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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

The ability to recreate synaptic functionalities in synthetic circuit elements is essential for neuromorphic computing systems that seek to emulate the cognitive powers of the brain with comparable efficiency and density. To date, silicon-based three-terminal transistors and two-terminal memristors have been widely used in neuromorphic circuits, in large part due to their ability to co-locate information processing and memory. Yet these devices cannot achieve the interconnectivity and complexity of the brain because they are power-hungry, fail to mimic key synaptic functionalities, and suffer from high noise and high switching voltages. To overcome these limitations, we have developed and characterized a biomolecular memristor that mimics the composition, structure, and switching characteristics of biological synapses. Here, we describe the process of assembling and characterizing biomolecular memristors consisting of a 5 nm-thick lipid bilayer formed between lipid-functionalized water droplets in oil and doped with voltage-activated alamethicin peptides. While similar assembly protocols have been used to investigate biophysical properties of droplet-supported lipid membranes and membrane-bound ion channels, this article focuses on key modifications of the droplet interface bilayer method essential for achieving consistent memristor performance. Specifically, we describe the liposome preparation process and the incorporation of alamethicin peptides in lipid bilayer membranes, and the appropriate concentrations of each constituent as well as their impact on the overall response of the memristors. As a result, we also detail the characterization process of biomolecular memristors, including measurement and analysis of memristive current-voltage relationships obtained via cyclic voltammetry, as well as short-term plasticity and learning in response to step-wise voltage pulse trains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit

Software vulnerabilities are flaws in computer systems that leave users open to attack. In many cases, these vulnerabilities go unnoticed and remain unresolved in codebases. Thus, awareness of software vulnerabilities among the public is crucial to ensure effective cybersecurity practices, the development of high quality software, and ultimately national security. This awareness can be better understood by studying the spread and evolution of software vulnerability discussions in online communities. This work is the first to evaluate and contrast how discussions about software vulnerabilities spread on three social platforms -- Twitter, GitHub, and Reddit. To lay the groundwork, we showcase a novel fundamental framework for measuring information spread that identifies the spread mechanisms and observables across platforms, the units of information, and the groups of measurements that can be applied to focus on a specific phenomena e.g., information cascades. We then analyze and contrast social network topologies for three example social networks and measure the scale and speed of the spread of discussion of specific vulnerabilities to understand how far and how widely they spread, how many users participate in discussions, and the duration of their spread. To demonstrate the awareness of more impactful software vulnerabilities, a subset of our analysis focuses on vulnerabilities targeted during recent major cyber attacks as well as vulnerabilities exploited by advanced persistent threat groups. We discover that usually, vulnerability discussions start on GitHub, before occurring on Twitter and Reddit. While studying how some user-level and content-level characteristics influence vulnerability spread, we observe that Twitter discussions started by users predicted to be humans have larger size, breadth, depth, adoption rate, lifetime, and structural virality compared to those started by users predicted to be bots. On Reddit, we contrast the differences in thread structure that originate from posts with positive, negative and neutral polarity. We find that posts that are positive have larger, deeper and wider discussions compared to negative and neutral posts. We anticipate the results of our analysis to not only increase the understanding of software vulnerability awareness but also inform models for simulating information spread across multiple social environments online.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ultrathin Magnesium-Based Coating as an Efficient Oxygen Barrier for Superconducting Circuit Materials

Scaling up superconducting quantum circuits based on transmon qubits necessitates substantial enhancements in qubit coherence time. Over recent years, tantalum (Ta) has emerged as a promising candidate for transmon qubits, surpassing conventional counterparts in terms of coherence time. However, amorphous surface Ta oxide layer may introduce dielectric loss, ultimately placing a limit on the coherence time. In this study, a novel approach for suppressing the formation of tantalum oxide using an ultrathin magnesium (Mg) capping layer is presented. Synchrotron-based X-ray photoelectron spectroscopy studies demonstrate that oxide is confined to an extremely thin region directly beneath the Mg/Ta interface. Additionally, it is demonstrated that the superconducting properties of thin Ta films are improved following the Mg capping, exhibiting sharper and higher-temperature transitions to superconductive and magnetically ordered states. Moreover, an atomic-scale mechanistic understanding of the role of the capping layer in protecting Ta from oxidation is established based on computational modeling. Further, this work provides valuable insights into the formation mechanism and functionality of surface tantalum oxide, as well as a new materials design principle with the potential to reduce dielectric loss in superconducting quantum materials. Ultimately, the findings pave the way for the realization of large-scale, high-performance quantum computing systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Materials Studies of Niobium Thin Films for Quantum Circuit Applications: Progress and Challenges

Niobium (Nb) films have emerged as a crucial material in the development of superconducting qubits, which are key components in quantum computing technology. Here, this review provides a comprehensive examination of Nb films from a materials perspective, focusing on their intrinsic properties, fabrication methods/techniques, and their influence on qubit performance, particularly through surface and interface driven loss mechanisms. We discuss the key material properties that are essential for qubit operation. Various deposition techniques for Nb thin films, such as sputtering, evaporation, molecular beam epitaxy, and atomic layer deposition, are explored, alongside their impact on film quality, uniformity, and qubit performance. Additionally, the influence of surface roughness, thin-film thickness, and substrate materials on quantum coherence is analyzed. Challenges such as defects and material degradation in Nb films are reviewed, along with strategies to mitigate these issues. Finally, we present the latest advancements and future directions in Nb film research, including potential improvements to enhance qubit coherence and scalability for large-scale quantum computing systems. Ultimately, a deeper understanding of surface and interface phenomena is essential for pushing the limits of qubit performance and realizing next-generation quantum technologies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Coordinative Entities: Forms of Organizing in Data Intensive Science

Scientific collaboration is a long-standing subject of CSCW scholarship that typically focuses on the development and use of computing systems to facilitate research. The research presented in this article investigates the sociality of science by identifying and describing particular, common forms of organizing that researchers in four different scientific realms employ to conduct work in both local contexts and as part of distributed, global projects. This paper introduces five prototypical forms of organizing we categorize as coordinative entities: the Principal Group, Intermittent Exchange, Sustained Aggregation, Federation, and Facility Organization. Coordinative entities as a categorization help specify, articulate, compare, and trace overlapping and evolving arrangements scientists use to facilitate data intensive research. We use this typology to unpack complexities of data intensive scientific collaboration in four cases, showing how scientists invoke different coordinative entities across three types of research activities: data collection, processing, and analysis. Finally, our contribution scrutinizes the sociality of scientific work to illustrate how these actors engage in relational work within and among diverse, dispersed forms of organizing across project, funding, and disciplinary boundaries.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

Gillespie’s direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie’s direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie’s direct method for network-free simulation. Lastly, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

New approach for stability and bifurcation analysis on predator–prey harvesting model for interval biological parameters with time delays

This paper presents a prey–predator harvesting model with time delay for bifurcation analysis. We consider the parameters of the proposed model with imprecise data as form of interval in nature, due to the lack of precise numerical information of the biological parameters such as prey population growth rate and predator population decay rate. The interaction between prey and predator is assumed to be governed by a Holling type II functional response and discrete type gestation delay of the predator for consumption of the prey under impreciseness of the biological parameters. Parametric functional form of interval number with two parameters is introduced. This study reveals that not only delay and harvesting effort play a significant role on the stability on the system but also interval parameters play a crucial role on the stability of the system. Computer simulations of numerical examples are given to explain our proposed imprecise model. We also address critically the biological implications of our analytical findings with proper numerical example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Capsule network-based semantic segmentation model for thermal anomaly identification on building envelopes

Thermography technology is widely used to inspect thermal anomalies in building façade systems. Computer vision-based techniques provide opportunities to autonomously detect such heat anomalies to significantly improve the efficiency of decision-making for building envelope retrofitting and maintenance. Here, in this work, we propose a novel Capsule Network-based deep learning model – CapsLab – that detects and identifies thermal anomalies by semantic segmentation. CapsLab is built based on our proposed prediction-tuning capsule (PT-Capsule) layer. Different from a traditional capsule layer, which consists of part-whole transformation and capsule-routing process, the proposed layer is composed of a prediction and tuning process, which helps decreasing the number of model parameters significantly. While the applicability of traditional Capsule Networks (CapsNets) has been limited to simpler tasks and smaller datasets due to their scalability issue, we can leverage the lightweight of the proposed PT-Capsule layer, and apply it to the semantic segmentation task. In this work, we also employ our previously presented performance metric, referred to as the Anomaly Identification Metric (AIM) (Kakillioglua et al. 2021), to evaluate the segmentation outputs. Traditional performance metrics do not accurately reflect the true performance of the segmentation models in thermal anomaly identification due to the high subjectivity in the annotation process and higher overlap ratio sensitivity of the standard metrics. AIM, on the other hand, is robust to these drawbacks. Experimental results show, both qualitatively and quantitatively, that our proposed segmentation method can effectively segment the thermal anomalies. Specifically, our model provides 9.38% and 13.53% improvements over the baseline model – DeepLabV3+ – based on traditional mIoU score and the AIM score, respectively, while requiring less model parameters and less computation at the same time. In addition, the scores that the AIM metric generates better align with the scores provided by building performance experts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

A crystal plasticity-based study of the relationship between microstructure and ultra-high-cycle fatigue life in nickel titanium alloys

Nickel Titanium (NiTi) alloys are often used in biomedical devices where failure due to mechanical fatigue is common. For other alloy systems, computational models have proven an effective means of determining the relationship between microstructural features and fatigue life. This work will extend the subset of those models which were based on crystal plasticity to examine the relationship between microstructure and fatigue life in NiTi alloys. It will explore the interaction between a spherical inclusion and the material’s free surface along with several NiTi microstructures reconstructed from 3D imaging. This work will determine the distance at which the free surface interacts with an inclusion and the effect of applied strain of surface-inclusion interaction. The effects of inclusion-inclusion interaction, matrix voiding, and matrix strengthening are explored and ranked with regards to their influence on fatigue life.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Phase-coexistence simulations of fluid mixtures by the Markov Chain Monte Carlo method using single-particle models

We present a single-particle Lennard–Jones (L-J) model for CO{sub 2} and N{sub 2}. Simplified L-J models for other small polyatomic molecules can be obtained following the methodology described herein. The phase-coexistence diagrams of single-component systems computed using the proposed single-particle models for CO{sub 2} and N{sub 2} agree well with experimental data over a wide range of temperatures. These diagrams are computed using the Markov Chain Monte Carlo method based on the Gibbs-NVT ensemble. This good agreement validates the proposed simplified models. That is, with properly selected parameters, the single-particle models have similar accuracy in predicting gas-phase properties as more complex, state-of-the-art molecular models. To further test these single-particle models, three binary mixtures of CH{sub 4}, CO{sub 2} and N{sub 2} are studied using a Gibbs-NPT ensemble. These results are compared against experimental data over a wide range of pressures. The single-particle model has similar accuracy in the gas phase as traditional models although its deviation in the liquid phase is greater. Since the single-particle model reduces the particle number and avoids the time-consuming Ewald summation used to evaluate Coulomb interactions, the proposed model improves the computational efficiency significantly, particularly in the case of high liquid density where the acceptance rate of the particle-swap trial move increases. We compare, at constant temperature and pressure, the Gibbs-NPT and Gibbs-NVT ensembles to analyze their performance differences and results consistency. As theoretically predicted, the agreement between the simulations implies that Gibbs-NVT can be used to validate Gibbs-NPT predictions when experimental data is not available.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Scaling methodologies and similarity analysis for thermal hydraulics test facility development for water-cooled small modular reactor

Small modular reactors (SMRs) represent a promising option for providing clean and sustainable energy due to their potential for enhanced safety, reduced capital costs, and increased siting flexibility. However, new reactor systems require the development and operation of representative scaled-down test facilities to support the verification and validation of system computer codes and models. Here this study reviews the research on scaling methodologies and similarity principles pivotal in developing non-nuclear integral effects test and separate effects test facilities for water-cooled SMRs. The study focuses on a review of the scaling methods, similarity approaches, and possible challenges posed by the unique and compact design features of integral-pressurized water reactor-type SMRs, and their representative test facilities. This study also reviews previous research related to scaling and similarity methodologies and provides insights into design considerations for achieving prototypic conditions in test facilities. The findings and recommendations emphasize the broader impact of appropriate scaling and similarity principles to ensure meaningful and transferable results from non-nuclear test facilities to accelerate the safe and efficient deployment of next-generation water-cooled SMRs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Neutralization of cement-asbestos waste by melting in an arc-resistance furnace

Highlights: • Interesting possibility for cement-asbestos wastes utilisation. • Product of thermal treatment of cement-asbestos may be considered as useful secondary raw material. • Complete destruction of asbestos fibrous structure by melting process in arc-resistance furnace. • Reduction of cement-asbestos waste melting point by flux addition. - Abstract: The paper presents the results of research on asbestos waste disposal by the melting process. The tests were carried out in a laboratory arc-resistance electric furnace. The obtained results showed that the fibrous structure of asbestos contained in cement-asbestos waste was completely destroyed. This led to the formation of new mineral phases without dangerous properties. The melting test was conducted on raw cement-asbestos samples without any additives and with a content of mineral compounds, the aim of which was to support the melting process. The additives were selected among others on the basis of the computer simulation results carried out using FactSage database computing system. The research results indicate that the melting process of asbestos wastes is a potential and interesting method of neutralizing hazardous asbestos waste, which allows for further treatment and material recycling.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Nanoscale Tracking of the High-Temperature Spin-State Transition in LaCoO 3

The high-temperature spin and electronic transitions in LaCoO 3 have recently been leveraged to create neuromorphic (brain-inspired) devices. While these devices have shown the potential for impactful functionality in next-generation computing systems, the nanoscale dynamics of the spin and electronic transitions that underlie their operation are not well understood. Inhomogeneities related to interfaces, electrode contacts, strain, and crystal defects can all affect device performance, making nanoscale characterization of the transitions essential for producing consistent and reliable devices. Here, we demonstrate the first nanoscale in situ measurement of the spin transition in LaCoO 3 at device-relevant temperatures (25–325 °C) over length scales of tens of nanometers using STEM-EELS. This measurement is enabled by an Al 2 O 3 coating, which prevents unwanted reduction of the LaCoO 3 specimen at high temperature and vacuum. The detailed understanding of LaCoO 3 transition dynamics enabled by such measurements will be crucial for optimizing LaCoO 3 -based neuromorphic devices and increasing reliability for real-world application.

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

Cavity Engineering of Photon–Phonon Interactions in Si Nanocavities

The ever-increasing demand for faster, smaller, and energy-efficient devices has pushed the frontiers of research toward silicon photonics to meet the challenges for fabricating the next generation of computing systems. In order to design new devices at the nanoscale, it is important to understand and be able to control material properties, which may differ significantly from their bulk counterparts. Here, we demonstrate very large tunability of phonon–photon interactions in Si nanowire cavities by engineering the cavity mode at the emission wavelength. Raman scattering measurements performed to quantify these interactions reveal that the anti-Stokes to Stokes scattering ratio can vary from 0.035 to 0.405 in Si nanowires compared to a value of 0.1 for bulk Si, demonstrating tunability by over an order of magnitude. Moreover, a ratio of 0.85 was attained at a temperature of 580 K, which is the highest value ever reported for Si. Cavity modes that can be easily changed by changing the nanowire diameter, cavity geometry, or excitation wavelength provide efficient ways of tuning these interactions. Nanocavity engineering offers a new approach for tuning phonon–photon interactions in silicon and opens up new avenues of research and applications in the fields of silicon photonics, Raman lasers, telecommunication, and optical cooling.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Mechanistic insights into chemical and photochemical transformations of bismuth vanadate photoanodes

Artificial photosynthesis relies on the availability of semiconductors that are chemically stable and can efficiently capture solar energy. Although metal oxide semiconductors have been investigated for their promise to resist oxidative attack, materials in this class can suffer from chemical and photochemical instability. Here we present a methodology for evaluating corrosion mechanisms and apply it to bismuth vanadate, a state-of-the-art photoanode. Analysis of changing morphology and composition under solar water splitting conditions reveals chemical instabilities that are not predicted from thermodynamic considerations of stable solid oxide phases, as represented by the Pourbaix diagram for the system. Computational modelling indicates that photoexcited charge carriers accumulated at the surface destabilize the lattice, and that self-passivation by formation of a chemically stable surface phase is kinetically hindered. Although chemical stability of metal oxides cannot be assumed, insight into corrosion mechanisms aids development of protection strategies and discovery of semiconductors with improved stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Three-dimensional crossbar arrays of self-rectifying Si/SiO 2 /Si memristors

Memristors are promising building blocks for the next generation memory, unconventional computing systems and beyond. Currently common materials used to build memristors are not necessarily compatible with the silicon dominant complementary metal-oxide-semiconductor (CMOS) technology. Furthermore, external selector devices or circuits are usually required in order for large memristor arrays to function properly, resulting in increased circuit complexity. Here we demonstrate fully CMOS-compatible, all-silicon based and self-rectifying memristors that negate the need for external selectors in large arrays. It consists of p- and n-type doped single crystalline silicon electrodes and a thin chemically produced silicon oxide switching layer. The device exhibits repeatable resistance switching behavior with high rectifying ratio (10 5 ), high ON/OFF conductance ratio (10 4 ) and attractive retention at 300 °C. We further build a 5-layer 3-dimensional (3D) crossbar array of 100 nm memristors by stacking fluid supported silicon membranes. The CMOS compatibility and self-rectifying behavior open up opportunities for mass production of memristor arrays and 3D hybrid circuits on full-wafer scale silicon and flexible substrates without increasing circuit complexity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Energy-efficient Mott activation neuron for full-hardware implementation of neural networks

To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, in this work, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal–oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.

electrical and electronic engineering↗
DOE OSTI2021

Modelling and control coordination scheme of a wind‐to‐hydrogen set for future renewable‐based power systems

New challenges regarding system stability and efficiency arise when power systems operate with a high penetration level of inverter‐based renewable sources (IBRSs) and few synchronous generators. Since IBRSs have been on the rise, to secure the stable operation of future power systems, IBRSs will be required to support systems without having to rely on remaining synchronous generators. Also, to efficiently manage the uncertainty of renewable production, power‐to‐gas technology can provide the required flexibility. This study proposes modelling and a control coordination scheme (CCS) of a wind‐to‐hydrogen (W2H) set to optimise electricity production from a variable‐speed wind turbine generator (WTG) while helping balance between supply and demand in a system. To achieve this, a grid‐forming (GFM) inverter‐based WTG is modelled and a set of electrolyser and fuel cell is integrated at the DC circuit of a GFM‐WTG to be coordinated. Furthermore, the CCS offers an opportunity to reduce the investment cost for deploying a W2H set by utilising the control capabilities of a WTG and reducing the need for an additional device. The performance of the proposed W2H set with the CCS was verified considering the variations in system load and wind speed by using Power System Computer Aided Design (PSCAD)/ElectroMagnetic Transients including Direct Current (EMTDC).

Kim, Jinho↗