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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

The Effects of Blood Glucose Levels on Cognitive Performance: A Review of the Literature

The purpose of this review paper is to discuss the research literature on the effects of blood glucose levels on executive and non-executive functions in humans. The review begins with a brief description of blood glucose, how it has been studied, previous syntheses of prior studies, and basic results regarding the role of blood glucose on cognitive functioning. The following sections describe work that investigated the effect of blood glucose on both non-executive and executive functions (e.g., sensory processing, psychomotor functioning, attention, vigilance, memory, language and communication, judgement and decision-making, and complex task performance). Within each section, summaries of the findings and challenges to the literature are included. Measurement conversions of blood glucose levels, blood glucose values, and associated symptoms are depicted. References to the types of tests used to investigate blood glucose and cognitive performance are provided. For more detailed descriptions of references within (and in addition to) this paper, an annotated bibliography is also provided. Several moderator variables including individual differences and contextual variables related to the effects of blood glucose levels on performance (e.g., age, gender, time of day, familiarity with the task and symptom awareness, expectancy effects, dose dependent effects, time dependent effects, task specific effects, rising and falling blood glucose levels, and speed and/or accuracy trade-offs) are addressed later in the paper. Some suggestions for future experimental methodologies are also made.

Feldman, Jolene↗
DOE OSTI2017

Mechanistic insight into neurotoxicity induced by developmental insults

Epidemiological and/or experimental studies have shown that unfavorable prenatal environmental factors, such as stress or exposure to certain neurotoxic environmental contaminants, may have adverse consequences for neurodevelopment. Alterations in neurogenesis can have harmful effects not only for the developing nervous system, but also for the adult brain where neurogenesis is believed to play a role in learning, memory, and even in depression. Many recent advances in the understanding of the complex process of nervous system development can be integrated into the field of neurotoxicology. In the past 15 years we have been using cultured neural stem or progenitor cells to investigate the effects of neurotoxic stimuli on cell survival, proliferation and differentiation, with special focus on heritable effects. This is an overview of the work performed by our group in the attempt to elucidate the mechanisms of developmental neurotoxicity and possibly provide relevant information for the understanding of the etiopathogenesis of complex brain disorders. - Highlights: • The developing nervous system is highly sensitive to toxic insults. • Neural stem cells are relevant models for mechanistic studies as well as for identifying heritable effects due to epigenetic changes. • Depending on the dose, the outcome of exposure to neurotoxicants ranges from altered proliferation and differentiation to cell death. • The elucidation of neurotoxicity mechanisms is relevant for understanding the etiopathogenesis of developmental and adult nervous system disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Block encoding of the three-dimensional heterogeneous Poisson equation with application to fracture flow

Quantum linear system (QLS) algorithms offer the potential to solve large-scale linear systems exponentially faster than classical methods. However, applying QLS algorithms to real-world problems remains challenging due to issues such as state preparation, data loading, and efficient information extraction. In this work, we study the feasibility of applying QLS algorithms to solve discretized three-dimensional (3D) heterogeneous Poisson equations, with specific examples relating to groundwater flow through geologic fracture networks. We explicitly construct a block encoding for the 3D heterogeneous Poisson matrix by leveraging the sparse local structure of the discretized operator. While classical solvers benefit from preconditioning, we show that block encoding the system matrix and preconditioner separately does not improve the effective condition number that dominates the QLS run-time. This differs from classical approaches where the preconditioner and the system matrix can often be implemented independently. Nevertheless, due to the structure of the problem in three dimensions, the quantum algorithm achieves a run-time of 𝑂⁡(𝑁 2/3 polylog 𝑁 ⋅log (1/𝜖)), outperforming the best classical methods (with run times of 𝑂⁡(𝑁⁢log 𝑁 ⋅log (1/𝜖))) and offering exponential memory savings. These results highlight both the promise and limitations of QLS algorithms for practical scientific computing, and point to effective condition-number reduction as a key barrier in achieving quantum advantages.

58 GEOSCIENCES↗
NASA NTRS2010

Nonvolatile Memory Technology for Space Applications

This slide presentation reviews several forms of nonvolatile memory for use in space applications. The intent is to: (1) Determine inherent radiation tolerance and sensitivities, (2) Identify challenges for future radiation hardening efforts, (3) Investigate new failure modes and effects, and technology modeling programs. Testing includes total dose, single event (proton, laser, heavy ion), and proton damage (where appropriate). Test vehicles are expected to be a variety of non-volatile memory devices as available including Flash (NAND and NOR), Charge Trap, Nanocrystal Flash, Magnetic Memory (MRAM), Phase Change--Chalcogenide, (CRAM), Ferroelectric (FRAM), CNT, and Resistive RAM.

Oldham, Timothy R.↗
DOE OSTI2018

Parallelizable adjoint stencil computations using transposed forward-mode algorithmic differentiation

Algorithmic differentiation (AD) is a tool for generating discrete adjoint solvers, which efficiently compute gradients of functions with many inputs, for example for use in gradient-based optimization. AD is often applied to large computations such as stencil operators, which are an important part of most structured-mesh PDE solvers. Stencil computations are often parallelized, for example by using OpenMP, and optimized by using techniques such as cache-blocking and tiling to fully utilize multicore CPUs and many-core accelerators and GPUs. Differentiating these codes with conventional reverse-mode AD results in adjoint codes that cannot be expressed as stencil operations and may not be easily parallelizable. They thus leave most of the compute power of modern architectures unused. We present a method that combines forward-mode AD and loop transformation to generate adjoint solvers that use the same memory access pattern as the original computation that they are derived from and can benefit from the same optimization techniques. The effectiveness of this method is demonstrated by generating a scalable adjoint CFD solver for multicore CPUs and Xeon Phi accelerators.

algorithmic differentiation↗
DOE OSTI2019

Replays of spatial memories suppress topological fluctuations in cognitive map

The spiking activity of the hippocampal place cells plays a key role in producing and sustaining an internalized representation of the ambient space—a cognitive map. These cells do not only exhibit location-specific spiking during navigation, but also may rapidly replay the navigated routs through endogenous dynamics of the hippocampal network. Physiologically, such reactivations are viewed as manifestations of “memory replays” that help to learn new information and to consolidate previously acquired memories by reinforcing synapses in the parahippocampal networks. Below we propose a computational model of these processes that allows assessing the effect of replays on acquiring a robust topological map of the environment and demonstrate that replays may play a key role in stabilizing the hippocampal representation of space.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1984

Utilization of Historic Information in an Optimisation Task

One of the basic components of a discrete model of motor behavior and decision making, which describes tracking and supervisory control in unitary terms, is assumed to be a filtering mechanism which is tied to the representational principles of human memory for time-series information. In a series of experiments subjects used the time-series information with certain significant limitations: there is a range-effect; asymmetric distributions seem to be recognized, but it does not seem to be possible to optimize performance based on skewed distributions. Thus there is a transformation of the displayed data between the perceptual system and representation in memory involving a loss of information. This rules out a number of representational principles for time-series information in memory and fits very well into the framework of a comprehensive discrete model for control of complex systems, modelling continuous control (tracking), discrete responses, supervisory behavior and learning.

Boesser, T.↗
DOE OSTI2016

Key Information File for Radioactive Waste Repositories - Preliminary Tests - 16169

The Key Information File (KIF) is a document designed to provide a summary of the existence, location and content of an engineered facility for the permanent disposal of radioactive wastes. It should be recognized as part of an internationally integrated system of records and memory. Its primary function would be to maintain awareness of the site, and long-term confidence in the effectiveness of the disposal system, so that the likelihood of unnecessary human disturbance is minimized. In order to ensure this document is accessible to a large audience, it would need to be limited in size. A draft format for the KIF has been proposed, and testing is being conducted on two cases, that of the 'Centre de stockage de la Manche', the closed French Repository (surface) and that of the planned Swedish Spent Fuel Repository (geological). Preliminary results show the potential for such a document. Comments and questions raised by the 'Records, Knowledge and Memory preservation' (RK and M) initiative, coordinated by the Nuclear Energy Agency (NEA) of OECD, are presented. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Effect of Ion Irradiation on Amorphous and Crystalline Ge–Se and Their Application as Phase Change Temperature Sensor

Research on phase change materials is predominantly focused on their application as memory devices or for temperature control which requires low phase change temperature. The Ge–Se binary chalcogenide glass system with its wide glass-forming region is a potential candidate for high-temperature and high-radiation phase change applications. In this study, the concept of employing Ge x Se 100– x glasses to monitor high temperature (450–528 °C) using the phase change effect, is reported. Materials selection, device structure, and performance of prototype sensors are analyzed. In addition, the effect of heavy ion irradiation by Xe ions with energies of 200, 600, and 1000 keV (fluence ≈10 14 cm –2 ) on the Ge x Se 100– x ( x = 30, 33, 40) thin films and phase change devices is studied. The irradiation effect on the amorphous and crystalline structure of the thin films is evaluated by Raman spectroscopy and X-ray diffraction (XRD). Although the changes in the structural units of amorphous films are negligible, in crystalline films orthorhombic-GeSe 2 crystals are found to be most affected by irradiation and a new phase, orthorhombic GeSe is found in the thin films after irradiation. The performance of a sensor with an active film of Ge 40 Se 60 is also shown as an example.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Si Doped Hafnium Oxide-A “Fragile” Ferroelectric System

Here, silicon doped hafnium oxide was the material used in the original report of ferroelectricity in hafnia in 2011. Since then, it has been subject of many further publications including the demonstration of the world's first ferroelectric field-effect transistor in the state-of-the-art 28 nm technology. Though many studies are conducted with a strong focus on application in memory devices, a comprehensive study on structural stability in these films remains to be seen. In this work, a film thickness of about 36 nm, instead of the 10 nm used in most previous studies, is utilized to carefully probe how the concentration range impacts the evolution of phases, the dopant distribution, the field cycling effects, and their interplay in the macroscopic ferroelectric response of the films. Si:HfO2 appears to be a rather fragile system: different phases seem close in energy and the system is thus rich in competing phenomena. Nonetheless, it offers ferroelectricity or field-induced ferroelectricity for elevated annealing conditions up to 1000 °C. Similar to the measures taken for conventional ferroelectrics such as lead zirconate titanate, engineering efforts to guarantee stable interfaces and stoichiometry are mandatory to achieve stable performance in applications such as ferroelectric memories, supercapacitors, or energy harvesting devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2002

Brain signaling and behavioral responses induced by exposure to (56)Fe-particle radiation

Previous experiments have demonstrated that exposure to 56Fe-particle irradiation (1.5 Gy, 1 GeV) produced aging-like accelerations in neuronal and behavioral deficits. Astronauts on long-term space flights will be exposed to similar heavy-particle radiations that might have similar deleterious effects on neuronal signaling and cognitive behavior. Therefore, the present study evaluated whether radiation-induced spatial learning and memory behavioral deficits are associated with region-specific brain signaling deficits by measuring signaling molecules previously found to be essential for behavior [pre-synaptic vesicle proteins, synaptobrevin and synaptophysin, and protein kinases, calcium-dependent PRKCs (also known as PKCs) and PRKA (PRKA RIIbeta)]. The results demonstrated a significant radiation-induced increase in reference memory errors. The increases in reference memory errors were significantly negatively correlated with striatal synaptobrevin and frontal cortical synaptophysin expression. Both synaptophysin and synaptobrevin are synaptic vesicle proteins that are important in cognition. Striatal PRKA, a memory signaling molecule, was also significantly negatively correlated with reference memory errors. Overall, our findings suggest that radiation-induced pre-synaptic facilitation may contribute to some previously reported radiation-induced decrease in striatal dopamine release and for the disruption of the central dopaminergic system integrity and dopamine-mediated behavior.

NASA Discipline Radiation Health↗
DOE OSTI2024

Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model

In the evolving landscape of high-performance computing, especially to fight the end of Moore’s Law and Dennard’s Scaling, the ability to shift between compute-bound and memory-bound states is critical for enhancing adaptability and flexibility to diverse system and domain-specific architectures. Such capability is vital for optimizing performance across distinguished hardware configurations, such as accelerators, memory hierarchies, and cache systems. Despite that ad hoc optimization techniques, such as compressed/approximate computation, have been enabled for compute-/data-intensive computing for improved performance in distinct hardware settings, there lacks an understanding of 1) the rational behind performance improvement; 2) capability of different optimizations; 3) what optimization to respond to specific computational and memory demands. This work proposes a compression-enabled roofline model to facilitate this adaptability with data compression techniques to balance and transform between computational and memory demands. This model enables applications to adjust in response to the specific strengths and limitations of the underlying hardware and system to optimize resource utilization. The effectiveness of this approach is demonstrated with matrix multiplication kernels on different input sizes, with turning on/off various compression techniques, including 1) low-precision floating point; 2) sparse matrix formulation; and 3) compressed arrays with ZFP. By reducing memory transfer volumes and cache misses and increasing data locality and computational intensity through compression, the specific roofline model can transform between compute and memory bounds to align more efficiently with system capabilities. This advancement not only improves overall performance but also maximizes adaptability in diverse computing environments.

Naraparaju, Ramasoumya [University of Washington]↗
DOE OSTI2025

Amorphous Indium Oxide Channel FEFETs With Write Voltage of 0.9 V and Endurance >10 12 for Refresh-Free Embedded Memory

This work presents, for the first time, a back-end-of-the-line (BEOL)-compatible W-doped indium oxide (IWO) ferroelectric field-effect transistor (FEFET) with a record-low operating voltage below 0.9 V and a write speed of 20 ns while achieving a transient read current window (CW) ratio ( I LVT /I HVT ) greater than 10 4 . The device also exhibits exceptional reliability characteristics such as: 1) measured bipolar write endurance up to 10 12 cycles; 2) a fast read speed of 50 ns; 3) read endurance surpassing 10 12 cycles; and 4) retention exceeding 10 4 s at 85 ∘ C. Furthermore, a physics-based numerical model has been developed to investigate the nanoscale characteristics of BEOL FEFET devices, leveraging nucleation-limited switching in HfO 2 ferroelectrics and dc characterization to extract material and channel parameters for accurate device simulation. The simulation uncovers the stochastic switching behavior of BEOL amorphous oxide semiconductor (AOS) FEFETs and demonstrates an intrinsic switching time as low as 1 ps, highlighting the potential of BEOL AOS FEFETs for ultrafast memory applications. These results establish AOS FEFETs as a compelling candidate for high-density embedded memory applications for last-level cache (LLC) (L4) in advanced CMOS technology nodes.

1-V ferroelectric field-effect transistor (FEFET)↗
DOE OSTI2024

EV Forecasting-Based Model Predictive Control for Distribution System Congestion Mitigation

The uncoordinated charging of electric vehicles (EVs) in time and space brings congestion issues to the distribution network. This paper proposes an EV charging demand forecasting-based model predictive control (MPC) method for distribution system congestion management. To effectively forecast the time-series EV station charging demand, a hybrid forecasting model that integrates the long short-term memory network (LSTM) and Transformer is proposed. The Transformer-LSTM model is trained using a one-year real historical charging dataset of EV stations to forecast future charging demand in 15-minute intervals. This informs the MPC for distribution network congestion management and minimization of PV curtailment. Numerical results carried out on the modified IEEE 123-bus distribution system demonstrate that the proposed method can effectively resolve line congestion issues through EV smart charging and PV curtailment while outperforming other benchmarks.

ADVANCED PROPULSION SYSTEMS,SOLAR ENERGY↗
DOE OSTI2024

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units

Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs such as social networks. We demonstrate a novel hardware-software co-design approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations including a planner and vectorization for the gather-scatter operations targeted for Graphcore’s Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed co-design approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes and sparsity. Here, we demonstrate that such a co-design approach can reduce the training time of atomistic GNNs and can improve the performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Resistive switching memories in MoS{sub 2} nanosphere assemblies

A resistive switching memory device consisting of reduced graphene oxide and indium tin oxide as top/bottom two electrodes, separated by dielectric MoS{sub 2} nanosphere assemblies as the active interlayer, was fabricated. This device exhibits the rewritable nonvolatile resistive switching with low SET/RESET voltage (∼2 V), high ON/OFF resistance ratio (∼10{sup 4}), and superior electrical bistability, introducing a potential application in data storage field. The resistance switching mechanism was analyzed in the assumptive model of the electron tunneling across the polarized potential barriers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Layer-by-layer charging in non-volatile memory devices using embedded sub-2 nm platinum nanoparticles

In this work, we demonstrate multi-level operation of a non-volatile memory metal oxide semiconductor capacitor by controlled layer-by-layer charging of platinum nanoparticle (PtNP) floating gate devices with defined gate voltage bias ranges. The device consists of two layers of ultra-fine, sub-2 nm PtNPs integrated between Al{sub 2}O{sub 3} tunneling and separation layers. PtNP size and interparticle distance were varied to control the particle self-capacitance and associated Coulomb charging energy. Likewise, the tunneling layer thicknesses were also varied to control electron tunneling to the first and second PtNP layers. The final device configuration with optimal charging behavior and multi-level programming was attained with a 3 nm Al{sub 2}O{sub 3} initial tunneling layer, initial PtNP layer with particle size 0.54 ± 0.12 nm and interparticle distance 4.65 ± 2.09 nm, 3 nm Al{sub 2}O{sub 3} layer to separate the PtNP layers, and second particle layer with 1.11 ± 0.28 nm PtNP size and interparticle distance 2.75 ± 1.05 nm. In this device, the memory window of the first PtNP layer saturated over a programming bias range of 7 V to 14 V, after which the second PtNP layer starts charging, exhibiting a multi-step memory window with layer-by-layer charging.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Conservative projection-based data-driven model order reduction of a fluid-kinetic spectral solver

Kinetic simulations are computationally intensive due to six-dimensional phase space discretization. Many kinetic spectral solvers use the asymmetrically weighted Hermite expansion due to its conservation and fluid-kinetic coupling properties, i.e., the lower-order Hermite moments capture and describe the macroscopic fluid dynamics, and higher-order Hermite moments describe the microscopic kinetic dynamics. We leverage this structure by developing a parametric data-driven reduced-order model based on the proper orthogonal decomposition, which projects the higher-order kinetic moments while retaining the fluid moments intact. We demonstrate analytically and numerically that the method ensures local and global mass, momentum, and energy conservation. The numerical results show that the proposed method effectively replicates the high-dimensional spectral simulations at a fraction of the computational cost and memory, as validated on the weak Landau damping and two-stream instability benchmark problems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗