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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Controlled release of entrapped nanoparticles from thermoresponsive hydrogels with tunable network characteristics

Thermoresponsive hydrogels have been studied intensively for creating smart drug carriers and controlled drug delivery. Understanding the drug release kinetics and corresponding transport mechanisms of nanoparticles (NPs) in a thermoresponsive hydrogel network is the key to the successful design of smart drug delivery systems. Here, we construct a mesoscopic model of rigid NPs entrapped in a hydrogel network in an aqueous solution, where the hydrogel network is formed by cross-linked semiflexible polymers of thermoresponsive poly(N-isopropylacrylamide) (PNIPAM). By varying the temperature crossing the lower critical solution temperature of PNIPAM, we can significantly change the hydrogel network characteristics. We systematically investigate how the matrix porosity and the nanoparticle size affect the transport kinetics of NPs at different temperatures. Quantitative results on the mean-squared displacement and the van Hove displacement distributions of NPs show that all NPs entrapped in the smart hydrogels undergo subdiffusion at both low and high temperatures. For a coil state, the transport of NPs in the hydrogels can be enhanced by decreasing the matrix porosity of the polymer network and NPs' size. However, when the solution temperature is increased above the critical temperature, the hydrogel network collapses following the coil-to-globule transition, with the NPs tightly trapped in some local regions inside the hydrogels. Consequently, the NP diffusion coefficient can be reduced by two orders of magnitude, or the diffusion processes can even be completely stopped. These findings provide new insights for designing controlled drug release from stimuli-responsive hydrogels, including autonomously switch on/off drug release in response to physical and chemical stimuli.

42 ENGINEERING↗
NASA NTRS2000

NASA and Industry Benefits of ACTS High Speed Network Interoperability Experiments

This paper provides synopses of the design. implementation, and results of key high data rate communications experiments utilizing the technologies of NASA's Advanced Communications Technology Satellite (ACTS). Specifically, the network protocol and interoperability performance aspects will be highlighted. The objectives of these key experiments will be discussed in their relevant context to NASA missions, as well as, to the comprehensive communications industry. Discussion of the experiment implementation will highlight the technical aspects of hybrid network connectivity, a variety of high-speed interoperability architectures, a variety of network node platforms, protocol layers, internet-based applications, and new work focused on distinguishing between link errors and congestion. In addition, this paper describes the impact of leveraging government-industry partnerships to achieve technical progress and forge synergistic relationships. These relationships will be the key to success as NASA seeks to combine commercially available technology with its own internal technology developments to realize more robust and cost effective communications for space operations.

Zernic, M. J.↗
DOE OSTI2023

Quantum Communication Networks for Energy Applications: Review and Perspective

Abstract The energy sector is expected to undergo significant changes in the coming decades with the advent of new technologies, including smart grid development, microgrid expansion, increasing electric vehicle and renewable energy usage, and enhanced measures to minimize greenhouse gas emission, among others. In tandem, these changes are expected to create new opportunities for the deployment of quantum technologies within the energy sector. Building on the authors' previous reviews on the current state of and future opportunities for quantum sensing, quantum computing and quantum simulations for energy sector applications, this work provides an overview of recent progress in quantum networking and communications for the energy industry, with a focus on platforms, devices, and protocols, including quantum teleportation and quantum key distribution. Specific areas of relevance to the energy sector are then analyzed, including the role of quantum networks for greenhouse gas monitoring, secure data collection and transmission in smart grids, nuclear power plants’ safety, facilitating oil and gas exploration, and other energy‐relevant applications. This review concludes with a brief overview of areas for future innovation, including the need for platforms for simulating quantum networks, quantum material and platform design, and computational approaches to accelerate quantum protocol discovery and development.

Paudel, Hari P.↗
DOE OSTI2024

An additively manufactured near-eutectic Al-Ce-Ni-Mn-Zr alloy with high creep resistance

A new additively manufactured (AM) Al-7.5Ce-4.5Ni-0.4Mn-0.7Zr (wt.%) near-eutectic alloy is reported, which shows unprecedented creep resistance up to 400 °C (a homologous temperature of 0.72). The eutectic solidification microstructure comprises ~ 27 vol% of coarsening-resistant second phase network with an ultrafine (<100 nm) inter-phase spacing. Both Mn and Zr contribute to creep resistance of the alloy. Small amount of Mn addition promotes selection of coarsening resistant phases without compromising the alloy processability. Zr not only improves hot-tearing resistance, but further enhances the second phase coarsening resistance resulting in improved creep resistance. Neutron diffraction performed during creep deformation reveals that the underlying mechanism for creep resistance in this alloy is impedance to dislocation motion stemming from the ultrafine eutectic solidification microstructure, whereas load transfer strengthening becomes less effective as the creep temperature increases. The second phase forms a continuous network in the as-fabricated condition, which is maintained during long-term creep at 300 °C. However, this network is fragmented into fine dispersoids at higher temperatures. It is proposed that the rate-limiting deformation mechanism at 300–400 °C is (i) dislocation climb for the alloy with fragmented second phase dispersoids and (ii) Orowan looping for the alloy with a continuous second phase network. In conclusion, the present design of an AM-processable multicomponent eutectic alloy with high creep resistance can be applied to other metallic systems exhibiting eutectic reactions, with expected extreme creep resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tunable and Robust Optical and Structural Properties of a Cooperative Squaraine-Dye Aggregate-DNA DX-DAE Tile System

Molecular excitons, which are excitations delocalized over multiple dyes in a wavelike manner, are of interest for a wide range of applications, including quantum information science. Numerous studies have templated a variety of synthetic dyes via a DNA scaffold to induce dye aggregation to create molecular excitons upon photoexcitation. Dye aggregate optical properties are critically dependent on relative dye geometry and local environment; therefore, an understanding of dye-dye and DNA-dye interactions is critical for advancing toward more complex DNA-dye systems. The extensively studied DNA Holliday junction (HJ) and less-studied double-crossover (DX) tile motif are fundamental test beds for designing complex and ultimately modular DNA-dye architectures. Here, we report the first study of single-linked squaraine dye aggregation and exciton delocalization on a larger and more stable (compared with the HJ) DX tile motif. We first highlight a few DNA-dye constructs that support single dyes and aggregates with distinct optical properties that are both tunable—through sample design, buffer conditions, and heat treatment—and robust to environment changes, including transfer to solid phase. Next, we assess several experimental and design considerations that demonstrate directed dye-driven assembly of a novel double-tile DNA configuration. Our results demonstrate that single-linked squaraine dyes templated to DX tiles provide a viable research path to design and evaluate dye aggregate networks that support exciton delocalization. We include herein the first report of exciton delocalization in the solid phase in a DNA-dye construct. Additionally, our findings indicate that dye aggregation impacts the assembly of the DNA-dye construct, and, in some cases, thereby cooperates with the DNA to determine a final robust system configuration. Finally, we show that a controlled annealing schedule can be employed to promote the homogeneous assembly of DNA-dye constructs. The findings in this study contribute to the understanding of DNA-dye systems and the relevant factors involved in their directed assembly to achieve specific constructs with desirable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ionic-content-driven restructuring of spirobisindane ionene networks: implications for mechanics, self-healing, and gas transport

Polymers of intrinsic microporosity (PIMs) offer exceptional gas permeability but remain brittle and susceptible to physical aging, limiting their durability in separation applications. Here, we introduce a reconfigurable microporous polymer network that uniquely integrates permanent PIM microporosity with autonomous, intrinsic self-healing driven by imidazolium-based ionic motifs. Spirobisindane units generate the intrinsic free-volume architecture, while an imidazolium-containing polyamide ionene supplies dynamic ionic and hydrogen-bonding interactions that reorganize under mild activation. Incorporation of imidazolium-based ionic liquids further tunes cohesion, mobility, and densification, enabling the network to relax, re-associate, and retain microporosity without structural collapse. Through a comprehensive multiscale approach combining spectroscopy, scattering, thermal and mechanical characterization with all-atom molecular dynamics and density functional theory calculations, we elucidate how ionic content, as a single control parameter that reshapes free-volume distributions, modulates local coordination environments, and governs relaxation and healing kinetics. At intermediate ionic loadings, the networks achieve rapid, repeatable self-healing while maintaining CO$_2$ selectivity, demonstrating an optimal balance between segmental mobility and structural integrity. By establishing how hierarchical ionic interactions couple structure, dynamics, and transport in microporous ionene networks, this work provides generalizable design rules for adaptive soft-matter systems that require simultaneous mechanical resilience, reconfigurability, and selective gas transport.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Data-driven discovery of coordinates and governing equations

The discovery of governing equations from scientific data has the potential to transform data-rich fields that lack well-characterized quantitative descriptions. Advances in sparse regression are currently enabling the tractable identification of both the structure and parameters of a nonlinear dynamical system from data. The resulting models have the fewest terms necessary to describe the dynamics, balancing model complexity with descriptive ability, and thus promoting interpretability and generalizability. This provides an algorithmic approach to Occam’s razor for model discovery. However, this approach fundamentally relies on an effective coordinate system in which the dynamics have a simple representation. In this work, we design a custom deep autoencoder network to discover a coordinate transformation into a reduced space where the dynamics may be sparsely represented. Thus, we simultaneously learn the governing equations and the associated coordinate system. We demonstrate this approach on several example high-dimensional systems with low-dimensional behavior. The resulting modeling framework combines the strengths of deep neural networks for flexible representation and sparse identification of nonlinear dynamics (SINDy) for parsimonious models. This method places the discovery of coordinates and models on an equal footing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence–based image segmentation model such as Meta’s Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Federation in genomics pipelines: techniques and challenges

Federation is a popular concept in building distributed cyberinfrastructures, whereby computational resources are provided by multiple organizations through a unified portal, decreasing the complexity of moving data back and forth among multiple organizations. Federation has been used in bioinformatics only to a limited extent, namely, federation of datastores, e.g. SBGrid Consortium for structural biology and Gene Expression Omnibus (GEO) for functional genomics. Here, we posit that it is critical to federate both computational resources (CPU, GPU, FPGA, etc.) and datastores to support popular bioinformatics portals, with fast-increasing data volumes and increasing processing requirements. A prime example, and one that we discuss here, is in genomics and metagenomics. It is vital that the processing of the data be done without having to transport the data across large network distances. We exemplify our design and development through our experience with metagenomics-RAST (MG-RAST), the most popular metagenomics analysis pipeline. Currently, it is hosted completely at Argonne National Laboratory. Yet, through a recently started collaborative National Institutes of Health project, we are taking steps toward federating this infrastructure. Being a widely used resource, we have to move toward federation without disrupting 50 K annual users. Here, we describe the computational tools that will be useful for federating a bioinformatics infrastructure and the open research challenges that we see in federating such infrastructures. It is hoped that our manuscript can serve to spur greater federation of bioinformatics infrastructures by showing the steps involved, and thus, allow them to scale to support larger user bases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Ransomware Attack Modeling and Artificial Intelligence-Based Ransomware Detection for Digital Substations

Ransomware has become a serious threat to the current computing world, requiring immediate attention to prevent it. Ransomware attacks can also have disruptive impacts on operation of smart grids including digital substations. This paper provides a ransomware attack modeling method targeting disruptive operation of a digital substation and investigates an artificial intelligence (AI)-based ransomware detection approach. The proposed ransomware file detection model is designed by a convolutional neural network (CNN) using 2-D grayscale image files converted from binary files. Here, the experimental results show that the proposed method achieves 96.22% of ransomware detection accuracy.

artificial intelligence↗
DOE OSTI2016

An Application Development Platform for Neuromorphic Computing

Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic computing systems developed as a hardware based approach to the implementation of neural networks. They feature highly adaptive and programmable structural elements, which model arti cial neural networks with spiking behavior. We design them to solve problems using evolutionary optimization. In this paper, we highlight the current hardware and software implementations of DANNA, including their features, functionalities and performance. We then describe the development of an Application Development Platform (ADP) to support efficient application implementation and testing of DANNA based solutions. We conclude with future directions.

Dean, Mark↗
DOE OSTI2021

Virtual Framework for Development and Testing of Federation Software Stack

Softwarization of networked infrastructures combined with containerization of codes promises unprecedented computing capabilities distributed across the federations of computing systems and physical instruments. The development and testing of a software stack that implements these capabilities over an expensive physical production infrastructure is not cost-effective, and in the early stages, may potentially cause service disruptions. To address these aspects, we develop the Virtual Federated Science Instrument Environment (VFSIE), a digital twin of the physical infrastructure that emulates a multi-site federation. Each federated site is emulated using containers and virtual hosts that are connected over local-area networks, and the sites, in turn, are connected over an emulated wide-area network. We describe the framework design and implementation details. We also illustrate its application by emulating a federation of four laboratories that use Jupyter Notebook for computations and the EPICS software system for instrument control.

Al Najjar, Anees↗
DOE OSTI2018

167-PFlops Deep Learning for Electron Microscopy: From Learning Physics to Atomic Manipulation

An artificial intelligence system called MENNDL, which used 25,200 Nvidia Volta GPUs on Oak Ridge National Laboratory’s Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, asynchronous genetic algorithm augmented with a support vector machine to automatically find a superior deep learning network topology and hyper-parameter set than a human expert can find in months. For the application of electron microscopy, the system furthers the goal of improving our understanding of the electron-beam-matter interactions and real-time image-based feedback, which enables a huge step beyond human capacity toward nanofabricating materials automatically. Finally, MENNDL has been scaled to the 4,200 available nodes of Summit achieving a measured 152.5 PFlops, with an estimated sustained performance of 167 PFlops when the entire machine is available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

LINEBACKER: LINE-speed Bio-inspired Analysis and Characterization for Event Recognition

The cyber world is a complex domain, with digital systems mediating a wide spectrum of human and machine behaviors. While this is enabling a revolution in the way humans interact with each other and data, it also is exposing previously unreachable infrastructure to a worldwide set of actors. Existing solutions for intrusion detection and prevention that are signature-focused typically seek to detect anomalous and/or malicious activity for the sake of preventing or mitigating negative impacts. But a growing interest in behavior-based detection is driving new forms of analysis that move the emphasis from static indicators (e.g. rule-based alarms or tripwires) to behavioral indicators that accommodate a wider contextual perspective. Similar to cyber systems, biosystems have always existed in resource-constrained hostile environments where behaviors are tuned by context. So we look to biosystems as an inspiration for addressing behavior-based cyber challenges. In this paper, we introduce LINEBACKER, a behavior-model based approach to recognizing anomalous events in network traffic and present the design of this approach of bio-inspired and statistical models working in tandem to produce individualized alerting for a collection of systems. Preliminary results of these models operating on historic data are presented along with a plugin to support real-world cyber operations.

biosequence model↗
DOE OSTI2023

Coarse-to-fine Task-driven Inpainting for Geoscience Images

The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. As far as we know, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images, and they never consider the following geoscience task when developing inpainting methods. Here, this paper aims to repair the occluded regions for a better geoscience task performance and advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with the help of designed coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we propose a MaskMix based data augmentation method, which augments inpainting masks instead of augmenting original images, to exploit the limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method. The code is available at: https://github.com/HMS97/Task-driven-Inpainting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

IRNet: A General Purpose Deep Risidual Regression Framework for Materials Discovery

Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applying machine learning methods to create predictive models capable of mapping from composition and crystal structures to materials properties. Generally, these are regression problems with the input being a 1D vector composed of numerical attributes representing the material composition and/or crystal structure. While neural networks consisting of fully connected layers have been applied to such problems, their performance often suffers from the vanishing gradient problem when network depth is increased. Hence, predictive modeling for such tasks has been mainly limited to traditional machine learning techniques such as Random Forest. In this paper, we study and propose design principles for building deep regression networks composed of fully connected layers with numerical vectors as input. We introduce a novel deep regression network with individual residual learning, IRNet, that places shortcut connections after each layer so that each layer learns the residual mapping between its output and input. We use the problem of learning properties of inorganic materials from numerical attributes derived from material composition and/or crystal structure to compare IRNet’s performance against that of other machine learning techniques. Using multiple datasets from the Open Quantum Materials Database (OQMD) and Materials Project for training and evaluation, we show that IRNet provides significantly better prediction performance than the state-of-the-art machine learning approaches currently used by domain scientists. We also show that IRNet’s use of individual residual learning leads to better convergence during the training phase than when shortcut connections are between multi-layer stacks while maintaining the same number of parameters.

deep learning↗
DOE OSTI2024

Generic Multi-Layer Perceptron Inference Accelerator on FPGA (vneuron) v1.0

We have designed and implemented a neural network inference compute engine (vneuron) that can be deployed in the fabric of any FPGA without using special hardware accelerator primitive. The "vneuron" is purely written in verilog, and supports scalable neural network structure with fully connected layers and ReLU activation ( Multi-Layer Perceptron architecture) with 16 bits of precision. We have demonstrated it on an Xilinx Artix 7 FPGA for a 16-input, 8-output MLP with 3 layer, 1600 parameters. It takes 40 DSP48E and 40 BRAM18, and takes 131 clock cycles for computing (1048 ns when clocked at 125MHz). We include PyTorch quantization from a given floating point model, and provide behavioral verification simulation in the disclosed software package.

Du, Qiang↗