses by employing a tantalum-based materials platform and annealed sapphire substrates. With this knowledge we predict the relaxation times of aluminum- and tantalum-based transmon qubits, and find that they are consistent with experimental results. We additionally optimize device geometry to maximize coherence within a coaxial tunnel architecture, and realize on-chip quantum memories with single-photon Ramsey times of 2.0 – 2.7 ms, limited by their energy relaxation times of 1.0 – 1.4 ms. These results demonstrate an advancement towards a more modular and compact coaxial circuit architecture for bosonic qubits with reproducibly high coherence.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

All-optical reconfiguration of single silicon-vacancy centers in diamond for non-volatile memories

Strain engineering is vital for tuning the optical and spin properties of solid-state color centers, enhancing spin coherence and compensating emission wavelength shift. Here, we develop an all-optical approach to directly modify the local strain of color centers at the nanoscale by migrating the nearby defect. High-power pulsed optical irradiation triggers defect migration, which subsequently leads to the redistribution of the local crystal lattice of the host material. This redistribution alters the strain experienced by nearby color centers. Using silicon-vacancy centers in diamond, we validate this method and demonstrate a ground state splitting enhancement of up to 1.8 THz. Unlike conventional methods, our approach requires no external fields or nanostructure modifications, enabling non-volatile strain control and optical memory functionality across wide temperature ranges. Its local, permanent nature offers a scalable path for enhancing spin coherence in large-scale quantum systems and has potential applications in photonic machine learning.

Nanoscale devices↗