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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Tuning Metastability of Poly(3-hexyl thiophene) Solutions to Enable in Situ Atomic Force Microscopy Imaging of Surface Nucleation

Poly(3-hexyl thiophene) (P3HT), a workhorse material for the study of organic electronic devices, assembles into nanofibers (NFs) upon crystallization from solution, providing the means to tailor morphology and improve device performance. We investigate how P3HT regioregularity, molecular weight, and concentration influence the hysteresis between crystallization and melting temperatures of NFs. Hysteresis is found to increase with molecular weight and reach an asymptote for polymer chains above the chain folding threshold (Mn > 12 kg/mol). Regioregularity does not substantially affect the hysteresis above a level of 88%, while increasing polymer concentration is found to reduce hysteresis. This insight facilitates in situ high-resolution atomic force microscopy measurements that provide better understanding of the mechanism of NF crystallization on graphite. We find an interchain distance (along the a crystallographic axis) of 16.7 ± 0.7 Å, and a spacing of 7.7 ± 0.2 Å along the c axis, suggesting that the crystals on graphite are form I.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Gate-tunable ReS{sub 2}/MoTe{sub 2} heterojunction with high-performance photodetection

Van der Waals heterojunctions based on atomically thin two-dimensional (2D) materials have attracted numerous attention for their special scientific research value and promising applications in photoelectric and micro-nano electronic devices. Especially, the carrier generation, separation, and extraction process in 2D materials can be easily modulated by external field, which may facilitate some multifunctional electronics and optoelectronics. In this paper, we report a unique type-II band alignment ReS{sub 2}/MoTe{sub 2} heterojunction with rectification inversion due to the fact the bottom few-layer MoTe{sub 2} can be easily tuned from p-type to n-type state through the applied back-gate voltage. Then we study photodetection properties of ReS{sub 2}/MoTe{sub 2} heterojunction, a relatively fast photoresponse time of 109 μs and a considerable photoresponsivity of 0.34 AW{sup −1} for 520 nm at room temperature show great potential in photodetection. Our studies of ReS{sub 2}/MoTe{sub 2} heterojunction with rectification inversion and high-performance photodetection will facilitate the development of electronics and optoelectronics based on atomically-thin heterojunctions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Formation of functionally graded steel by laser powder bed fusion via in-situ carbon doping

Additive Manufacturing (AM) enables functional integration by combining multiple components into a single part to shorten assembly time, reduce weight, and improve performance. Laser Powder Bed Fusion (LPBF) is an important AM method due to excellent spatial resolution, surface finish, and material properties without the need for extensive post-processing. Functional integration could be enhanced by spatial tuning of properties, but LPBF cannot readily vary material composition. Here, this paper addresses a method to add spatial composition control by printing small quantities of dopants via liquid carrier prior to laser fusion. The impact of carbon black suspension added to select regions of a Stainless Steel 316 L powder bed on melt pool dimension, hardness, and porosity is reported. The distribution of the carbon between the doped and plain layers and the resulting spatial variation in hardness is measured. Optical microscopy and composition analysis show that the carbon dispersed uniformly within the layer of deposition and diffused as little as 50 μm in the build direction. Keyhole conditions dramatically increase the inter-layer transport of the dopant. The added carbon increased hardness by >50 %. Porosity increased in doped regions but remained below 1.5 % for the best processing parameters. These results demonstrate that the composition of LPBF parts could be controlled in 3-dimensions using a dopant that is soluble in the melt pool. Additional work will be required to evaluate different dopant materials and optimize processing conditions for full density, but microalloying with soluble dopants appears to be a plausible solution to enhance functional integration with LPBF.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Challenge of Characterizing High-Concentration Electrolytes at the Molecular Level: A Perspective

High-concentration electrolytes (HCEs) are promising materials composed of highly concentrated salt solutions in organic solvents. HCEs have many desirable properties and are particularly important in the field of batteries. However, the number of ways in which these materials can be tuned is very large, which is crucial for tailored electrolyte design. Moreover, the molecular characterization of HCEs is challenging both experimentally and computationally, but it is necessary for their rational design. Therefore, currently the structure–property–performance relationship of these electrolytes has not been directly derived from their characterization. Here, in this Perspective, we present a brief overview of the HCEs and discuss the state-of-the-art characterization methods used to study them at the molecular level. We also address the challenges associated with these methods, including both experimental techniques and computational tools currently available. Emphasis is placed on methods aimed at understanding the physical phenomena that govern the molecular structure and dynamics occurring on the subnanosecond and nanometer time and length scales. Finally, we discuss new strategies for obtaining a comprehensive characterization of HCEs at the molecular level.

electrolytes↗
DOE OSTI2026

Unraveling Fundamental Activity–Stability Relationships in Rutile Oxides

The oxygen evolution reaction (OER) is a key anodic half-cell reaction that accompanies several critical electrochemical reduction reactions of interest to a variety of applications. Despite steady advances in understanding and qualitatively predicting OER activity and selectivity trends, a comprehensive description or prediction of material aqueous (in)stability and degradation mechanisms remains elusive, even though these processes critically influence device lifetime and economic feasibility. In this work, we investigate the interplay, or lack thereof, between OER activity and material aqueous stability across rutile oxides, with a particular focus on iridium oxide (IrO 2 ). By applying a Born–Haber cycle, we calculate the thermodynamic driving force for metal dissolution as a function of the applied bias and electrolyte conditions. We apply interpretable machine learning techniques, including principal component analysis and symbolic regression, to analyze trends across rutile oxides and find that key thermodynamic descriptors for OER activity and surface stability are only very weakly correlated. Instead, the local atomic environment─especially electronic structure signatures for interactions between the active site and its neighbors─plays a more important role in predicting material stability. Leveraging these insights, we investigate the impact of doping IrO 2 with a range of transition metals and show that the stability of Ir active sites can be tuned largely independently of its predicted OER activity. These insights lay the foundation for material design to improve stability with respect to corrosion, with the ultimate aim to enhance long-term stability without sacrificing catalytic performance in the OER.

evolution reactions↗
DOE OSTI2016

Effective g-factors of carriers in inverted InAs/GaSb bilayers

We perform tilt-field transport experiment on inverted InAs/GaSb, which hosts quantum spin Hall insulator. By means of coincidence method, Landau level (LL) spectra of electron and hole carriers are systematically studied at different carrier densities tuned by gate voltages. When Fermi level stays in the conduction band, we observe LL crossing and anti-crossing behaviors at odd and even filling factors, respectively, with a corresponding g-factor of 11.5. It remains nearly constant for varying filling factors and electron densities. On the contrary, for GaSb holes, only a small Zeeman splitting is observed even at large tilt angles, indicating a g-factor of less than 3.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Comparative evaluation of analogue front-end designs for the CMS Inner Tracker at the High Luminosity LHC

The CMS Inner Tracker, made of silicon pixel modules, will be entirely replaced prior to the start of the High Luminosity LHC period. One of the crucial components of the new Inner Tracker system is the readout chip, being developed by the RD53 Collaboration, and in particular its analogue front-end, which receives the signal from the sensor and digitizes it. Three different analogue front-ends (Synchronous, Linear, and Differential) were designed and implemented in the RD53A demonstrator chip. A dedicated evaluation program was carried out to select the most suitable design to build a radiation tolerant pixel detector able to sustain high particle rates with high efficiency and a small fraction of spurious pixel hits. The test results showed that all three analogue front-ends presented strong points, but also limitations. The Differential front-end demonstrated very low noise, but the threshold tuning became problematic after irradiation. Moreover, a saturation in the preamplifier feedback loop affected the return of the signal to baseline and thus increased the dead time. The Synchronous front-end showed very good timing performance, but also higher noise. For the Linear front-end all of the parameters were within specification, although this design had the largest time walk. This limitation was addressed and mitigated in an improved design. The analysis of the advantages and disadvantages of the three front-ends in the context of the CMS Inner Tracker operation requirements led to the selection of the improved design Linear front-end for integration in the final CMS readout chip.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Auxiliary field Quantum Monte Carlo for dilute neutrons on the lattice

We employ constrained path Auxiliary Field Quantum Monte Carlo (AFQMC) in the pursuit of studying physical nuclear systems using a lattice formalism. Since AFQMC has been widely used in the study of condensed-matter systems such as the Hubbard model, we benchmark our method against published results for both one- and two-dimensional Hubbard model calculations. We then turn our attention to cold atomic and nuclear systems. We use an onsite contact interaction that can be tuned in order to reproduce the known scattering length and effective range of a given interaction. Developing this machinery allows us to extend our calculations to study nuclear systems within a lattice formalism. In conclusion, we perform initial calculations for a range of nuclear systems from two- to few-body neutron systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Polarization transmission in the Hadron Storage Ring of the Electron-Ion Collider

The successful operation of the future Electron-Ion Collider is contingent on maintaining high hadron beam polarization up to 275 GeV. The Hadron Storage Ring lattice, however, features a symmetry-breaking interaction region that excites strong, nonsystematic spin resonances, posing a significant threat to polarization preservation. This paper systematically investigates two complementary strategies to ensure high polarization transmission. The first method involves optimizing the vertical betatron phase advance between Siberian snakes to orchestrate a cancellation of depolarizing kicks across the ring. We demonstrate through simulations that both dynamic and fixed-optics solutions based on this principle can successfully preserve polarization through the strongest resonances. The second, more powerful approach involves optimizing the snake rotation axes to suppress resonance driving terms at their source. We revisit established symmetric configurations, such as the Lee-Courant schemes, and introduce a novel, highly symmetric “Doubly Lee-Courant” (DLC) scheme, which enforces a local 𝜋 spin phase advance across every consecutive pair of snakes. Our analysis reveals a clear performance hierarchy, with the DLC configuration providing an exceptionally robust and energy-insensitive baseline for polarization preservation. We conclude that a hybrid strategy, using a DLC snake scheme as a symmetric foundation and betatron phase tuning for fine corrections, offers the most effective path forward for the EIC and future high-energy polarized-beam facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Derivation of WECC Distributed PV System Model Parameters from Quasi-Static Time-Series Distribution System Simulations

The response of low voltage networks with high penetration of PV systems to transmission network faults will, in the future, determine the overall power system performance during certain hours of the year. The WECC distributed PV system model (PVD1) is designed to represent small-scale distribution-connected systems. Although default values are provided by WECC for the model parameters, tuning of those parameters seems to become important in order to accurately estimate the partial loss of distributed PV systems for bulk system studies. The objective of this paper is to describe a new methodology to determine the WECC distributed PV system (PVD1) model parameters and to derive parameter sets obtained for six distribution circuits of a Californian investor-owned utility with large amounts of distributed PV systems. The results indicate that the parameters for the partial loss of distributed PV systems may differ significantly from the default values provided by WECC.

14 SOLAR ENERGY↗
DOE OSTI2022

Direction-optimizing Label Propagation Framework for Structure Detection in Graphs: Design, Implementation, and Experimental Analysis

Label Propagation is not only a well-known machine learning algorithm for classification but also an effective method for discovering communities and connected components in networks. We propose a new Direction-optimizing Label Propagation Algorithm (DOLPA) framework that enhances the performance of the standard Label Propagation Algorithm (LPA), increases its scalability, and extends its versatility and application scope. As a central feature, the DOLPA framework relies on the use of frontiers and alternates between label push and label pull operations to attain high performance. It is formulated in such a way that the same basic algorithm can be used for finding communities or connected components in graphs by only changing the objective function used. Additionally, DOLPA has parameters for tuning the processing order of vertices in a graph to reduce the number of edges visited and improve the quality of solution obtained. We present the design and implementation of the enhanced algorithm as well as our shared-memory parallelization of it using OpenMP. We also present an extensive experimental evaluation of our implementations using the LFR benchmark and real-world networks drawn from various domains. Compared with an implementation of LPA for community detection available in a widely used network analysis software, we achieve at most five times the F-Score while maintaining similar runtime for graphs with overlapping communities. We also compare DOLPA against an implementation of the Louvain method for community detection using the same LFR-graphs and show that DOLPA achieves about three times the F-Score at just 10% of the runtime. For connected component decomposition, our algorithm achieves orders of magnitude speedups over the basic LP-based algorithm on large-diameter graphs, up to 13.2× speedup over the Shiloach-Vishkin algorithm, and up to 1.6× speedup over Afforest on an Intel Xeon processor using 40 threads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

MuyGPyS

MuyGPs is a GP estimation method that affords fast hyperparameter optimization by way of performing leave-one-out cross-validation. MuyGPs achieves best-in-class speed and scalability by limiting inference to the information contained in k nearest neighborhoods for prediction locations for both hyperparameter optimization and tuning. This feature affords the optimization of hyperparameters by way of leave-one-out cross-validation, as opposed to the more expensive loglikelihood evaluations requires by similar sparse methods

Priest, BenjaminW.↗
DOE OSTI2023

Fabrication-robust silicon photonic devices in standard sub-micron silicon-on-insulator processes

Perturbations to the effective refractive index from nanometer-scale fabrication variations in waveguide geometry plague high index-contrast photonic platforms; this includes the ubiquitous sub-micron silicon-on-insulator (SOI) process. Such variations are particularly troublesome for phase-sensitive devices, such as interferometers and resonators, which exhibit drastic changes in performance as a result of these fabrication-induced phase errors. In this Letter, we propose and experimentally demonstrate a design methodology for dramatically reducing device sensitivity to silicon width variations. We apply this methodology to a highly phase-sensitive device, the ring-assisted Mach–Zehnder interferometer (RAMZI), and show comparable performance and footprint to state-of-the-art devices, while substantially reducing stochastic phase errors from etch variations. This decrease in sensitivity is directly realized as energy savings by significantly reducing the required corrective thermal tuning power, providing a promising path toward ultra-energy-efficient large-scale silicon photonic circuits.

Rizzo, Anthony (ORCID:000000034752797X)↗
DOE OSTI2017

Advanced and Exploratory Shock Sensing Mechanisms.

Mechanical component response to shock environments must be predictable in order to ensure reliability and safety. Whether the shock input results from accidental drops during transportation to projectile impact scenarios, the system must irreversibly transition into a safe state that is incapable of triggering the component . With this critical need in mind, the 2017 Nuclear Weapons Summer Product Realization Institute (NW SPRINT) program objective sought the design of a passive shock failsafe with emphasis on additively manufactured (AM) components. Team Advanced and Exploratory (A&E) responded to the challenge by designing and delivering multiple passive shock sensing mech anisms that activate within a prescribed mechanical shock threshold. These AM failsafe designs were tuned and validated using analytical and computational techniques including the shock response spectrum (SRS) and finite element analysis (FEA). After rapid prototyping, the devices experienced physical shock tests conducted on Sandia drop tables to experimentally verify performance. Keywords: Additive manufacturing, dynamic system, failsafe, finite element analysis, mechanical shock, NW SPRINT, shock respon se spectrum

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2018

Implementing Neural Adaptive Filtering in Engineered Detection Systems

The retina plays an important role in animal vision --- namely to pre-process visual information before sending it to the brain. The goal of this LDRD was to develop models of motion-sensitive retinal cells for the purpose of developing retinal-inspired algorithms to be applied to real-world data specific to Sandia's national security missions. We specifically focus on detection of small, dim moving targets amidst varying types of clutter or distractor signals. We compare a classic motion-sensitive model, the Hassenstein-Reichardt model, to a model of the OMS (object motion- sensitive) cell, and find that the Reichardt model performs better under continuous clutter (e.g. white noise) but is very sensitive to particular stimulus conditions (e.g. target velocity). We also demonstrate that lateral inhibition, a ubiquitous characteristic of neural circuitry, can effect target-size tuning, improving detection specifically of small targets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

sPHENIX heavy flavor jet tagging studies in p+p at $\sqrt{s_{NN}}=200~GeV$

Heavy-flavor jets, which are initiated from heavy quarks, are ideal probes for studying flavor dependent parton energy loss. We report on the performance of jet flavor tagging using two Neural Network Machine Learning (ML) models: the Long Short-Term Memory (LSTM) model and an Attention-based Neural Network, in simulations of 200 GeV p + p collisions. The tagging performance of bottom quark initiated jets with both ML models surpasses that of the traditional cut-based method. Technical details, including sample and kinematic variable selections, the machine learning training and testing setup with parameter tuning, and outcome comparisons, will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Comparative study and test on a two-body wave energy point absorber under various constraints.

Power generation and motion response of the floating wave energy converters is of great importance in the research topic of ocean energy harvesting. As a classic design for harvesting the ocean wave energy, the wave energy point absorber is commonly studied theoretically using the idealized 1DOF and 2DOF dynamic model based on the number of the free body within the system. Whereas the mooring system is usually ignored and only the heave motion is considered. In this paper, a point absorber is designed prototyped and tested in a water tank under three different constrains, the single body 1DOF, the two body 2DOF and the two body 6DOF. All the designs and configurations are presented in detail. Numerical analysis is conducted and presented as well to help understand the test results deeper. The test results indicate that the two body system can be tuned to match with the wave excitation and achieve higher energy absorption. In addition, the compared results between the 2DOF configuration and 6DOF configuration show that the classic 2DOF dynamic model is able to predict the performance of the point absorber with mooring well.

Li, Xiaofan↗
DOE OSTI2020

FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks

Parallel training of an ensemble of Deep Neural Networks (DNN) on a cluster of nodes is an effective approach to shorten the process of neural network architecture search and hyper-parameter tuning for a given learning task. Prior efforts have shown that data sharing, where the common preprocessing operation is shared across the DNN training pipelines, saves computational resources and improves pipeline efficiency. Data sharing strategy, however, performs poorly for a heterogeneous set of DNNs where each DNN has varying computational needs and thus different training rate and convergence speed. This paper proposes FLEET, a flexible ensemble DNN training framework for efficiently training a heterogeneous set of DNNs. We build FLEET via several technical innovations. We theoretically prove that an optimal resource allocation is NP-hard and propose a greedy algorithm to efficiently allocate resources for training each DNN with data sharing. We integrate data-parallel DNN training into ensemble training to mitigate the differences in training rates and introduce checkpointing into this context to address the issue of different convergence speeds. Experiments show that FLEET significantly improves the training efficiency of DNN ensembles without compromising the quality of the result.

Guan, Hui↗