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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Enhanced thermoelectric performance in Cu-intercalated BiTeI by compensation weakening induced mobility improvement

The low weighted carrier mobility has long been considered to be the key challenge for improvement of thermoelectric (TE) performance in BiTeI. The Rashba-effect-induced two-dimensional density of states in this bulk semiconductor is beneficial for thermopower enhancement, which makes it a prospective compound for TE applications. In this report, we show that intercalation of minor Cu-dopants can substantially alter the equilibria of defect reactions, selectively mediate the donor-acceptor compensation, and tune the defect concentration in the carrier conductive network. Consequently, the potential fluctuations responsible for electron scattering are reduced and the carrier mobility in BiTeI can be enhanced by a factor of two to three between 10 K and 300 K. The carrier concentration can also be optimized by tuning the Te/I composition ratio, leading to higher thermopower in this Rashba system. Cu-intercalation in BiTeI gives rise to higher power factor, slightly lower lattice thermal conductivity, and consequently improved figure of merit. Compared with pristine BiTe 0.98 I 1.02 , the TE performance in Cu 0.05 BiTeI reveals a 150% and 20% enhancement at 300 and 520 K, respectively. Ultimately, these results demonstrate that defect equilibria mediated by selective doping in complex TE and energy materials could be an effective approach to carrier mobility and performance optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Modeling Heavy/Medium-Duty Fuel Consumption Based on Drive Cycle Properties

This paper presents multiple methods for predicting heavy/medium-duty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel truck operating over the Heavy Heavy-Duty Diesel Truck (HHDDT), City Suburban Heavy Vehicle Cycle (CSHVC), New York Composite Cycle (NYCC), and hydraulic hybrid vehicle (HHV) drive cycles. Each model was trained using one of four drive cycles as a training cycle and the other three as testing cycles. By comparing the training and testing results, a representative training cycle was chosen and used to further tune each method. HHDDT as the training cycle gave the best predictive results, because HHDDT contains a variety of drive characteristics, such as high speed, acceleration, idling, and deceleration. Among the four model approaches, MARS gave the best predictive performance, with an average absolute percent error of -1.84% over the four chassis dynamometer drive cycles. To further evaluate the accuracy of the predictive models, the approaches were first applied to real-world data. MARS outperformed the other three approaches, providing an average absolute percent error of -2.2% of four real-world road segments. The MARS model performance was then compared to HHDDT, CSHVC, NYCC, and HHV drive cycles with the performance from Future Automotive System Technology Simulator (FASTSim). The results indicated that the MARS method achieved a comparative predictive performance with FASTSim.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Colossal Cryogenic Electro‐Optic Response Through Metastability in Strained BaTiO 3 Thin Films

The search for thin film electro-optic materials that can retain superior performance under cryogenic conditions has become critical for quantum computing. Barium titanate thin films show large linear electro-optic coefficients in the tetragonal phase at room temperature, which is severely degraded down to ≈200 pm V −1 in the rhombohedral phase at cryogenic temperatures. There is immense interest in manipulating these phase transformations and retaining superior electro-optic properties down to liquid helium temperature. Utilizing the thermodynamic theory of optical properties, a large low-temperature electro-optic response is designed by engineering the energetic competition between different ferroelectric phases, leading to a low-symmetry monoclinic phase with a massive electro-optic response. The existence of this phase is demonstrated in a strain-tuned BaTiO 3 thin film that exhibits a linear electro-optic coefficient of 2516 ± 100 pm V −1 at 5 K, which is an order of magnitude higher than the best reported performance thus far. Importantly, the electro-optic coefficient increases by 100 × during cooling, unlike the conventional films, where it degrades. Further, at the lowest temperature, significant higher order electro-optic responses also emerge. These results represent a new framework for designing materials with property enhancements by stabilizing highly tunable metastable phases with strain.

barium titanate↗
DOE OSTI2025

Atomically Fine-Tuning Organic–Inorganic Carbon Molecular Sieve Membranes for Hydrogen Production

Polymeric membranes with great processability are attractive for the H 2 /CO 2 separation required for hydrogen production from renewable biomass with carbon capture for utilization and sequestration. However, it remains elusive to engineer polymer architectures to obtain desired sub-3.3 Å ultramicropores to efficiently sieve H 2 from CO 2 . Herein, we demonstrate a scalable way of carbonizing polybenzimidazole (PBI) at low temperatures, followed by vapor phase infiltration (VPI) to atomically narrow ultramicropores throughout the films, forming hybrid organic–inorganic carbon molecular sieves (CMSs). One VPI cycle (100 s) for the PBI carbonized at 500 °C remarkably increases H 2 /CO 2 selectivity from 9.6 to 83 at 100 °C, surpassing Robeson’s upper bound. The CMS demonstrates a stable H 2 /CO 2 separation performance when challenged with simulated syngas streams and can be fabricated into thin-film composite membranes, outperforming state-of-the-art membranes. Finally, the scalable approach can be ubiquitous to molecularly fine-tune ultramicropores of leading polymeric membranes to further improve their size-sieving ability and thus separation efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Solving a trillion unknowns per second with HPGMG on Sunway TaihuLight

Benchmarks for supercomputers are important tools, not only for evaluating and ranking modern supercomputers, but also for providing hints for future architecture design. As a new benchmark, HPGMG (high performance geometric multigrid) solves a linear equation set with a full geometric multi-grid algorithm. It involves computation on different scales, data movement with various volumes, global communication and neighbor communication with both large and small messages, etc., and is more correlated to real world applications than traditional benchmarks such as LINPACK. Therefore, it is desirable to examine how well HPGMG can perform on leadership supercomputers such as Sunway Taihulight. Sunway Taihulight, the No. 1 supercomputer in the Top 500 list from June 2016 to June 2018, which uses a specially designed many-core architecture SW26010, is of great interest to the community of high performance computing. With careful analysis and code design, we came up with an efficient implementation of HPGMG on SW26010 processors. We not only employed traditional optimization techniques such as 2.5D partitioning, double buffering, and collective data load, but also introduced a micro-benchmark to help with the choice of optimization direction and parameter tuning. Another contribution is that we proposed a new procedure for the major operations, by granulating and reordering the smooth function and the ghost exchange operation, leading to reduced memory copy and accelerated communication process. Our optimized implementation of HPGMG on Sunway TaihuLight achieved a ground-breaking performance of 1.036 × 10 12 Degrees of Freedom per second at the finest level, which is No. 1 on the HPGMG list of Nov 2017.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Crystal Structural Effect of AuCu Alloy Nanoparticles on Catalytic CO Oxidation

Controlling the physical and chemical properties of alloy nanoparticles (NPs) is an important approach to optimize NP catalysis. Unlike other tuning knobs, such as size, shape, and composition, crystal structure has received limited attention and not been well understood for its role in catalysis. This deficiency is mainly due to the difficulty in synthesis and fine-tuning of the NPs’ crystal structure. Here, Exemplifying by AuCu alloy NPs with face centered cubic (fcc) and face centered tetragonal (fct) structure, we demonstrate a remarkable difference in phase segregation and catalytic performance depending on the crystal structure. During the thermal treatment in air, the Cu component in fcc-AuCu alloy NPs segregates more easily onto the alloy surface as compared to that in fct-AuCu alloy NPs. As a result, after annealing at 250 °C in air for 1 h, the fcc- and fct-AuCu alloy NPs are phase transferred into Au/CuO and AuCu/CuO core/shell structures, respectively. More importantly, this variation in heterostructures introduces a significant difference in CO adsorption on two catalysts, leading to a largely enhanced catalytic activity of AuCu/CuO NP catalyst for CO oxidation. Furthermore, the same concept can be extended to other alloy NPs, making it possible to fine-tune NP catalysis for many different chemical reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Local Structure Analysis and Modelling of Lignin‐Based Carbon Composites through the Hierarchical Decomposition of the Radial Distribution Function

Abstract Carbonized lignin has been proposed as a sustainable and domestic source of activated, amorphous, graphitic, and nanostructured carbon for many industrial applications as the structure can be tuned through processing conditions. However, the inherent variability of lignin and its complex physicochemical structure resulting from feedstock and pulping selection make the Process‐Structure‐Property‐Performance (PSPP) relationships hard to define. In this work, radial distribution functions (RDFs) from synchrotron X‐ray and neutron scattering of lignin‐based carbon composites (LBCCs) are investigated using the Hierarchical Decomposition of the Radial Distribution Function (HDRDF) modelling method to characterize the local atomic environment and develop quantitative PSPP relationships. PSPP relationships for LBCCs defined by this work include crystallite size dependence on lignin feedstock as well as increasing crystalline volume fraction, nanoscale composite density, and crystallite size with increasing reduction temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Learning-based CO 2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation

There have been increasing concerns over the air quality inside buildings as high levels of bio-effluents can cause nausea, dizziness, headaches, and fatigue to the people working in those spaces. First published in 2004 as Standard 62.1, ASHRAE Standard 62.2-2019 requires highly occupied spaces to implement heating, ventilation, and air conditioning (HVAC) that can dilute contaminants produced by occupants. In this regard, occupant-centric ventilation control has been regarded as an effective practice to maintain a satisfactory indoor air quality (IAQ) when dealing with highly variable occupancy environments. However, few established models in current literature and practice consider dynamic occupancy behavior and adaptive IAQ control. To address this gap, a dynamic indoor CO2 model is constructed using machine learning algorithms to forecast CO2concentrations across a range of forecasting horizons. Herein, we tuned and compared six state-of-the-algorithms—including Support Vector Machine, Ada Boost, Random Forest, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. The algorithms’ performances are validated using CO 2 and historical meteorological data collected from a campus classroom with a variable occupancy rate. Simulation results showed that Multilayer Perceptron can strongly predict the volatile CO 2 behavior and also outperforms other algorithms in terms of accuracy. Furthermore, a control strategy capable of modeling and detecting dynamic patterns of CO 2 level is utilized to modulate the ventilation rate in real-time and also reduce the energy consumption. The proposed controller reduced the HVAC fan’s energy consumption by 51.4% and provide ventilation as needed per the ASHRAE standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

The impact of hot-press conditions on the durability of polymer electrolyte membrane fuel cells

The proton exchange membrane integrity can be compromised during hot-press fabrication of membrane electrode assemblies (MEAs) causing premature cell failures during operation. In this work, infrared (IR) thermography was used as a diagnostic tool to spatially visualize hydrogen (H 2 ) crossover and identify process-induced-membrane irregularities (PIMs). These irregularities were identified as seed locations for MEA failures. Fine tuning of hot-press conditions was used to mitigate premature cell failures informed by accelerated stress testing (AST). The impact of PIMs on the initial performance, high-frequency resistances, open-circuit voltage, and H 2 crossover are reported. Nafion XL and 212 membranes, hot-pressed with a force of 16 kg/cm 2 and temperature of 120°C, were found to be consistently irregularity-free. Irregularity-free MEAs using Nafion 211, 212, and XL membranes demonstrated AST lifetime improvements of 58, 64 and 400%, respectively, compared to those fabricated with non-optimized conditions. In conclusion, this work highlights the importance of fabrication parameters on premature cell failures.

08 HYDROGEN↗
DOE OSTI2015

Systematic Tuning and Multifunctionalization of Covalent Organic Polymers for Enhanced Carbon Capture

Porous covalent polymers are attracting increasing interest in the fields of gas adsorption, gas separation, and catalysis due to their fertile synthetic polymer chemistry, large internal surface areas, and ultrahigh hydrothermal stabilities. While precisely manipulating the porosities of porous organic materials for targeted applications remains challenging, we show how a large degree of diversity can be achieved in covalent organic polymers by incorporating multiple functionalities into a single framework, as is done for crystalline porous materials. In this work, we synthesized 17 novel porous covalent organic polymers (COPs) with finely tuned porosities, a wide range of Brunauer–Emmett–Teller (BET) specific surface areas of 430–3624 m 2 g –1 , and a broad range of pore volumes of 0.24–3.50 cm 3 g –1 , all achieved by tailoring the length and geometry of building blocks. Furthermore, we are the first to successfully incorporate more than three distinct functional groups into one phase for porous organic materials, which has been previously demonstrated in crystalline metal–organic frameworks (MOFs). COPs decorated with multiple functional groups in one phase can lead to enhanced properties that are not simply linear combinations of the pure component properties. For instance, in the dibromobenzene-lined frameworks, the bi- and multifunctionalized COPs exhibit selectivities for carbon dioxide over nitrogen twice as large as any of the singly functionalized COPs. These multifunctionalized frameworks also exhibit a lower parasitic energy cost for carbon capture at typical flue gas conditions than any of the singly functionalized frameworks. Despite the significant improvement, these frameworks do not yet outperform the current state-of-art technology for carbon capture. Nonetheless, the tuning strategy presented here opens up avenues for the design of novel catalysts, the synthesis of functional sensors from these materials, and the improvement in the performance of existing covalent organic polymers by multifunctionalization.

mathematical methods↗
DOE OSTI2021

Demonstration of a 20‐W membrane‐external‐cavity surface‐emitting laser for sodium guide star applications

Abstract We present a record 20‐W continuous‐wave output power membrane‐external‐cavity surface‐emitting laser (MECSEL) double‐bonded to silicon carbide (SiC) heat spreaders emitting around 1178 nm. We achieved a maximum slope efficiency of 27.7% and an optical‐to‐optical efficiency of 26.7%. By utilising an intracavity birefringent filtre, the wavelength was tuned over 75 nm and maximum output power of 12.7 W was recorded at 1178 nm. This study shows the potential of MECSELs to achieve high‐performance and cost‐effective optically pumped semiconductor disk lasers targeting applications in sodium guide stars.

Priante, D.↗
DOE OSTI2016

Structural origination of charge transfer complex nanostructures: Excellent candidate for field emission

Worldwide strategies for amalgamating rationally controlled one-dimensional organic nanowires are of fundamental importance for their applications in flexible, cheaper and lighter electronics. In this work we have fabricated large-area, ordered CuTCNQ (copper-7,7,8,8-tetracyanoquinodimethane) nano architecture arrays over flexible conducting substrate and discussed the rational growth and integration of nanostructures. Here we adopted the organic solid phase reaction (VLS) technique for the growth of organic hierarchies and investigated how field emission properties changes by tuning the nanostructures morphology i.e., by varying length, diameter, alignment and orientation over flexible substrate. The CuTCNQ nanowires with optimized geometry exhibit excellent high field emission performance with low turn-on and threshold field values. The result strongly indicate that CuTCNQ nanowires on flexible carbon cloth substrate are promising candidates for constructing cold cathode based emission display devices, vacuum nanoelectronics, and etc.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2017

Multi-frequency spin manipulation using rapidly tunable superconducting coplanar waveguide microresonators

In this work, we demonstrate the use of frequency-tunable superconducting NbTiN coplanar waveguide microresonators for multi-frequency pulsed electron spin resonance (ESR) experiments. By applying a bias current to the center pin, the resonance frequency (~7.6 GHz) can be continuously tuned by as much as 95 MHz in 270 ns without a change in the quality factor of 3000 at 2 K. We demonstrate the ESR performance of our resonators by measuring donor spin ensembles in silicon and show that adiabatic pulses can be used to overcome magnetic field inhomogeneities and microwave power limitations due to the applied bias current. We take advantage of the rapid tunability of these resonators to manipulate both phosphorus and arsenic spins in a single pulse sequence, demonstrating pulsed double electron-electron resonance. Our NbTiN resonator design is useful for multi-frequency pulsed ESR and should also have applications in experiments where spin ensembles are used as quantum memories.

42 ENGINEERING↗
DOE OSTI2020

Spectral and photochemical diversity of tandem cysteine cyanobacterial phytochromes

The atypical trichromatic cyanobacterial phytochrome NpTP1 from Nostoc punctiforme ATCC 29133 is a linear tetrapyrrole (bilin)-binding photoreceptor protein that possesses tandem-cysteine residues responsible for shifting its light-sensing maximum to the violet spectral region. Using bioinformatics and phylogenetic analyses, in this study we established that tandem-cysteine cyanobacterial phytochromes (TCCPs) compose a well-supported monophyletic phytochrome lineage distinct from prototypical red/far-red cyanobacterial phytochromes. To investigate the light-sensing diversity of this family, we compared the spectroscopic properties of NpTP1 (here renamed NpTCCP) with those of three phylogenetically diverged TCCPs identified in the draft genomes of Tolypothrix sp. PCC7910, Scytonema sp. PCC10023, and Gloeocapsa sp. PCC7513. Recombinant photosensory core modules of ToTCCP, ScTCCP, and GlTCCP exhibited violet-blue–absorbing dark-states consistent with dual thioether-linked phycocyanobilin (PCB) chromophores. Photoexcitation generated singly-linked photoproduct mixtures with variable ratios of yellow-orange and red-absorbing species. The photoproduct ratio was strongly influenced by pH and by mutagenesis of TCCP- and phytochrome-specific signature residues. Our experiments support the conclusion that both photoproduct species possess protonated 15E bilin chromophores, but differ in the ionization state of the noncanonical “second” cysteine sulfhydryl group. We found that the ionization state of this and other residues influences subsequent conformational change and downstream signal transmission. We also show that tandem-cysteine phytochromes present in eukaryotes possess similar amino acid substitutions within their chromophore-binding pocket, which tune their spectral properties in an analogous fashion. Taken together, our findings provide a roadmap for tailoring the wavelength specificity of plant phytochromes to optimize plant performance in diverse natural and artificial light environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Application of deep learning methods for beam size control during user operation at the Advanced Light Source

Past research at the Advanced Light Source (ALS) provided a proof-of-principle demonstration that deep learning methods could be effectively employed to compensate for the significant perturbations to the transverse electron beam size induced by user-controlled adjustments of the insertion devices. However, incorporating these methods into the ALS’ daily operations has faced notable challenges. The complexity of the system’s operational requirements and the significant upkeep demands has restricted their sustained application during user operation. Here, we introduce the development of a more robust neural network (NN)-based algorithm that utilizes a novel online fine-tuning approach and its systematic integration into the day-to-day machine operations. Our analysis emphasizes the process of NN model selection, demonstrates the superior performance of the NN-based method over traditional feedback methods, and examines the effectiveness and resilience of the new algorithm during user-operation scenarios. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Autonomous quantum absorption refrigerators

Here, we propose a quantum absorption refrigerator using the quantum physics of resonant tunneling through quantum dots. The cold and hot reservoirs are fermionic leads, tunnel coupled via quantum dots to a central fermionic cavity, and we propose configurations in which the heat absorbed from the (very hot) central cavity is used as a resource to selectively transfer heat from the cold reservoir on the left to the hot reservoir on the right. Heat transport in the device is particle-hole symmetric; we find two regimes of cooling as a function of the dot energies—symmetric with respect to the Fermi energy of the reservoirs—and we associate them with heat transfer by electrons above the Fermi level and holes below the Fermi level. We also discuss optimizing the cooling effect by fine-tuning the energy of the dots as well as their linewidth and characterize regimes where the transport is thermodynamically reversible such that the Carnot coefficient of performance is achieved with zero cooling power delivered.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Maps of ice wedge thermokarst pool expansion from twenty-seven circumpolar survey areas

This repository includes data and code to accompany the manuscript 'Topography controls variability in circumpolar permafrost thaw pond expansion' by Abolt et al. The data include satellite imagery and derived maps of thermokarst pools from twenty-seven survey areas in North America and Siberia. The code, written in MATLAB (R2021a), contains demonstrations of the workflow for generating the maps. The demonstrations include training a generalized UNet for mapping thermokarst pools using data from three survey areas, 'fine tuning' the UNet for use at a specific survey area using transfer learning, applying a trained UNet to infer thermokarst pool extent within satellite imagery, and performing histogram matching as a pre-processing step to improve satellite imagery contrast. Contains MATLAB script files and M files, TIF files, shape files, XML, Excel, TXT, and CSV files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Optimizing Geant4 Hadronic Models

Geant4, the leading detector simulation toolkit used in high energy physics, employs a set of physics models to simulate interactions of particles with matter across a wide range of energies. These models, especially the hadronic ones, rely largely on directly measured cross-sections and inclusive characteristics, and use physically motivated parameters. However, they generally aim to cover a broad range of possible simulation tasks and may not always be optimized for a particular process or a given material. The Geant4 collaboration recently made many parameters of the models accessible via a configuration interface. This opens a possibility to fit simulated distributions to the thin target experimental datasets and extract optimal values of the model parameters and the associated uncertainties. Such efforts are currently undertaken by the Geant4 collaboration with the goal of offering alternative sets of model parameters, also known as "tunes", for certain applications. The effort should subsequently lead to more accurate estimates of the systematic errors in physics measurements given the detector simulation role in performing the physics measurements. Results of the study are presented to illustrate how Geant4 model parameters can be optimized through applying fitting techniques, to improve the agreement between the Geant4 and the experimental data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗