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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Comparative Studies of Optoelectronic Properties, Structures, and Surface Morphologies for Phosphorus-Doped Poly-Si/SiOx Passivating Contacts

We investigated and compared optoelectronic properties, crystallographic structures, and nanoscale surface morphologies of ex-situ phosphorus-doped polycrystalline silicon (poly-Si)/SiO x passivating contacts, formed by different deposition methods (sputtering, plasma-enhanced chemical vapour deposition (PECVD), and low-pressure chemical vapour deposition (LPCVD)). Across all these deposition technologies, a similar trend is observed: higher diffusion temperatures yield films that are more crystalline but have rougher surface morphologies due to bigger surface crystal grains. Also, the recrystallization process of the as-deposited Si films starts from the SiO x interface, rather than from the film surface and bulk. However, there are some distinct differences among these technologies. Firstly, the LPCVD method yields the roughest surface and smallest degree of crystallinity on finished poly-Si films. In contrast, the PECVD method has the smoothest surface for both as-deposited Si and annealed poly-Si films. Secondly, as-deposited sputtered and PECVD Si films contain only an amorphous phase whereas as-deposited LPCVD films has already had some crystalline phase. Thirdly, the LPCVD phosphorus in-diffusion into the substrate depends strongly on the initial film thickness, whereas for the other two methods it is weakly dependent on thickness.

crystallographic structures↗
DOE OSTI2018

Comparative Study of Finite Element Methods Using the Time-Accuracy-Size(TAS) Spectrum Analysis

We present a performance analysis appropriate for comparing algorithms using different numerical discretizations. By taking into account the total time-to-solution, numerical accuracy with respect to an error norm, and the computation rate, a cost-benefit analysis can be performed to determine which algorithm and discretization are particularly suited for an application. This work extends the performance spectrum model in for interpretation of hardware and algorithmic tradeoffs in numerical PDE simulation. As a proof-of-concept, popular finite element software packages are used to illustrate this analysis for Poisson’s equation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Comparative study of Cs,O coadsorption on Ga{sub 0.75}Al{sub 0.25}N (0001) and (001) surfaces

Based on first principle calculations, we carried out comparative research of Cs,O coadsorption on Ga{sub 0.75}Al{sub 0.25}N (0001) surface and Ga{sub 0.75}Al{sub 0.25}N (001) (2 × 2) reconstruction surface. On the (0001) surface, the average coordinate difference between Cs and O atoms along [0001] direction is obviously smaller than that along [001] direction on (001) (2 × 2) reconstruction surface. This is due to the existence of dimers and trenches on the (001) (2 × 2) reconstruction surface. Cs atoms are favored to locate on the dimers while O atoms are favored to locate in the trenches. As a result, Cs–O dipoles on (001) (2 × 2) reconstruction surface make an important contribution on lowering work function. For both (0001) and (001) (2 × 2) reconstruction surface, work function values fluctuate up and down at the stage of Cs,O alternative adsorption, and the valley values are smaller and smaller. The site of Mg dopant has an influence on work function of the surfaces, which results in the “fish scale field” effect.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches

This paper explores the detection and localization of cyber-attacks on power systems, focusing on comparing conventional machine learning (ML) and deep learning methods, and graph neural network (GNN)-based techniques. We assess the detection accuracy of these approaches and their potential to pinpoint the locations of specific buses under attack. Given the demonstrated success of GNNs in other time series anomaly detection applications, we aim to evaluate their performance within the context of power systems cyber-attack. Utilizing the IEEE 68-bus system, we simulated four types of attacks to test the selected approaches. Our results indicate that GNN-based methods outperform conventional machine learning and deep learning models in detection. Additionally, GNNs show promise in accurately localizing attacks for simple scenarios, although they still face challenges in more complex cases.

artificial intelligence↗
DOE OSTI2023

Comparative Study and Validation of Photovoltaic Model Formulations for the IBPSA Modelica Library based on Rooftop Measurement Data

Domain-overarching system models are crucial to investigate sector coupling concepts. Specifically, the coupling of building and electrical energy systems becomes crucial to integrate renewable energy sources such as photovoltaic power systems (PV). For such interdisciplinary simulation models, Modelica is a suitable language. However, most open-source Modelica libraries are either domain-specific or lack simple-to-parameterize PV models. We close this gap by developing a PV model for the IBPSA Modelica library. The model comprises two I-V-characteristic models and three mounting-dependent approaches to calculate the cell temperature. The I-V-characteristic models follow a single- and two-diodes approach. This study uses measurement data from a rooftop PV system in Berlin, Germany, for validation. The focus lies on comparing the implemented single- and two-diodes approach. Results prove that both models accurately calculate the modules’ DC power output and cell temperature. The two-diodes approach slightly outperforms the single-diode one at the expense of a higher parameterization effort.

Maier, Laura↗
DOE OSTI2021

A Comparative Study of Joint Modeling Methods and Analysis of Fasteners [Slides]

Motivation: Crucial aspect of mechanical design is joining methodology of parts. Ability to analyze joint and fasteners in system for structural integrity is fundamental. Different modeling representations of fasteners include spring, beam, and solid elements. Various methods compared for linear system to decide method appropriate for design study. New method for modeling fastener joint is explored from full system perspective. Analysis results match well with published experimental data for new method.

42 ENGINEERING↗
DOE OSTI2020

A comparative study of thermal- and electrocatalytic conversion of furfural: methylfuran as a primary and major product

Controlling reaction selectivity represents a fundamental challenge in heterogeneous catalysis. Here, we compare the selectivity in aqueous-phase thermal catalytic and electrocatalytic conversion of furfural and report the fundamental difference in elementary steps in the two reaction systems. Specifically, we observed that furfural alcohol and 2-methylfuran, which is the hydrogenation and hydrogenolysis product of furfural, respectively, are both primary products in electrocatalysis over a Cu electrode, with 2-methylfuran dominating the product distribution under electrode potentials between -0.55 and -0.75 V versus RHE. By contrast, in an aqueous-phase thermal reaction using a SiO 2 -supported Cu catalyst, furfural alcohol and its derivatives from the ring-rearrangement reaction are the major products, without production of 2-methylfuran, at the reaction temperature between 140 and 200 °C. Here, we propose that the distinct selectivity trends for oxygenate conversion via thermal and electrocatalytic reduction result from the distinct sequence of proton attack to the aldehyde group and may be generally true for reduction reactions of other biomass-derived oxygenates.

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

Polyolefin plastic waste hydroconversion to fuels, lubricants, and waxes: a comparative study

Hydroconversion technologies have surged to the forefront of deconstructing plastic waste. Recent studies have been performed over several catalysts with varying conditions and plastics that make comparisons difficult. We compile and compare data from the literature by introducing various metrics and perform a simple energy analysis. We draw mechanistic similarities to and differences from the past literature on small alkane hydroconversion and leverage the former to propose standard approaches to tune product selectivity. We exemplify the plastics materials gap and the challenges it creates. Lastly, we discuss the current limitations and suggest future work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Rapid identification of homogeneous O 2 evolution catalysts and comparative studies of Ru(II)-carboxamides vs. Ru(II)-carboxylates in water-oxidation

One of the major challenges in developing sustainable energy storage systems is to discover robust oxygen evolving catalysts (OECs) that can efficiently oxidize water and produce oxygen, electrons and protons under highly oxidizing conditions. Thus, developing new screening methodologies and practical tools for OEC discovery is essential. We report a screening protocol which allows the accurate identification, benchmarking and kinetic analysis of molecular OECs using chemical oxidants, based on an oxygen-sensitive luminescent dissolved oxygen optical probe (DOOP). To demonstrate the practicality of this protocol, we screened more than 100 reactions to benchmark and evaluate the structure-activity relationship of several transition-metal complexes 1–16 based on Ru, Fe and Cu. Water-oxidation by Ru(II)-carboxylates is compared to structurally similar and less studied Ru(II)-carboxamides. A new Ru(II) complex 6 containing the doubly deprotonated N,N'-bis(aryl)pyridine-2,6-dicarboxamide was also synthesized and its oxygen evolution rate was found to be dramatically enhanced at neutral pH.

25 ENERGY STORAGE↗
DOE OSTI2023

A Comparative Study of Electrical Double Layer Effects for CO Reduction Reaction Kinetics

Solvation models describe how the interactions between the solutes and solvents affect the reactivity and selectivity in electrochemical processes. In this study, we developed a framework for evaluating the effects of applied potential and electrical double layer on CO reduction (COR), comparing fully explicit, implicit, and hybrid solvation models at the standard hydrogen electrode (SHE) scale. We analyzed all crucial intermediates leading to the production of C 1 and C 2 products and found good agreement across these models. Some notable differences were observed in the implicit description of *C and *CO adsorption at higher overpotentials and overall trends in the adsorption energies within the hybrid model. Using this unified SHE framework, we built comparable microkinetic models for COR kinetics and rates. Despite small differences in thermodynamics, solvation-model-based microkinetic simulations showed good agreement for onset potentials against the benchmark experiment. Only qualitative difference was observed for C 1+ versus hydrogen evolution at high overpotentials for the implicit model. Finally, we constructed a generalized C 2 selectivity map in descriptor space (U SHE , ΔG electrolyte CO ), which highlights the limitations of a copper-based COR catalyst and guides the search for optimal descriptor parameters to maximize C 2 selectivity. In conclusion, these findings demonstrate the importance of considering electrical double layer effects in reduction reactions and offer a useful framework for comparing solvation models and predicting optimal electrochemical conditions for specific applications.

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

A comparative study of Higgs boson production from vector-boson fusion

The data taken in Run II at the Large Hadron Collider have started to probe Higgs boson production at high transverse momentum. Future data will provide a large sample of events with boosted Higgs boson topologies, allowing for a detailed understanding of electroweak Higgs boson plus two-jet production, and in particular the vector-boson fusion mode (VBF). We perform a detailed comparison of precision calculations for Higgs boson production in this channel, with particular emphasis on large Higgs boson transverse momenta, and on the jet radius dependence of the cross section. We study fixed-order predictions at next-to-leading order and next-to-next-to-leading order QCD, and compare the results to NLO plus parton shower (NLOPS) matched calculations. The impact of the NNLO corrections on the central predictions is mild, with inclusive scale uncertainties of the order of a few percent, which can increase with the imposition of kinematic cuts. We find good agreement between the fixed-order and matched calculations in non-Sudakov regions, and the various NLOPS predictions also agree well in the Sudakov regime. We analyze backgrounds to VBF Higgs boson production stemming from associated production, and from gluon-gluon fusion. At high Higgs boson transverse momenta, the Δy$_{jj}$ and/or m$_{jj}$ cuts typically used to enhance the VBF signal over background lead to a reduced efficiency. We examine this effect as a function of the jet radius and using different definitions of the tagging jets. QCD radiative corrections increase for all Higgs production modes with increasing Higgs boson p$_{T}$, but the proportionately larger increase in the gluon fusion channel results in a decrease of the gluon-gluon fusion background to electroweak Higgs plus two jet production upon requiring exclusive two-jet topologies. We study this effect in detail and contrast in particular a central jet veto with a global jet multiplicity requirement.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Large-Scale Interlaboratory DI-FT-ICR MS Comparability Study Employing Various Systems

We report ultra-high resolution mass spectrometry (UHR-MS) coupled with direct infusion (DI) electrospray ionization offers a fast solution for accurate untargeted profiling. Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometers have been shown to produce a wealth of insights into complex chemical systems because they enable unambiguous molecular formula assignment even if the vast majority of signals is of unknown identity. Inter-laboratory comparisons are required to apply this type of instrumentation in quality control (for food industry or pharmaceutics), large-scale environmental studies or clinical diagnostics. Extended comparisons employing different FT-ICR MS instruments with qualitative direct infusion analysis are scarce since the majority of detected compounds cannot be quantified. The extent to which observations can be reproduced by different laboratories remains unknown. We set up a preliminary study which encompassed a set of seventeen laboratories around the globe, diverse in instrumental characteristics and applications, to analyze the same sets of extracts from commercially available standard human blood plasma and Standard Reference Material (SRM) for blood plasma (SRM1950), which were delivered at different dilutions or spiked with different concentrations of pesticides. The aim of this study was to assess the extent to which the outputs of differently tuned FTICR mass spectrometers, with different technical specifications, are comparable for setting the frames of a future DI-FT-ICR MS ring trial. We concluded that a cluster of five laboratories, with diverse instrumental characteristics showed comparable and representative performance across all experiments, setting a reference to be used in a future ring trial on blood plasma.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

BOUT++ nonlinear simulation for a comparative study with the measured 2D ELM structures in the KSTAR H-mode plasma

Here, time-dependent nonlinear simulation using the BOUT++ 3-field model is performed for a study of edge localized mode (ELM) dynamics in KSTAR H-mode plasmas. The simulated results of the ELM evolution are directly compared with the 2D ELM structures, measured by electron cyclotron emission imaging systems, from the initial growth to crash phase. The simulation results are qualitatively in good agreement with the observations. Specifically, it is notable in simulation and experiment that higher-n modes in a quasi-steady state phase evolve into lower-n modes close to the ELM-crash in time. It indicates that lower-n modes may be responsible for the onset of the crash. A postanalysis confirms that nonlinear mode coupling between toroidal harmonics may drive the lower-n mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Photocatalytic Degradation of the Nanocomposite Film Comprising Polyvinyl Chloride (PVC) and Sonochemically Synthesized Iron-Doped Zinc Oxide: A Comparative Study of Performances Between Sunlight and UV Radiation

Photocatalytic degradation of a composite film made of PVC and Fe-doped ZnO nanoparticles under both UV and sunlight have been accomplished and compared. The metal-doped semiconductor nanoparticles were previously synthesized using ultrasound. The nanoparticles had average crystal size of 33.67 nm and a bandgap of 2.69 eV. Catalysts loading were varied. Composite film with 0.033 g Fe-ZnO per g PVC was found to be the optimum with respect to degradation and mechanical properties. Degradation was monitored by loss in weight. Under sunlight 12% degradation could be achieved with the composite film in 210 min whereas under UV light the degradation was 10%. For pure PVC film, the corresponding degradations were 4.2 and 4.84% respectively. A plausible mechanism has been suggested and validated with experimental results which fitted the proposed model satisfactorily.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2017

Phosphides or nitrides for better NLO properties? A detailed comparative study of alkali metal doped nano-cages

Highlights: • Alkali metal doped boron and aluminum phosphide nano-cages are evaluated for NLO properties. • The NLO response of phosphide nano-cages is compared with nitride nano-cages. • Phosphide nano-cages show better NLO properties than the corresponding nitride nano-cages. • Conductivity of doped nano-cages is much higher than pristine nano-cages. • MAl{sub 11}P{sub 12} and MB{sub 11}P{sub 12} show monotonic increase in hyperpolarizability with size of alkali metal. - Abstract: Phosphide nano-cages of MX{sub 12}P{sub 11} and MX{sub 11}P{sub 12} (where X = B or Al, M = Li, Na, K) are investigated theoretically for geometric, electronic and nonlinear optical (NLO) properties. Alkali metal is doped by replacing (a) aluminum or boron atom and (b) phosphorus atom. Alkali metal doping can remarkably alter the electronic and NLO properties of the nano-cages, regardless of doping site. HOMO-LUMO gap is decreased whereas first hyperpolarizability is enhanced significantly in doped AlP and BP nano-cages. PDOS (partial density of states) analysis suggests that diffuse excess electrons from alkali metal play important role in boosting NLO properties of phosphide nano-cages. TD-DFT calculations are performed to study the effect of alkali metal doping on absorption spectra. Red shift is observed in the UV–vis spectra of all considered nano-cages. Comparison of phosphide nano-cages with nitride nano-cages indicated that doping nitride nano-cages with alkali metal leads to greater hyperpolarizability and NLO response than doping phosphide nano-cages.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Conduction Heat Transfer through Solid in Porous Materials: A Comparative Study by Finite-Element Simulations and Effective Medium Approximations

The development of insulation materials with low effective thermal conductivity is essential for energy savings in various applications, including buildings, food services, pipe insulation, and refrigeration. Such materials can be developed by using micro- or nanoporous structures, as well as low-thermal conductivity gases and/or reduced pressure. A variety of effective medium approximation (EMA) models have been developed to study thermal transport through solid in porous structures. However, in many cases, the impacts of porosity on solid conduction are not well predicted by EMA models because of the assumptions made for simplification. Furthermore, the results can vary by a factor of up to 1.5, depending on the morphology of the pores. Hence, proper guidance is needed to choose the appropriate EMA model for a given morphology. This work presents a finite element method study using COMSOL Multiphysics software for various geometries, including hollow cubes, overlapping hollow spheres (normally stacked and tightly packed), and Voronoi structures, and compares the results to those obtained from various EMA models. Simulation results for the hollow cubes and Voronoi structures matched well with the Maxwell-Eucken and Russell model. The results for hollow spheres closely matched the Bauer model and the Glicksman model with fitting parameters. This work provides guidance on analyzing and designing insulation materials for energy savings in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Six Machine-Learning Methods for Predicting Hospital-Stay Duration for Patients with Sepsis: A Comparative Study

Sepsis is a life-threatening medical condition that, if not treated promptly, can result in tissue damage, organ failure, and death. According to the Centers for Disease Control, about 270,000 individuals die of sepsis in the US each year. Further, sepsis expenditures accounted for 13% of total US hospital costs in 2013, totaling more than $24 billion. Our project objectives were to determine if Machine Learning algorithms could reliably predict hospital stay duration for patients with sepsis. The data set we used has been de-identified and is freely available through the BupaR package. The data includes 1050 cases, 15214 events, and 16 types of actions related to sepsis patient care. First, we used process mining to determine how long each patient was in the hospital. Using BupaR’s functions, we created several process model graphs. These process models depict the movement of patients at a hospital and provide duration data for each patent case. Second, we identified outlier data and created two dataset versions: one with and one without outliers. We then applied the following analysis methods: Linear Regression, Random Forest, K-Nearest Neighbors, Neural Networks, XGBoost, and lightGBM. We compared the model validations for the six machine learning models using the same data-splitting method. We found that the XGBoost model had the best prediction accuracy of 73.9 percent for cases with outliers, and 79 percent for cases without outliers. We also found that the lightGBM model had the lowest mean absolute error between prediction and actual duration in days with 3.66 days for the case with outliers, and 2.4 days for the case without outliers. These two models outperformed the other four models. This work will be enhanced in the future by exploring new prediction algorithms and comparing them with the results of this study.

Chen, Lingtao↗