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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Software stewardship and advancement of a high-performance computing scientific application: QMCPACK

Here, we provide an overview of the software engineering efforts and their impact in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code targeting high-performance computing (HPC) systems. Aspects included are: (i) strategic expansion of continuous integration (CI) targeting CPUs, using GitHub Actions own runners, and NVIDIA and AMD GPUs used in pre-exascale systems, (ii) incremental reduction of memory leaks using sanitizers, (iii) incorporation of Docker containers for CI and reproducibility, and (iv) refactoring efforts to improve maintainability, testing coverage, and memory lifetime management. We quantify the value of these improvements by providing metrics to illustrate the shift towards a predictive, rather than reactive, maintenance approach. Our goal, in documenting the impact of these efforts on QMCPACK, is to contribute to the body of knowledge on the importance of research software engineering (RSE) for the stewardship and advancement of community HPC codes to enable scientific discovery at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Profiling B cell immunodominance after SARS-CoV-2infection reveals antibody evolution to non-neutralizing viral targets

Dissecting the evolution of memory B cells (MBCs) against SARS-CoV-2 is critical for understanding antibody recall upon secondary exposure. Here, we used single-cell sequencing to profile SARS-CoV-2-reactive B cells in 38 COVID-19 patients. Using oligo-tagged antigen baits, we isolated B cells specific to the SARS-CoV-2 spike, nucleoprotein (NP), open reading frame 8 (ORF8), and endemic human coronavirus (HCoV) spike proteins. SARS-CoV-2 spike-specific cells were enriched in the memory compartment of acutely infected and convalescent patients several months post symptom onset. With severe acute infection, substantial populations of endemic HCoV-reactive antibody-secreting cells were identified and possessed highly mutated variable genes, signifying preexisting immunity. Finally, MBCs exhibited pronounced maturation to NP and ORF8 over time, especially in older patients. Monoclonal antibodies against these targets were non-neutralizing and non-protective in vivo. These findings reveal antibody adaptation to non-neutralizing intracellular antigens during infection, emphasizing the importance of vaccination for inducing neutralizing spike-specific MBCs.

antibody↗
DOE OSTI2020

Effect of Al substitution on the magnetocaloric properties of Ni-Co-Mn-Sn multifunctional alloys

Ni-Mn-based metamagnetic shape memory alloys have been extensively studied due to their multifunctional properties including magnetocaloric effect (MCE), magnetoresistance effect, and magnetic-field-induced strain. However, the large intrinsic thermal hysteresis of these alloys sets a limit on further development for practical applications. Here, with a few atomic percent Al substitution, we greatly reduced the thermal hysteresis and transformation temperature interval while maintaining the large magnetization difference (ΔM) between austenite and martensite. The simultaneous achievement of a large ΔM and low thermal hysteresis leads to a remarkable enhancement in MCE upon Al substitution, and a maximum magnetic entropy change of 31.6 J/kg K is achieved under a field of 5 T in Ni 39 Co 11 Mn 40 Sn 8 Al 2 . Furthermore, our in situ high-energy X-ray diffraction experiments show that the Ni 39 Co 11 Mn 40 Sn 8 Al 2 has good geometric compatibility between austenite and martensite, which accounts for the reduced thermal hysteresis in this alloy. This study is instructive for the development of high-performance magnetocaloric materials in the metamagnetic shape memory alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhanced superelasticity of nanocrystalline NiTi/NiTiNbFe laminar composite

This study explores a new concept of the synergy between two constituent shape memory alloys (SMAs) in a composite to achieve a wide superelastic temperature window. In this work, a nanocrystalline NiTi/NiTiNbFe dual-SMA composite is designed, in which two constituent SMAs are both capable of stress-induced martensitic transformation, but within different temperature ranges. Based on the principle of lattice strain matching between the two SMAs, superelastic martensitic transformation in one induces superelastic transformation in the other at temperatures the latter will not when standing alone. Furthermore, via this synergy the composite is able to achieve an ultra-wide superelastic temperature window of 336°C, from -196 to 140°C, with a ~5% tensile strain recovery and high superelastic stresses in the range of 700~1400 MPa. Such properties appear superior to all NiTi-based shape memory alloys to date.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Neuroanatomical and cognitive correlates of visual hallucinations in Parkinson’s disease and dementia with Lewy bodies: Voxel-based morphometry and neuropsychological meta-analysis

Visual hallucinations (VH) are common in Parkinson's disease and dementia with Lewy bodies, two forms of Lewy body disease (LBD), but the neural substrates and mechanisms involved are still unclear. We conducted meta-analyses of voxel-based morphometry (VBM) and neuropsychological studies investigating the neuroanatomical and cognitive correlates of VH in LBD. For VBM (12 studies), we used Seed-based d Mapping with Permutation of Subject Images (SDM-PSI), including statistical parametric maps for 50% of the studies. For neuropsychology (35 studies), we used MetaNSUE to consider non-statistically significant unreported effects. VH were associated with smaller grey matter volume in occipital, frontal, occipitotemporal, and parietal areas (peak Hedges' g -0.34 to -0.49). In patients with Parkinson's disease without dementia, VH were associated with lower verbal immediate memory performance (Hedges' g -0.52). Both results survived correction for multiple comparisons. Here, abnormalities in these brain regions might reflect dysfunctions in brain networks sustaining visuoperceptive, attention, and executive abilities, with the latter also being at the basis of poor immediate memory performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

4D Printing via an Unconventional Fused Deposition Modeling Route to High-Performance Thermosets

An unprecedented four-dimensional (4D) printing process allowing high-performance and shape memory thermoset to be printed, for the first time, by fused deposition modeling (FDM) with isotropic properties has been achieved. Here, bisphenol A-based epoxy and benzoxazine were formulated to a low-temperature thermoplastic and high-temperature thermoset resin, which is melt-extrudable and can be postcured into covalently cross-linked material. Carbon nanotube (CNT) was added in the resin to work as both mechanical enhancement filler and rheology modifier to prevent shape deformation during postcuring process. The cross-layer reaction fuses individual layers into an integrity, thus eliminating layer delamination induced by FDM, offering isotropic mechanical properties regardless of the printing orientations. The highly cross-linked network provides outstanding mechanical strength and superb thermal stability. The excellent shape memory performance with fast recovery rate and large recovery degree is also obtained in the three-dimensional (3D) printed composites.

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

Kelvin Probe Force Microscopy Imaging of Plasticity in Hydrogenated Perovskite Nickelate Multilevel Neuromorphic Devices

Ion drift in nanoscale electronically inhomogeneous semiconductors is among the most important mechanisms being studied for designing neuromorphic computing hardware. However, nondestructive imaging of the ion drift in operando devices directly responsible for multiresistance states and synaptic memory represents a formidable challenge. Here, we present Kelvin probe force microscopy imaging of hydrogen-doped perovskite nickelate device channels subject to high-speed electric field pulses to directly visualize proton distribution by monitoring surface potential changes spatially, which is also supported with finite element-based electric field distribution studies. First-principles calculations provide mechanistic insights into the origin of surface potential changes as a function of hydrogen donor doping that serves as the contrast mechanism. We demonstrate 128 (7-bit) nonvolatile conductance levels in such devices relevant to in-memory computing applications. The synaptic plasticity measurements are implemented in spiking neural networks and show promising results for classification (SciKit Learn’s Iris and Wine data sets) and control (OpenAI’s CartPole-v1 and BipedalWalker-v3) simulation tasks.

Kelvin probe force microscopy↗
DOE OSTI2023

Influence of surface adsorption on MoS 2 memtransistor switching kinetics

Sulfur-deficient polycrystalline two-dimensional (2D) molybdenum disulfide (MoS 2 ) memtransistors exhibit gate-tunable memristive switching to implement emerging memory operations and neuromorphic computing paradigms. Grain boundaries and sulfur vacancies are critical for memristive switching; however, the underlying physical mechanisms are not fully understood. Furthermore, the adsorption of water and gaseous species strongly perturbs electronic transport in monolayer MoS 2 , and little work has been done to explore the influence of surface interactions on defect-related kinetics that produces memristive switching. Here, we study the switching kinetics of back-gated MoS 2 memtransistors using current transient measurements in a controlled atmosphere chamber. Here, we observe that adsorbed water molecules lead to suppression of the electronic trap-filling processes concomitant with the resistive switching process, resulting in altered kinetics of the resistive switching. Additionally, using the transient response from “bunched” drain voltage pulse trains performed as a function of temperature, we extract the energy of the affected trap state and find that it places the trap roughly midgap [E T = E C – 0.7 (±0.4) eV]. Our results highlight the importance of controlling for surface interactions that may affect switching kinetics in 2D memtransistors, synaptic transistors, and related memory devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optical and microstructural characterization of Er 3+ doped epitaxial cerium oxide on silicon

Rare-earth ion dopants in solid-state hosts are ideal candidates for quantum communication technologies, such as quantum memories, due to the intrinsic spin–photon interface of the rare-earth ion combined with the integration methods available in the solid state. Erbium-doped cerium oxide (Er:CeO 2 ) is a particularly promising host material platform for such a quantum memory, as it combines the telecom-wavelength (~ 1.5 μm) 4f–4f transition of erbium, a predicted long electron spin coherence time when embedded in CeO 2 , and a small lattice mismatch with silicon. In this work, we report on the epitaxial growth of Er:CeO 2 thin films on silicon using molecular beam epitaxy, with controlled erbium concentration between 2 and 130 parts per million (ppm). We carry out a detailed microstructural study to verify the CeO 2 host structure and characterize the spin and optical properties of the embedded Er 3+ ions as a function of doping density. In as-grown Er:CeO 2 in the 2–3 ppm regime, we identify an EPR linewidth of 245(1) MHz, an optical inhomogeneous linewidth of 9.5(2) GHz, an optical excited state lifetime of 3.5(1) ms, and a spectral diffusion-limited homogeneous linewidth as narrow as 4.8(3) MHz. We test the annealing of Er:CeO 2 films up to 900 °C, which yields narrowing of the inhomogeneous linewidth by 20% and extension of the excited state lifetime by 40%.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Study of carbon nanotube embedded honey as a resistive switching material

In this paper, natural organic honey embedded with carbon nanotubes (CNTs) was studied as a resistive switching material for biodegradable nonvolatile memory in emerging neuromorphic systems. CNTs were dispersed in a honey-water solution with the concentration of 0.2 wt% CNT and 30 wt% honey. The final honey-CNT-water mixture was spin-coated and dried into a thin film sandwiched in between Cu bottom electrode and Al top electrode to form a honey-CNT based resistive switching memory (RSM). Surface morphology, electrical characteristics and current conduction mechanism were investigated. The results show that although CNTs formed agglomerations in the dried honey-CNT film, both switching speed and the stability in SET and RESET process of honey-CNT RSM were improved. The mechanism of current conduction in CNT is governed by Ohm’s law in low-resistance state and the low-voltage range in high-resistance state, but transits to the space charge limited conduction at high voltages approaching the SET voltage.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A broad-host-range event detector: expanding and quantifying performance between Escherichia coli and Pseudomonas species

Modern microbial biodesign relies on the principle that well-characterized genetic parts can be reused and reconfigured for different functions. However, this paradigm has only been successful in a limited set of hosts, mostly comprised from common lab strains of Escherichia coli . It is clear that new applications such as chemical sensing and event logging in complex environments will benefit from new host chassis. This study quantitatively compared how the same chemical event logger performed across four strains and three different microbial species. An integrase-based sensor and memory device was operated by two representative soil Pseudomonads— Pseudomonas fluorescens SBW25 and Pseudomonas putida DSM 291. Quantitative comparisons were made between these two non-traditional hosts and two benchmark E. coli chassis including the probiotic Nissle 1917 and common cloning strain DH5α. The performance of sensor and memory components changed according to each host, such that a clear chassis effect was observed and quantified. These results were obtained via fluorescence from reporter proteins that were transcriptionally fused to the integrase and downstream recombinant region and via data-driven kinetic models. The Pseudomonads proved to be acceptable chassis for the operation of this event logger, which outperformed the common E. coli DH5α in many ways. This study advances an emerging frontier in synthetic biology that aims to build broad-host-range devices and understand the context by which different species can execute programmable genetic operations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Light storage, retrieval, and controllable interference in an atomic tripod system

Highly efficient quantum memories are essential for advancing quantum information processing technologies, including scalable quantum computing and quantum networks. We experimentally demonstrate a light storage and retrieval protocol in a tripod system using an ensemble of laser-cooled 87 Rb atoms. The tripod system, which consists of three ground states and an excited state, offers rich dynamics: its use to coherently store and retrieve a weak probe pulse in the 87 Rb 𝐹 = 1 ground-state manifold leads to the interference of two spin-wave excitations during storage time that translate to an interference in the peak intensity of the retrieved probe pulse. Our work shows that these interferences, which manifest when varying the pulse sequence or energy level structure, can be controlled experimentally by varying the storage time, optical phase, and magnetic field strength. Theoretical simulations exhibit excellent agreement with the experimental results. In conclusion, this work demonstrates the rich dynamics and versatile capabilities of atomic tripod systems for light storage and retrieval, with key advantages over conventional Λ systems, highlighting the potential of atomic tripod systems for applications in quantum information processing, quantum synchronization, and atomic memory protocols.

Coherent control↗
DOE OSTI2021

Nanocrystalline strain glass TiNiPt and its superelastic behavior

TiNi-based shape-memory alloys are known to exhibit a strain glass state under certain conditions, generally in the presence of high-density defects such as excess solute atoms or alloying elements, dislocations, and nanoprecipitates. In this paper, we report a strain glass transition in a nanocrystalline Ti 50 Ni 35 Pt 15 alloy. The nanocrystalline strain glass state is achieved by a combined effect of high-density grain boundaries and high concentration doping of Pt atoms in the B2 matrix. The nanocrystalline Ti 50 Ni 35 Pt 15 strain glass alloy showed a large near-complete progressive superelasticity with a recovery strain of about 6% and a low apparent Young's modulus of about 30 GPa in a wide temperature range of over 200 °C. In situ synchrotron x-ray diffraction measurement showed that the strain glass B2 [B2(SG)] phase experienced B2(SG)→R→B19 transformation upon loading and B19→B2(SG) upon unloading. The findings of this study provide insight for the development of nanocrystalline strain glass shape-memory alloys.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

NeuralCubes: Deep Representations for Visual Data Exploration

Visual exploration of large multi-dimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. Techniques based on data cubes are some of the most promising approaches. However, these techniques usually require a large memory footprint for large datasets. To tackle this problem, we present NeuralCubes: neural networks that predict results for aggregate queries, similar to data cubes. NeuralCubes learns a function that takes as input a given query, for instance, a geographic region and temporal interval, and outputs the result of the query. The learned function serves as a real-time, low-memory approximator for aggregation queries. Our models are small enough to be sent to the client side (e.g. the web browser for a web-based application) for evaluation, enabling data exploration of large datasets without database/network connection. Here, we demonstrate the effectiveness of NeuralCubes through extensive experiments on a variety of datasets and discuss how NeuralCubes opens up opportunities for new types of visualization and interaction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

RDMA vs. RPC for Implementing Distributed Data Structures

Distributed data structures are key to implementing scalable applications for scientific simulations and data analysis. In this paper we look at two implementation styles for distributed data structures: remote direct memory access (RDMA) and remote procedure call (RPC). We focus on operations that require individual accesses to remote portions of a distributed data structure, e.g., accessing a hash table bucket or distributed queue, rather than global operations in which all processors collectively exchange information. We look at the trade-offs between the two styles through microbenchmarks and a performance model that approximates the cost of each. The RDMA operations have direct hardware support in the network and therefore lower latency and overhead, while the RPC operations are more expressive but higher cost and can suffer from lack of attentiveness from the remote side. We also run experiments to compare the real-world performance of RDMA- and RPC-based data structure operations with the predicted performance to evaluate the accuracy of our model, and show that while the model does not always precisely predict running time, it allows us to choose the best implementation in the examples shown. We believe this analysis will assist developers in designing data structures that will perform well on current network architectures, as well as network architects in providing better support for this class of distributed data structures.

distributed data structures↗
DOE OSTI2020

Cross-Market Price Difference Forecast Using Deep Learning for Electricity Markets

Price forecasting is in the center of decision making in electricity markets. Many researches have been done in forecasting energy prices while little research has been reported on forecasting price difference between day-ahead and realtime markets due to its high volatility, which however plays a critical role in virtual trading. To this end, this paper takes the first attempt to employ novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units to forecast the price difference between day-ahead and real-time markets for the same node. The raw data is collected from PJM market, processed and fed into the proposed network. The Root Mean Squared Error (RMSE) and customized performance metric are used to evaluate the performance of the proposed method. Case studies show that it outperforms the traditional statistical models like ARIMA, and machine learning models like XGBoost and SVR methods in both RMSE and the capability of forecasting the sign of price difference. Additionally, to cross-market price difference forecast, the proposed approach has the potential to be applied to solve other forecasting problems such as price spread forecast in DA market for Financial Transmission Right (FTR) trading purpose.

DA/RT price difference↗
DOE OSTI2021

Ionizing radiation effects in SONOS-based neuromorphic inference accelerators

Here, we evaluate the sensitivity of neuromorphic inference accelerators based on Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) charge trap memory arrays to total ionizing dose (TID) effects. Data retention statistics were collected for 16 Mbit of 40 nm SONOS digital memory exposed to ionizing radiation from a Co-60 source, showing good retention of the bits up to the maximum dose of 500 krad(Si). Using this data, we formulate a rate-equation-based model for the TID response of trapped charge carriers in the ONO stack, and predict the effect of TID on intermediate device states between ‘program’ and ‘erase’. This model is then used to simulate arrays of low-power, analog SONOS devices that store 8-bit neural network weights and support in situ matrix-vector multiplication. We evaluate the accuracy of the irradiated SONOS-based inference accelerator on two image recognition tasks – CIFAR-10 and the challenging ImageNet dataset – using state-of-the-art convolutional neural networks, such as ResNet-50. We find that across the datasets and neural networks evaluated, the accelerator tolerates a maximum TID between 10 krad(Si) and 100 krad(Si), with deeper networks being more susceptible to accuracy losses due to TID.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

SARS-CoV-2 Infection Severity Is Linked to Superior Humoral Immunity against the Spike

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a global pandemic. The antigen specificity of the antibody response mounted against this novel virus is not understood in detail. Here, we report that subjects with a more severe SARS-CoV-2 infection exhibit a larger antibody response against the spike and nucleocapsid protein and epitope spreading to subdominant viral antigens, such as open reading frame 8 and nonstructural proteins. Subjects with a greater antibody response mounted a larger memory B cell response against the spike, but not the nucleocapsid protein. Additionally, we revealed that antibodies against the spike are still capable of binding the D614G spike mutant and cross-react with the SARS-CoV-1 receptor binding domain. Together, this study reveals that subjects with a more severe SARS-CoV-2 infection exhibit a greater overall antibody response to the spike and nucleocapsid protein and a larger memory B cell response against the spike.

60 APPLIED LIFE SCIENCES↗