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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening ∗

Abstract Interface structures in complex oxides remain an active area of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D STEM datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO 3 and SrTiO 3 , using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We test the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and create a DCNN regression model to predict step positions. We quantify the applicability of the model to different thicknesses and the transferability of the approach. The method shown here is general and can be applied for any inverse imaging problem where forward models are present.

42 ENGINEERING↗
DOE OSTI2023

FAIR AI models in high energy physics

Abstract The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Philympics 2021: Prophage Predictions Perplex Programs

Most bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Here, we present a library of gold-standard bacterial genome annotations that include manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms to identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f 1 score from the analysis of seven different algorithms for the prediction of prophages. We discuss caveats and concerns in this analysis and how those concerns may be mitigated.

Roach, Michael J.↗
DOE OSTI2024

BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA

Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes, and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-seq or protein data, respectively, but not both. A further significant performance improvement integrating all three data types was made by the recently released GeneMark-ETP. We here present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS, and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under an assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperforms BRAKER1 and BRAKER2. The average transcript-level F1-score is increased by about 20 percentage points on average, whereas the difference is most pronounced for species with large and complex genomes. BRAKER3 also outperforms other existing tools, MAKER2, Funannotate, and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Coulomb Interaction-Driven Entanglement of Electrons on Helium

The generation and evolution of entanglement in many-body systems is an active area of research that spans multiple fields, from quantum information science to the simulation of quantum many-body systems encountered in condensed matter, subatomic physics, and quantum chemistry. Motivated by recent experiments exploring quantum information processing systems with electrons trapped above the surface of cryogenic noble gas substrates, we theoretically investigate the generation of entanglement between two electrons via their unscreened Coulomb interaction. The model system consists of two electrons confined in separate electrostatic traps that establish microwave-frequency quantized states of their motion. We compute the motional energy spectra of the electrons, as well as their entanglement, by diagonalizing the model Hamiltonian with respect to a single-particle Hartree product basis. We also compare our results with the predictions of an effective Hamiltonian. The computational procedure outlined here can be employed for device design and guidance of experimental implementations. In particular, the theoretical tools developed here can be used for fine-tuning and optimization of control parameters in future experiments with electrons trapped above the surface of superfluid helium or solid neon. Published by the American Physical Society 2024

Physics↗
DOE OSTI2023

Machine Learning Models for Network Traffic Classification in Programmable Logic

Network traffic classification via machine learning on network packet payloads has emerged as an active area of research for network security due to the high accuracy machine learning models have achieved in classifying payloads. For effective deployment as part of network security, these machine learning models must not only classify malicious packet payloads accurately, they must also identify anomalous payloads and perform inference at speeds generally faster than 10,000 packets per second to be effective. This work explores the in- ference speeds and accuracy of several neural network models implemented in programmable logic on various field programmable gate arrays (FPGA) including the Xilinx VC1902 and Xilinx Zynq Ultrascale+. This work also presents the design and performance of both an autoencoder and variational autoencoder programmed on the FPGA for identifying anomalous packet payloads. The performance benefits of the FPGA implementation for this type of packet payload inspection driven by machine learning are compared against graphics processing unit (GPU) inference implementations run on two state-of-the-art datacenter GPU devices, the NVIDIA V100 and A100. The model accuracy difference between the FPGA and GPU implementations was found to be 4% or less while the Xilinx VC1902 outperformed both the NVIDIA V100 and A100 for inference speeds on all the models explored except the variational autoencoder.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Investigation of AlGaN-Delta–GaN-Based UV Photodiodes in a Metal–Semiconductor–Metal Configuration for Efficient and Fast Solar Blind UV Sensing

Metal–semiconductor–metal (MSM) configured UV photodiodes (PD’s) were designed and fabricated on an AlGaN/GaN–based substrate for efficient and ultrafast UV detection. The purpose was to investigate the feasibility of obtaining efficient and ultrafast temporal response from these devices in the UV given the challenges associated with the formation of Schottky contacts on laterally oriented AlGaN/GaN thin films. Two sets of devices were implemented using Pt and Au as metal contacts with 5-µm finger width, 5-µm finger spacing, and a 50-µm x 50-µm active area. Spectral and voltage bias studies were done to establish the spectral profile and the effect of bias voltage on the responsivity of the detectors at 265 nm. Here, the best vertical MSM PD’s produced 0.6-A/W responsivity under 10-V bias voltage at 265 nm. Peak spectral responsivities were recorded as 1.35 A/W and 1.25 A/W at 240 nm for Pt and Au PD’s, respectively.

42 ENGINEERING↗
DOE OSTI2022

QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation

Quantum Machine Learning continues to be a highly active area of interest within Quantum Computing. Many of these approaches have adapted classical approaches to the quantum settings, such as QuantumFlow, etc. We push forward this trend, and demonstrate an adaption of the Classical Convolutional Neural Networks to quantum systems - namely QuCNN. QuCNN is a parameterised multi-quantum-state based neural network layer computing similarities between each quantum filter state and each quantum data state. With QuCNN, back propagation can be achieved through a single-ancilla qubit quantum routine. QuCNN is validated by applying a convolutional layer with a data state and a filter state over a small subset of MNIST images, comparing the backpropagated gradients, and training a filter state against an ideal target state.

high performance computing, quantum computing↗
DOE OSTI2024

Nanosecond Transient Validation of Surge Arrester Models to Predict Electromagnetic Pulse Response

The impact of high-altitude electromagnetic pulse events on the electric grid is not fully understood, and validated modeling of mitigations, such as lightning surge arresters (LSAs) is necessary to predict the propagation of very fast transients on the grid. Experimental validation of high frequency models for surge arresters is an active area of research. Further, this article serves to experimentally validate a previously defined ZnO LSA model using four metal-oxide varistor pucks and nanosecond scale pulses to measure voltage and current responses. The SPICE circuit models of the pucks showed good predictability when compared to the measured arrester response when accounting for a testbed inductance of approximately 100 nH. Additionally, the comparatively high capacitance of low-profile arresters show a favorable response to high-speed transients that indicates the potential for effective electromagnetic pulse mitigation with future materials design.

42 ENGINEERING↗
DOE OSTI2021

A Custom Low-Noise Silicon Photodiode Detector Designed for Use With X-Ray Capillary Optics

We present the first results of a custom low-noise silicon photodiode detector designed for use with X-ray capillary optics. This system is intended to perform as a smaller, lighter, and low-powered alternative to currently existing X-ray telescope technologies. The detector has an active area with a diameter of 100 μm, and its small size leads to a low capacitance of well under 1 pF and a leakage current of 1 pA or less at the depletion voltage. We tested the detector with both a discrete preamp and an ultralow-noise front-end application-specific integrated circuit (ASIC). The front-end ASIC, developed at Brookhaven National Laboratory, was designed for low-capacitance detectors but had not been previously tested with a detector. Here, we discuss the detector design, the experimental setup, and the resulting detector performance. Using radioactive laboratory sources, we measure a full-width at half-maximum (FWHM) of 211 ± 8 eV at 5.9 keV with the ASIC and are able to set the threshold as low as 0.6 keV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Assessing Anomaly-Based Intrusion Detection Configurations for Industrial Control Systems

To reduce cost and ease maintenance, industrial control systems (ICS) have adopted Ethernetbased interconnections that integrate operational technology (OT) systems with information technology (IT) networks. This integration has made these critical systems vulnerable to attack. Security solutions tailored to ICS environments are an active area of research. Anomalybased network intrusion detection systems are well-suited for these environments. Often these systems must be optimized for their specific environment. In prior work, we introduced a method for assessing the impact of various anomaly-based network IDS settings on security. This paper reviews the experimental outcomes when we applied our method to a full-scale ICS test bed using actual attacks. Our method provides new and valuable data to operators enabling more informed decisions about IDS configurations.

Gillen, Rob↗
DOE OSTI2020

Lineage-based functional types: characterising functional diversity to enhance the representation of ecological behaviour in Land Surface Models

Process-based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass-dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C 3 grasses and (sub)tropical C 4 grasses. Incorporation of species-level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) of dominant lineages to improve LSM representations. In this work, our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage-based functional types (LFTs), situated between species-level trait data and PFT-level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Metal-semiconductor-metal structured p-i-n GaN/AlGaN ultrafast detectors for deep-UV photodetection

We report the productive outcome of the design, fabrication, and testing of a new class of GaN/AlGaN-based ultrafast, sensitive photodiodes for deep-UV solar blind photodetection. Pt was employed as the interdigitated metal electrode to establish that while a Schottky contact was formed at the metal/semiconductor heterojunction, it was the quantum wells in the p-i-n vertical structure that produced the diode characteristics of the detectors. The metal–semiconductor–metal design retained its ultrafast property as expected in the p-i-n structure because the spacing between the interdigitated fingers was asymmetrically implemented to optimize the photogenerated carriers’ transit time from the quantum well to the external circuitry. Also, the vertical p-i-n structure provided its renowned efficient photocarrier generation, which was significantly enhanced by the delta quantum well architecture of the vertical epitaxial structure. The active area of the device was 300 μm × 300 μm with 5-μm finger width, and asymmetric electrode spacing of 2 μm, 3 μm, and 5 μm. Here, the best device had peak responsivity of 3.5 A/W under 262-nm illumination with full width half maximum of 337.2-ps and 37.5-ps rise times when biased with 20 V.

efficient↗
DOE OSTI2024

Two-dimensional perovskite templates for durable, efficient formamidinium perovskite solar cells

We present a design strategy for fabricating ultrastable phase-pure films of formamidinium lead iodide (FAPbI 3 ) by lattice templating using specific two-dimensional (2D) perovskites with FA as the cage cation. When a pure FAPbI 3 precursor solution is brought in contact with the 2D perovskite, the black phase forms preferentially at 100°C, much lower than the standard FAPbI 3 annealing temperature of 150°C. X-ray diffraction and optical spectroscopy suggest that the resulting FAPbI 3 film compresses slightly to acquire the (011) interplanar distances of the 2D perovskite seed. The 2D-templated bulk FAPbI 3 films exhibited an efficiency of 24.1% in a p-i-n architecture with 0.5–square centimeter active area and an exceptional durability, retaining 97% of their initial efficiency after 1000 hours under 85°C and maximum power point tracking.

14 SOLAR ENERGY↗
DOE OSTI2021

Damage Modeling of Solid Oxide Fuel Cells Accounting for Redox Effects

This work applies a multiscale mechanistic damage model developed for brittle ceramics and implemented in commercial finite element (FE) packages via user subroutines to study progressive damage in solid oxide fuel cells (SOFC) subjected to thermomechanical loading under normal operating and shutdown conditions including redox effects. The damage model captures the micromechanics of stiffness reduction due to material porosity change and microcracking and integrates the as-obtained stiffness reduction law into a continuum damage mechanics (CDM) formulation for the evolution of microcracks up to fracture. The volumetric “swelling” that occurs during redox is treated in constitutive modeling similarly to thermal expansion, but swelling strains are irreversible. Furthermore, this damage model was first validated through predictions of strength and stress-strain response for the SOFC electrode materials. Next, it has been applied to predict the potential for degradation in a generic planar SOFC stack with large active area cells. Multicell stack models were simulated in both co-flow and counter-flow configurations. In addition, a constant temperature redox cycle was also simulated to capture overall cell electrode damage due to volumetric swelling of the nickel (Ni)-based anode in the anode-supported cells.

25 ENERGY STORAGE↗
DOE OSTI2022

Degradation Mechanisms in Advanced MEAs for PEM Water Electrolyzers Fabricated by Reactive Spray Deposition Technology

Proton exchange membrane water electrolyzers (PEMWEs) have demonstrated enormous potential as the next generation hydrogen production technology. The main challenges that the state-of-the-art PEMWEs are currently facing are excessive cost and poor durability. Understanding the failure modes in PEMWEs is a key factor for improving their durability, lowering the precious metal loading, and hence cost reduction. Here, reactive spray deposition technology (RSDT) has been used to fabricate a membrane electrode assembly (MEA) with one order of magnitude lower Pt and Ir catalyst loadings (0.2–0.3 mg PGM cm –2 ) in comparison to the precious metal loadings in the stat-of-the-art commercial MEAs (2–3 mg PGM cm –2 ). As fabricated MEA with an active area of 86 cm 2 , has been tested for over 5000 h at steady-state conditions that are typical for an industrial hydrogen production system. Herein, we present and discuss the results from a comprehensive post-test analysis of the MEA of interest. The main degradation mechanisms, governing the performance loss in the RSDT fabricated MEA with ultra-low precious metal loadings, have been identified and discussed in detail. All failure modes are critically compared and the main degradation mechanism with the highest impact on the MEA performance loss among the others is identified.

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

On-Wafer Wide-Pore Anodic Aluminum Oxide

Anodized aluminum oxide (AAO) has been used as nanotemplates for nanomaterials and nanodevice fabrications. Microfabrication techniques are attracting attention for nanodevice synthesis. However, AAO requires a microfabrication-compatible substrate due to its brittleness. While there are studies that already show AAO on compatible substrates, the pore sizes may not be applicable for multicomponent nanodevices. In this study, wide pore AAOs with ohmic bottom contacts are fabricated on 76 mm Si wafers. Sputtering was used to deposit Al along with supporting layers to achieve this goal. A quiescent electropolishing technique was used to smooth the surface of Al. Standard photolithography was used to define the active area on the Al for anodization. Then 195 V two-step anodization was performed to fabricate wide pore AAOs with pore diameters ranging from 130 ± 32 nm to 400 ± 31 nm with interpore distance of 480 ± 47 nm. Furthermore, it also showed that the ordering of the pores depended on the current density over the more conventional anodization time.

25 ENERGY STORAGE↗
DOE OSTI2024

Stabilization of Ni-YSZ Anodes in Solid Oxide Fuel Cells using an ALD-Grown Aluminum Titanate Interlayer

The effect of an Al 2 TiO 5 (ALT) interlayer between Ni and YSZ on enhancing the thermal stability of Ni-YSZ solid oxide fuel cell was examined. Atomic layer deposition (ALD) was used to provide precise control of the structure and thickness of the ALT interlayer. The study's findings demonstrate that a 2 nm thick ALT interlayer deposited by ALD does not adversely affect the cell's ohmic resistance and effectively prevents Ni sintering and the loss of active area during high-temperature heat treatments. ALT layers thicker than 2 nm, although they enhanced Ni stability, were found to impede oxygen ion transport in the electrode and significantly increase the ohmic resistance of the cell, leading to a decline in performance.

25 ENERGY STORAGE↗