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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High-dimensional control co-design of a wave energy converter with a novel pitch resonator power takeoff system

Researchers are exploring adding wave energy converters to existing oceanographic buoys to provide a predictable source of renewable power. A ”pitch resonator” power take-off system has been developed that generates power using a geared flywheel system designed to match resonance with the pitching motion of the buoy. However, the novelty of the concept leaves researchers uncertain about various design aspects of the system. This work presents a novel design study of a pitch resonator to inform design decisions for an upcoming deployment of the system. The assessment uses control co-design via WecOptTool to optimize control trajectories for maximal electrical power production while varying five design parameters of the pitch resonator. Given the large search space of the problem, the control trajectories are optimized within a Monte Carlo analysis to identify optimal designs, followed by parameter sweeps around the optimum to identify trends between the design parameters. The gear ratio between the pitch resonator spring and flywheel are found to be the most sensitive design variables to power performance. Finally, the assessment also finds similar power generation for various sizes of resonator components, suggesting that correctly designing for optimal control trajectories at resonance is more critical to the design than component sizing.

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

A combined STM and SPA-LEED study of the “explosive” nucleation and collective diffusion in Pb/Si(111)

A novel type of very fast nucleation was recently found in Pb/Si(111) with 4- to 7-layer high islands becoming crystalline in an “explosive” way, when the Pb deposited amount in the wetting layer is compressed to θ c ~ 1.22 ML, well above the metallic Pb(111) density. This “explosive” nucleation is very different from classical nucleation when island growth is more gradual and islands grow in size by single adatom aggregation [8]. In order to identify the key parameters that control the nucleation we used scanning tunneling microscopy (STM) and spot profile analysis low energy electron diffraction (SPA-LEED). It was found that the number and duration of steps in iterative deposition used to approach θc and the flux rate have dramatic effects on the crystallization process. Larger depositions over shorter times induce greater spatial coverage fluctuations, so local areas can reach the critical coverage θ c easier. This can trigger the collective motion of the wetting layer from far away to build the Pb islands “explosively”. Here, the SPA-LEED experiments show that even low flux experiments in iterative deposition experiments can trigger transfer of material to the superstable 7-layer islands, as seen from the stronger satellite rings close to the (00) spot.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Controlling Nucleation and Crystal Growth of Ge in a Liquid Metal Solvent

In this work, the electrochemical liquid–liquid–solid (ec-LLS) deposition of crystalline germanium (Ge) in a eutectic mixture of liquid gallium (Ga) and indium (In) was analyzed as a function of liquid metal thickness, process temperature, and flux. Through control of reaction parameters, conditions were identified that allow selective nucleation and growth of crystalline Ge at the interface between e-GaIn and a crystalline Si substrate. The crystal growth rates of Ge by ec-LLS as a function of process temperatures were obtained from time-dependent powder X-ray diffraction measurements of crystalline Ge. The driving force, Δμ, for crystal formation in ec-LLS was estimated through analyses of the experimental data in conjunction with predictions from a finite-difference model. The required Δμ for Ge nucleation was tantamount to a supersaturation approximately 102 larger than the equilibrium concentration of Ge in e-GaIn at the investigated temperatures. These points are discussed both in the context of advancing new, low-temperature synthetic methodologies for crystalline semiconductor films and on understanding semiconductor crystal growth more deeply.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Design of Experiments for Dynamic Test Runs in Solvent-Based CO 2 Capture Pilot Plants

Test runs in the pilot plants consume significant resources, and therefore, the learning from test runs should be maximized. Test runs conducted in the pilot plants are often steady state. It takes several hours for reaching steady-state in the pilot plants, and thus, the duration of the test runs needs to be long even for collecting few steady-state data points. On the other hand, a large number of measurements can be collected through dynamic test runs in a short span of time. This paper presents a systematic design of dynamic experiments (DoDEs) for identifiability of model parameters, which is achieved by persistently exciting the inputs signals. A pseudorandom binary sequence (PRBS) is designed as the input signal for DoDE due to its efficiency in obtaining sufficient spectral content. However, due to the long sequence size of the PRBS signal, a Schroeder-phase input signal, which is a multisine signal, is also designed. Tests for both types of signals are run in the Pilot Solvent Test Unit (PSTU) at the National Carbon Capture Center in Wilsonville, Alabama. The transient data are used to solve dynamic data reconciliation and parameter estimation problem. The estimated parameters are found to be not only superior to those estimated from using data collected from hundreds of steady-state test runs in a nonreactive (air–water) system, but the parameters could be estimated by using the dynamic data collected for about 24 h from the pilot plant for the MEA-H 2 O–CO 2 system.

CO2 capture↗
DOE OSTI2021

Evaluation of Force Fields for Molecular Dynamics Simulations of Platinum in Bulk and Nanoparticle Forms

Here, understanding the size- and shape-dependent properties of platinum nanoparticles is critical for enabling the design of nanoparticle-based applications with optimal and potentially tunable functionality. Toward this goal, we evaluated nine different empirical potentials with the purpose of accurately modeling faceted platinum nanoparticles using molecular dynamics simulation. First, the potentials were evaluated by computing bulk and surface properties - surface energy, lattice constant, stiffness constants, and the equation of state - and comparing these to prior experimental measurements and quantum mechanics calculations. Then, the potentials were assessed in terms of the stability of cubic and icosahedral nanoparticles with faces in the {100} and {111} planes, respectively. Although none of the force fields predicts all the evaluated properties with perfect accuracy, one potential - the embedded atom method formalism with a specific parameter set - was identified as best able to model platinum in both bulk and nanoparticle forms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

RDX Compression, α→ γ Phase Transition, and Shock Hugoniot Calculations from Density-Functional-Theory-Based Molecular Dynamics Simulations

Prediction of the density and lattice compression properties of the α and γ phases of the hexahydro-1,3,5-trinitro-1,3,5-s-triazine (RDX) crystal and of the low-pressure α → γ phase transition upon pressure increase are general tests used to assess the accuracy of density-functional-theory- (DFT-) based computational methods and to identify the essential parameters that govern the behavior of this high-energy-density material under extreme conditions. The majority of previous DFT studies have analyzed such issues under static optimization conditions by neglecting the corresponding temperature effects. In this paper, we extend previous investigations and analyze the performance of dispersion-corrected density functional theory to predict the compression of RDX in the pressure range of 0–9 GPa and the corresponding α → γ phase transition under realistic temperature and pressure conditions. We demonstrate that, by using static dispersion-corrected density functional theory calculations, direct interconversion between the α and γ phases upon compression is not observed. This limitation can be addressed by using isobaric–isothermal molecular dynamic simulations in conjunction with DFT-D2-calculated potentials, an approach that is shown to provide an accurate description of both the crystallographic RDX lattice parameters and the dynamical effects associated with the α→ γ phase transformation. An even more comprehensive and demanding analysis was done by predicting the corresponding shock Hugoniot curve of RDX in the pressure range of 0–9 GPa. It was found that the theoretical results reproduce reasonably well the available experimental Hugoniot shock data for both the α and γ phases. Finally, the results obtained demonstrate that a satisfactory prediction of the shock properties in high-energy-density materials undergoing crystallographic and configurational transformations is possible through the combined use of molecular dynamics simulations in the isobaric–isothermal ensemble with dispersion-corrected density functional theory methods.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Capacitive Energy Extraction by Few-Layer Graphene Electrodes

Capacitive double-layer expansion is a promising technology to harvest energy arising from the salinity difference between freshwater and seawater. Its optimal performance requires a careful selection of the operation potentials and electrode materials. While carbonaceous materials such as graphene and various forms of activated carbons are routinely used as the electrodes, there is little knowledge on how the quantum capacitance and the electric double-layer (EDL) capacitance, which are on the same order of magnitude, affect the capacitive performance. Toward understanding that from a theoretical perspective, here we study the capacitive energy extraction with graphene electrodes as a function of the number of graphene layers. The classical density functional theory is joined with the electronic density functional theory to obtain the EDL and the quantum capacitance, respectively. The theoretical results show that the quantum capacitance contribution plays a dominant role in extracting energy using the single-layer graphene, but its effect diminishes as the number of graphene layers increases. The overall extracted energy is dominated by the EDL contribution beyond about four graphene layers. Electrodes with more graphene layers are able to extract more energy at low charging potential. Here, because many porous carbons have nanopores with stacked graphene layers, our theoretical predictions are useful to identify optimal operation parameters for capacitive energy extraction with porous electrodes of different wall thickness.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Trap-Assisted Dopant Compensation Prevents Shunting in Poly-Si Passivating Interdigitated Back Contact Silicon Solar Cells

Using a trap-assisted compensation model, we explain why polycrystalline Si (poly-Si) passivating contacts are able to achieve low leakage current between the doped fingers of interdigitated back contact (IBC) monocrystalline Si solar cells despite mixing of boron and phosphorus dopants in the isolation region. The fill factor of IBC solar cells is strongly affected by the electrical isolation region between n- and p-type fingers, as this region is critical in minimizing shunting losses. During fabrication of monocrystalline Si solar cells with poly-Si passivating contacts, the intrinsic poly-Si isolation region inevitably gets contaminated with both p- and n-type dopants. Using dopant profiles measured with time-of-flight secondary ion mass spectrometry and scanning probe measurements of the isolation region, we demonstrate that despite the dopant spreading during cell processing, a well-compensated region between the doped fingers exists that prevents shunting. The trap-assisted dopant compensation mechanism significantly widens the compensated region to tens of microns, where the residual dopant densities are below the trap density. This enables a high-resistivity region, resulting in low shunt current. Using one-dimensional (1-D) finite element diode simulations, we identify the design parameters and experimental conditions under which a sufficiently resistive region can form. Furthermore, our measurements of 2-D local resistivity and work function maps across the isolation region using scanning spreading resistance microscopy and Kelvin probe force microscopy demonstrate the existence of a highly resistive, wide compensated region and confirm our proposed compensation mechanism. For our structures, this region is ~25 µm in width within a ~150 µm wide finger isolation region with nearly 3 orders of magnitude higher resistivity than the regions dominated by a single type of dopant.

14 SOLAR ENERGY↗
DOE OSTI2021

Impact of n -Butylammonium Bromide on the Chemical and Electronic Structure of Double-Cation Perovskite Thin Films

2D/3D perovskite heterostructures have emerged as a promising material composition to reduce nonradiative recombination in perovskite-based LEDs and solar cells. Such heterostructures can be created by a surface treatment with large organic cations, for example, n-butylammonium bromide (BABr). To understand the impact of the BABr surface treatment on the double-cation (Cs 0.17 FA 0.83 Pb(I 0.6 Br 0.4 ) 3 ) (FA = formamidinium) perovskite thin film and further optimize the corresponding structures, an in-depth understanding of the chemical and electronic properties of the involved surfaces, interfaces, and bulk is required. We study the impact of the BABr treatment with a combination of surface-sensitive X-ray photoelectron spectroscopy and bulk-sensitive resonant inelastic soft X-ray scattering (RIXS). A quantitative analysis of the BABr-treated perovskite thin film shows a modified chemical perovskite surface environment of carbon, nitrogen, bromine, iodine, and lead, indicating that the treatment leads to a perovskite surface with a modified composition and bonding structure. With K-edge RIXS, the local environment at the nitrogen and carbon atoms is probed, allowing us to identify the presence of BABr in the perovskite bulk albeit with a modified bonding environment. This, in turn, identifies a "hidden parameter" for the optimization of the BABr treatment and overall performance of 2D/3D perovskite solar cell absorbers.

2D/3D perovskite heterostructure↗
DOE OSTI2018

Machine-Learning Identification of the Sensing Descriptors Relevant in Molecular Interactions with Metal Nanoparticle-Decorated Nanotube Field-Effect Transistors

We report that carbon nanotube-based field-effect transistors (NTFETs) are ideal sensor devices as they provide rich information regarding carbon nanotube interactions with target analytes and have potential for miniaturization in diverse applications in medical, safety, environmental, and energy sectors. Herein, we investigate chemical detection with cross-sensitive NTFETs sensor arrays comprised of metal nanoparticle-decorated single-walled carbon nanotubes (SWCNTs). By combining analysis of NTFET device characteristics with supervised machine-learning algorithms, we have successfully discriminated among five selected purine compounds, adenine, guanine, xanthine, uric acid, and caffeine. Interactions of purine compounds with metal nanoparticle-decorated SWCNTs were corroborated by density functional theory calculations. Furthermore, by testing a variety of prepared as well as commercial solutions with and without caffeine, our approach accurately discerns the presence of caffeine in 95% of the samples with 48 features using a linear discriminant analysis and in 93.4% of the samples with only 11 features when using a support vector machine analysis. Lastly, we also performed recursive feature elimination and identified three NTFET parameters, transconductance, threshold voltage, and minimum conductance, as the most crucial features to analyte prediction accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Kinetic Model for the Reduction of Cu II Sites by NO + NH 3 and Reoxidation of NH 3 -Solvated Cu I Sites by O 2 and NO in Cu-SSZ-13

In this work, a kinetic model is developed for the reduction of Cu II sites by NO + NH 3 and the reoxidation of NH 3 -solvated Cu I sites by O 2 and NO in Cu-SSZ-13. Fourier transform infrared (FTIR) spectroscopy and spatially resolved capillary inlet mass spectrometry (SpaciMS) measurements during transient reactor experiments are utilized to identify the rate parameters associated with NO + NH 3 RHC (reduction half-cycle), proposed to occur via two distinct pathways involving adsorbed NH 3 and gas-phase NH 3 . The resulting NO + NH 3 RHC model is validated using spatiotemporal N 2 measurements covering a wide range of temperatures (200–450 °C) and space velocities (53,000–640,000 h –1 ). N 2 O formation is observed and modeled during NO + NH 3 RHC, with quantitative validation under standard selective catalytic reduction (SCR) conditions. Experimentally measured enthalpic and entropic changes associated with O 2 adsorption on NH 3 -solvated Cu I (ZCu(NH 3 ) 2 ) complexes [ Kamasamudram, K. Catal. Today 2010 , 151 (3–4), 212-222], along with activation energies estimated computationally for the intercage diffusion of ZCu(NH 3 ) 2 complexes [ Paolucci, C. Science 2017 , 357 (6 354), 898-903], are incorporated into a mean field kinetic model for the low-temperature oxidation half-cycle (OHC). Significant NH 3 release is observed during the isothermal oxidation of Cu I sites, attributed to desorption of NH 3 ligands from NH 3 -solvated Cu II dimers (Z 2 Cu 2 (NH 3 ) 4 O 2 ). Reduction of these dimeric complexes leads to the consumption of one NO/Cu II , contradicting the expected reduction stoichiometry. Inclusion of a global Arrhenius rate for the NO titration of Z 2 Cu 2 (NH 3 ) 4 O 2 complexes provides accurate representations of standard SCR on reduced and oxidized catalysts, predicting transient NO and NH 3 consumption between 150 and 250 °C as a function of hydrothermal aging. Deactivation of low-temperature standard SCR by NH 3 is observed at high NH 3 pressures, modeled via the formation of superoxo amino (ZCu(NH 3 ) 3 OO*) complexes during NH 3 titration of Z 2 Cu 2 (NH 3 ) 4 O 2 complexes [ Negri, C. J. Am. Chem. Soc. 2020 , 142 (37), 15884-15896]. The redox kinetic model presented here provides a foundational description of active site redox during low-temperature standard SCR, combining the recent kinetic, spectroscopic, and computational findings on the mechanism of standard SCR over Cu-SSZ-13.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

Abstract A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Sub-nanosecond signal propagation in anisotropy-engineered nanomagnetic logic chains

Energy efficient nanomagnetic logic (NML) computing architectures propagate binary information by relying on dipolar field coupling to reorient closely spaced nanoscale magnets. In the past, signal propagation in nanomagnet chains were characterized by static magnetic imaging experiments; however, the mechanisms that determine the final state and their reproducibility over millions of cycles in high-speed operation have yet to be experimentally investigated. Here we present a study of NML operation in a high-speed regime. We perform direct imaging of digital signal propagation in permalloy nanomagnet chains with varying degrees of shape-engineered biaxial anisotropy using full-field magnetic X-ray transmission microscopy and time-resolved photoemission electron microscopy after applying nanosecond magnetic field pulses. Moreover, an intrinsic switching time of 100 ps per magnet is observed. In conclusion these experiments, and accompanying macrospin and micromagnetic simulations, reveal the underlying physics of NML architectures repetitively operated on nanosecond timescales and identify relevant engineering parameters to optimize performance and reliability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Formation pathways of mesoporous silica nanoparticles with dodecagonal tiling

Considerable progress in the fabrication of quasicrystals demonstrates that they can be realized in a broad range of materials. However, the development of chemistries enabling direct experimental observation of early quasicrystal growth pathways remains challenging. Here, we report the synthesis of four surfactant-directed mesoporous silica nanoparticle structures, including dodecagonal quasicrystalline nanoparticles, as a function of micelle pore expander concentration or stirring rate. We demonstrate that the early formation stages of dodecagonal quasicrystalline mesoporous silica nanoparticles can be preserved, where precise control of mesoporous silica nanoparticle size down to <30 nm facilitates comparison between mesoporous silica nanoparticles and simulated single-particle growth trajectories beginning with a single tiling unit. Our results reveal details of the building block size distributions during early growth and how they promote quasicrystal formation. This work identifies simple synthetic parameters, such as stirring rate, that may be exploited to design other quasicrystal-forming self-assembly chemistries and processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Higher-order factorization machine for accurate surrogate modeling in material design

Efficient and robust optimization is important in material science for identifying optimal structural parameters and enhancing material performance. Surrogate-based active learning algorithms have recently gained great attention for their ability to efficiently navigate large, high-dimensional design spaces. Among surrogate models, 2 nd -order factorization machine (FM) models are widely employed as the surrogate model in active learning algorithms due to their balance between simplicity and effectiveness. However, their quadratic nature limits their capacity to capture complex, higher-order interactions among variables, often leading to suboptimal solutions. To overcome this limitation, we propose an active learning scheme integrating a 3 rd -order FM model, capable of modeling three-variable interactions and more intricate relationships in material systems. We comprehensively evaluate the surrogate modeling performance of the 3 rd -order FM case using various objective functions. Furthermore, we examine the optimization reliability and efficiency of the 3 rd -order FM-based active learning in a real-world material design task (e.g., nanophotonic structures for transparent radiative cooling). Our study shows that the 3 rd -order FM outperforms the 2 nd -order model in both surrogate accuracy and optimization performance, highlighting higher-order models’ promises for material design and optimization problems.

Factorization machine↗
DOE OSTI2024

Bayesian stability and force modeling for uncertain machining processes

Accurately simulating machining operations requires knowledge of the cutting force model and system frequency response. However, this data is collected using specialized instruments in an ex-situ manner. Bayesian statistical methods instead learn the system parameters using cutting test data, but to date, these approaches have only considered milling stability. This paper presents a physics-based Bayesian framework which incorporates both spindle power and milling stability. Initial probabilistic descriptions of the system parameters are propagated through a set of physics functions to form probabilistic predictions about the milling process. The system parameters are then updated using automatically selected cutting tests to reduce parameter uncertainty and identify more productive cutting conditions, where spindle power measurements are used to learn the cutting force model. The framework is demonstrated through both numerical and experimental case studies. Results show that the approach accurately identifies both the system natural frequency and cutting force model.

42 ENGINEERING↗
DOE OSTI2019

Complete catalog of ground-state diagrams for the general three-state lattice-gas model with nearest-neighbor interactions on a square lattice

Here, the ground states of the general three-state lattice-gas (equivalently, S = 1 Ising) model with nearest-neighbor interactions on a square lattice are explored in the full, five-dimensional parameter space of three interaction constants and two generalized chemical potentials or fields. The complete catalog of fifteen topologically different ground-state diagrams (zero-temperature phase diagrams), which show the regions of stability of the different ground states in the full parameter space of the model, is obtained and discussed in both lattice-gas and Ising-spin language. The results extend those of a recent study in a reduced parameter space, which identified six topologically different ground-state diagrams.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Hard superellipse phases: particle shape anisotropy & curvature

In this work, we report computer simulations of two-dimensional convex hard superellipse particle phases vs. particle shape parameters including aspect ratio, corner curvature, and sidewall curvature. Shapes investigated include disks, ellipses, squares, rectangles, and rhombuses, as well as, shapes with non-uniform curvature including rounded squares, rounded rectangles, and rounded rhombuses. Using measures of orientational order, order parameters, and a novel stretched bond orientational order parameter, we systematically identify particle shape properties that determine liquid crystal and crystalline phases including their coarse boundaries and symmetry. We observe phases including isotropic, nematic, tetratic, plastic crystals, square crystals, and hexagonal crystals (including stretched variants). Our results catalog known benchmark shapes, but include new shapes that also interpolate between known shapes. Our results indicate design rules for particle shapes that determine two-dimensional liquid, liquid crystalline, and crystalline microstructures that can be realized via particle assembly.

42 ENGINEERING↗