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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

LYNM PE1 Pre-Experiment A Site Characterization Report

Underground chemical explosive experiments such as LYNM PE1 generate large multiphenomenological datasets, require complex site preparation and build out, and utilize cutting edge models and analysis techniques to analyze and simulate the explosion-induced signals. This wide range of outcomes makes it a necessity to thoroughly characterize the testbed in advance of experiments in a way that complements the wide suite of data being generated. Here, we present a broad overview of the site characterization work and data collection that was conducted before Experiment A, which is the first in a series of three PE1 experiments. This work includes, but is not limited to, geologic mapping, physical sample collection, analysis of material properties, geophysical borehole logging, and in-situ measurements. This information was collected by a large, dedicated team and was used to inform site construction, finalize instrumentation placement, generate Geologic Framework Models, feed pre-experiment predictions, and facilitate post-experiment data analysis

58 GEOSCIENCES↗
DOE OSTI2024

LYNM PE1 Pre-Experiment A Site Characterization Report

Underground chemical explosive experiments such as LYNM PE1 generate large multi-phenomenological datasets, require complex site preparation and build out, and utilize cutting edge models and analysis techniques to analyze and simulate the explosion-induced signals. This wide range of outcomes makes it a necessity to thoroughly characterize the testbed in advance of experiments in a way that complements the wide suite of data being generated. Here, we present a broad overview of the site characterization work and data collection that was conducted before Experiment A, which is the first in a series of three PE1 experiments. This work includes, but is not limited to, geologic mapping, physical sample collection, analysis of material properties, geophysical borehole logging, and in-situ measurements. This information was collected by a large, dedicated team and was used to inform site construction, finalize instrumentation placement, generate Geologic Framework Models, feed pre-experiment predictions, and facilitate post-experiment data analysis.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2013

Realistic multisite lattice-gas modeling and KMC simulation of catalytic surface reactions: Kinetics and multiscale spatial behavior for CO-oxidation on metal (100) surfaces

A realistic molecular-level description of catalytic reactions on single-crystal metal surfaces can be provided by stochastic multisite lattice-gas (msLG) models. This approach has general applicability, although in this report, we will focus on the example of CO-oxidation on the unreconstructed fcc metal (100) or M(100) surfaces of common catalyst metals M = Pd, Rh, Pt and Ir (i.e., avoiding regimes where Pt and Ir reconstruct). These models can capture the thermodynamics and kinetics of adsorbed layers for the individual reactants species, such as CO/M(100) and O/M(100), as well as the interaction and reaction between different reactant species in mixed adlayers, such as (CO + O)/M(100). The msLG models allow population of any of hollow, bridge, and top sites. This enables a more flexible and realistic description of adsorption and adlayer ordering, as well as of reaction configurations and configuration-dependent barriers. Adspecies adsorption and interaction energies, as well as barriers for various processes, constitute key model input. The choice of these energies is guided by experimental observations, as well as by extensive Density Functional Theory analysis. Model behavior is assessed via Kinetic Monte Carlo (KMC) simulation. We also address the simulation challenges and theoretical ramifications associated with very rapid diffusion and local equilibration of reactant adspecies such as CO. These msLG models are applied to describe adsorption, ordering, and temperature programmed desorption (TPD) for individual CO/M(100) and O/M(100) reactant adlayers. In addition, they are also applied to predict mixed (CO + O)/M(100) adlayer structure on the nanoscale, the complete bifurcation diagram for reactive steady-states under continuous flow conditions, temperature programmed reaction (TPR) spectra, and titration reactions for the CO-oxidation reaction. Extensive and reasonably successful comparison of model predictions is made with experimental data. Furthermore, we discuss the possible transition from traditional mean-field-type bistability and reaction kinetics for lower-pressure to multistability and enhanced fluctuation effects for moderate- or higher-pressure. Behavior in the latter regime reflects a stronger influence of adspecies interactions and also lower diffusivity in the higher-coverage mixed adlayer. We also analyze mesoscale spatiotemporal behavior including the propagation of reaction diffusion fronts between bistable reactive and inactive states, and associated nucleation-mediated transitions between these states. This behavior is controlled by complex surface mass transport processes, specifically chemical diffusion in mixed reactant adlayers for which we provide a precise theoretical formulation. The msLG models together with an appropriate treatment of chemical diffusivity enable equation-free heterogeneous coupled lattice-gas (HCLG) simulations of spatiotemporal behavior. In addition, msLG + HCLG modeling can describe coverage variations across polycrystalline catalysts surfaces, pressure variations across catalyst surfaces in microreactors, and could be incorporated into a multiphysics framework to describe mass and heat transfer limitations for high-pressure catalysis. (C) 2013 Elsevier Ltd. All rights reserved.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mesoscale Magnetostructural Phase Separation in Fe‐deficient Fe 5 GeTe 2

Two-dimensional van der Waals ferromagnet Fe 5-x GeTe 2 (F5GT) is promising for spintronic applications due to its high Curie temperature, layered structure, and ability to host complex magnetic textures. However, the origin of its sample-dependent magnetic anisotropy remains unclear, hindering control of its magnetic behavior. Here, we use spatially resolved cryogenic scanning transmission electron microscopy (STEM) to correlatively map magnetism, lattice structure, and chemistry across atomic-to-micron scales. We reveal that only mesoscale, not nanoscale, inclusions of a Fe-deficient secondary phase significantly modify magnetic behavior, establishing a previously unrecognized critical length scale. This phase separation, induced by quenching, leads to in-plane magnetic anisotropy, while slow cooling confines separation to a few nanometers and preserves out-of-plane anisotropy. These findings reconcile prior inconsistencies and establish a predictive framework for tuning magnetism in F5GT through thermal processing, with broader implications for controlling anisotropy in other two-dimensional magnetic materials.

2D ferromagnets↗
DOE OSTI2024

Computational modeling of coupled mechanical damage and electrochemistry in ternary oxide composite electrodes

Performance degradation of ternary layered oxide cathodes largely originates from their loss of structural integrity in cyclic usage. Mechanical damage, such as intergranular fracture of the active particles, is not only a mechanical cleavage process but also interferes with electrochemical kinetics such as infiltration of liquid electrolyte, surface corrosion of the constituent primary particles, and may eventually isolate the primary grains from the electron conducting network. Here, in this work, we develop a computational framework that integrates electrochemistry of a LiNi x Mn y Co 1−x−y O 2 (NMC) composite cathode with mechanical damage of the active particles. To fully examine the intricate chemomechanical behavior of the electrode, we evaluate the effects of the anisotropic material properties, the influence of mechanical potential on Li transport, and the concurrent intergranular fracture and electrolyte penetration along the grain boundaries upon multiple cycles. Electrolyte infiltration benefits capacity retention but aggravates further mechanical damage by corrosion. Structural failure mostly occurs in the first charging due to the anisotropic mechanical strain between the primary grains, while the resulting damage remains stable in the later few cycles. The results are consistent with experimental observations and the integration of electrochemistry and mechanical failure enables a step further understanding of the complex mechanism of battery degradation.

Battery degradation↗
NASA NTRSDate not supplied

Recent Developments to the Porous Microstructure Analysis (PuMA) Software

Introduction The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.

Tomography↗
NASA NTRSDate not supplied

Recent Developments to the Porous Microstructure Analysis (PuMA) Software

The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.

Tomography↗
DOE OSTI2015

Single-crystal-to-single-crystal metalation of a metal-organic framework: A route toward structurally well-defined catalysts

Metal–organic frameworks featuring ligands with open chelating groups are versatile platforms for the preparation of a diverse set of heterogeneous catalysts through postsynthetic metalation. The crystalline nature of these materials allows them to be characterized via X-ray diffraction, which provides valuable insight into the structure of the metal sites that facilitate catalysis. A highly porous and thermally robust zirconium-based metal–organic framework, Zr 6 O 4 (OH) 4 (bpydc) 6 (bpydc 2– = 2,2'-bipyridne-5,5'-dicarboxylate), bears open bipyridine sites that readily react with a variety of solution- and gas-phase metal sources to form the corresponding metalated frameworks. Remarkably, Zr 6 O 4 (OH) 4 (bpydc) 6 undergoes a single-crystal-to-single-crystal transformation upon metalation that involves a change in space group from $Fm\bar{3}m$ to $Pa\bar{3}m$. This structural transformation leads to an ordering of the metalated linkers within the framework, allowing structural characterization of the resulting metal complexes. Furthermore, Zr 6 O 4 (OH) 4 (bpydc) 6 yields an active heterogeneous catalyst for arene C–H borylation when metalated with [Ir(COD) 2 ]BF 4 (COD = 1,5-cyclooctadiene). These results highlight the unique potential of metal–organic frameworks as a class of heterogeneous catalysts that allow unparalleled structural characterization and control over their active sites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Constrained Surface Complexation Modeling: Rutile in RbCl, NaCl, and NaCF 3 SO 3 Media to 250 °C

In this paper, a comprehensive set of molecular-level results, primarily from classical molecular dynamics (CMD) simulations, are used to constrain CD-MUSIC surface complexation model (SCM) parameters describing rutile powder titrations conducted in RbCl, NaCl, and NaTr (Tr = triflate, CF 3 SO 3 – ) electrolyte media from 25 to 250 °C. Rb + primarily occupies the innermost tetradentate binding site on the rutile (110) surface at all temperatures (25, 150, 250 °C) and negative charge conditions (-0.1 and -0.2 C/m 2 ) probed via CMD simulations, reflecting the small hydration energy of this large, monovalent cation. Consequently, variable SCM parameters (Stern-layer capacitance values and intrinsic Rb + binding constants) were adjusted relatively easily to satisfactorily match the CMD and titration data. The larger hydration energy of Na + results in a more complex inner-sphere distribution, which shifts from bidentate to tetradentate binding with increasing negative charge and temperature, and this distribution was not matched well for both negative charge conditions, which may reflect limitations in the CMD and/or SCM approaches. Finally, in particular, the CMD axial density profiles for Rb + and Na + reveal that peak binding distances shift toward the surface with increasing negative charge, suggesting that the CD-MUSIC framework may be improved by incorporating CD or Stern-layer capacitance values that vary with charge.

36 MATERIALS SCIENCE↗
DOE OSTI2018

NMR Spectroscopy Reveals Adsorbate Binding Sites in the Metal–Organic Framework UiO-66(Zr)

In this paper, we assign 1 H and 13 C NMR resonances emanating from acetone, methanol, and cyclohexane adsorbed inside the pores of UiO-66(Zr). These results are informed by density functional theory (DFT) calculations, which probe the role of two competing effects inside of the pore environment: (i) nucleus independent chemical shifts (NICSs) generated by ring currents in conjugated linkers and (ii) small molecule coordination to the metal-oxyhydroxy cluster. These interactions are found to perturb the chemical shift of in-pore adsorbate relative to ex-pore adsorbate (which resides in spaces between the MOF particles). Changes in self-solvation upon adsorption may also perturb the chemical shift. Our results indicate that cyclohexane preferentially adsorbs in the tetrahedral pores of UiO-66(Zr), while acetone and methanol adsorb at the Zr–OH moieties on the metal-oxyhydroxy clusters in a more complex fashion. Finally, this method may be used to probe molecular adsorption sites and material void saturation with selected adsorbates, and with further development may eventually be used to trace in-pore chemistry of MOF materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Understanding polaronic transport in complex oxides by combining precise synthesis and first-principles many-body theory

In complex oxides, charge carriers often couple strongly with lattice vibrations to form polarons–entangled electron–phonon quasiparticles whose transport properties remain difficult to characterize. Experimental access to intrinsic polaronic transport requires ultraclean samples, while theoretical description demands methods beyond low-order perturbation theory. Here, we show a predictive theory–experiment workflow to study polaron transport in complex oxides. Focusing on a prototypical polaronic oxide, anatase TiO 2 , we combine growth of high-quality oxygen-vacancy-doped films using hybrid molecular beam epitaxy with a first-principles electron–phonon diagrammatic Monte-Carlo (FEP-DMC) framework recently developed for accurate polaron predictions. Our films exhibit record-high electron mobility for anatase TiO 2 , in excellent agreement with FEP-DMC calculations conducted prior to experiment, which predict a room-temperature mobility of 45 ± 15 cm −2 V −1 s −1 and a mobility-temperature scaling of μ ∝ T −1.9 ± 0.077 . Microscopic analysis using scanning transmission electron microscopy and x-ray photoelectron spectroscopy reveals the role of oxygen vacancies in modulating transport at lower temperatures. FEP-DMC further provides quantitative insight into polaron formation energy, phonon cloud distribution, lattice distortion around the polaron, and the polaronic contribution to mobility. Together, these results provide a deeper microscopic understanding of large-polaron transport in a complex oxide and provide the blueprint to characterize other polaronic materials.

anatase TiO2↗
DOE OSTI2024

3D high-fidelity automated neutronics guided optimization of fusion blanket designs

The compact Fusion Pilot Plant (FPP) is defined in the recent National Academies of Sciences, Engineering, and Medicine report as the next step of fusion energy demonstration with a $50$ MWe peak net electricity production, $Q_e$ greater than $1$, and at least $3$ hours of continuous operation. This fusion pilot plant will be a test bed enabling materials, designs, and fuel management assessment, and it will represent an engineering challenge because of its high-fusion power and compact design targets. Previous reactor data is limited to experiments operating in different design space ranges. Therefore, design iterations and assessments should rely on high-fidelity first-principle theoretical and computational models. The high-fidelity integrated modeling of the plasma is a fundamental part of fusion energy research. However, the whole device modeling is often neglected, utilizing low-fidelity, system-level analysis. Recently, the need for high-fidelity multi-physics modeling was recognized, resulting in a selection of integrated tools. Further, autonomous design optimization requires a streamlined framework that perturbs the design point, reruns the analysis, and examines the outputs. However, high-fidelity analysis requires complex geometry specification that is difficult to perturb. This work presents the parametric CAD generation tool TRACER and a new neutronic workflow. TRACER allows the perturbation of the geometry representation, creating geometry files ready for further analysis. The streamlined neutronic workflow allows efficient and accurate calculations. The two new tools coupled together were used to perform a 3D high-fidelity multi-objective, multi-input optimization of an "ARC Class" compact tokamak design. The workflow was driven by an optimization driver for full automation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Diffusion Model-Guided Inverse Design of Bimetallic Catalysts for Ammonia Decomposition

In the past decade, artificial intelligence and deep learning have played increasingly prominent roles in materials design and discovery. Among these, generative AI models, known for their ability to create unique and complex structures, have emerged as state-of-the-art tools for materials screening due to their high efficiency and low computational cost. In catalysis, one of the major challenges is identifying promising material candidates within an immense chemical space. This challenge can be addressed using generative approaches, such as diffusion-based inverse design models. In this study, we present a machine learning-guided workflow that employed a diffusion model for the inverse design of bimetallic alloy catalysts for low-carbon ammonia decomposition, a key reaction for ammonia emission control and sustainable hydrogen production. Catalyst candidates were evaluated using nitrogen adsorption energy as the key descriptor, inspired by multiscale modeling. The proposed workflow identified low-cost, environmentally friendly catalysts with excellent catalytic performance, which have been validated theoretically and experimentally. Our framework decoupled the generative and property-prediction components, enhancing both flexibility and accuracy in the catalytic material design process.

Adsorption↗
DOE OSTI2024

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex process.Our approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant" relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant" representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public1.

Tabassum, Anika [ORNL] (ORCID:0000000254600955)↗
DOE OSTI2015

COUPLING OF RELAP5-SCDAP MOD4.0 AND NEUTRONIC CODES

High-fidelity and accurate nuclear system codes play a key role in the design and analysis of complex nuclear power plants, which consist of multiple subsystems, such as the reactor core (and its fuel, burnable poisons, control elements, etc.), the reactor internal structures, the vessel, and the energy conversion subsystem and beyond to grid demand. Most commonly the interplay between these various subsystems is modeled using coupled codes, each of which represents one of the subsystems. And the most common direct coupling is that of thermal-hydraulics and neutronics codes. The subject of this paper is the coupling of codes that model not only thermal-hydraulics and neutronics, but also structural components damage. Furthermore, the neutronic component is not limited to the sole core solver. The coupled code system encompasses thermal-hydraulics, material performance of the fuel, neutronic solver, and neutronic data preparation. Thus, this paper presents a framework for coupling RELAP5/SCDAPSIM/MOD4.0 with a suite of neutron kinetics codes that includes NESTLE, DRAGON and a version of the ENDF library. The version of the RELAP5/SCDAPSIM/MOD4.0 code used in this work is one developed by Innovate System Software (ISS) as part of the international SCDAP Development and Training Program (SDTP) for best-estimate analysis to model reactor transients including severe accident phenomena. This RELAP5/SCDAPSIM/MOD4.0 code version is also capable of predicting nuclear fuel performance. It uses nodal power distributions to calculate mechanical and thermal parameters such as heat-up, oxidation and meltdown of fuel rods and control rods, the ballooning and rupture of fuel rod cladding, the release of fission products from fuel rods, and the disintegration of fuel rods into porous debris and molten material. On the neutronics side, this work uses the NESTLE and DRAGON codes. NESTLE is a multi-dimensional static and kinetic neutronic code developed at North Carolina State University. It solves up to four energy groups neutron diffusion equations utilizing the Nodal Expansion Method (NEM) in Cartesian or hexagonal geometry. The DRAGON code, developed at Ecole Polytechnique de Montreal, performs lattice physics calculations based on the neutron transport equation and is capable of using very fine energy group structures. In this work, we have developed a coupling approach to exchange data among the various modules. In the coupling process, the generated nuclear data (in fine multigroup energy structure) are collapsed down into two- or four-group energy structures for use in NESTLE. The neutron kinetics and thermal-hydraulics modules are coupled at each time step by using the cross-section data. The power distribution results of the neutronic calculations are transmitted to the thermal-hydraulics code. The spatial distribution of coolant density and the fuel-moderator temperature, which result from the thermal-hydraulic calculations, are transmitted back to the neutron kinetics codes and then the loop is closed using new neutronics results. Details of the actual data transfers will be described in the full length paper.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1990

Spin bipolaron in the framework of emery model for high-T(sub c) copper oxide superconductors

The high-T(sub c) oxide compounds discovered recently exhibit a number of interesting physical properties. Two-dimensional antiferromagnetic spin order has been observed in these materials at the oxygen deficiency. This fact can be explained by strong correlation of the spins, situated on Cu sites in the conducting planes of the oxide superconductors. The doping or the oxygen deficiency lead to the occurrence of holes, occupying the oxygen p-orbitals according to the Emery model. At the small hole concentration they can move along the antiferromagnetic lattice of spins, localized on Cu sites. Researchers consider the two holes situation and describe in what way their behavior depends on the antiferromagnetic exchange interation J. It is known that in the framework of Hubbard model with strong on-site Coulomb repulsion, a single hole can form a spin polaron of the large radius. It is reasonable to admit that two holes with parallel spins (triplet) form the spin bipolaron complex owing to the hole excitations' capability to polarize Cu spin surroundings. Such an excitation was considered in the phenomenological way. Here the problem is discussed on the basis of the microscopic approach in the framework of the variational principle. A special kind of wave function is used for such a purpose. The wave function is constructed by generalizing the trial functions proposed in over two holes excitation situation (triplet) and then the region of spin bipolaron existance in the framework of Emery model is studied. In this model the Hamiltonian can be easily rewritten by forming the oxygen states transforming as the irreducible representations of the group D(sub 4).

Golub, A. A.↗
DOE OSTI2023

Vibration Analysis of a Unimorph Nanobeam with a Dielectric Layer of Both Flexoelectricity and Piezoelectricity

In this study, for the first time, free and forced vibrational responses of a unimorph nanobeam consisting of a functionally graded base, along with a dielectric layer of both piezoelectricity and flexoelectricity, is investigated based on paradox-free local/nonlocal elasticity. The formulation and boundary conditions are attained by utilizing the energy method Hamilton’s principle. In order to set a comparison, the formulation of a model in the framework of differential nonlocal is first presented. An effective implementation of the generalized differential quadrature method (GDQM) is then utilized to solve higher-order partial differential equations. This method can be utilized to solve the complex equations whose analytic results are quite difficult to obtain. Lastly, the impact of various parameters is studied to characterize the vibrational behavior of the system. Additionally, the major impact of flexoelectricity compared to piezoelectricity on a small scale is exhibited. The results show that small-scale flexoelectricity, rather than piezoelectricity, is dominant in electromechanical coupling. One of the results that can be mentioned is that the beams with higher nonlocality have the higher voltage and displacement under the same excitation amplitude. The findings can be helpful for further theoretical as well as experimental studies in which dielectric material is used in smart structures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

3D Hybrid Plasmonic Framework with Au Nanopillars Embedded in Nitride Multilayers Integrated on Si

Integration of nanoscale photonic and plasmonic components on Si substrates is a critical step toward Si-based integrated nanophotonic devices. Herein, a set of unique complex 3D metamaterials with intercalated nanolayered and nanopillar structures with tunable plasmonic and optical properties on Si substrates is designed. More specifically, the 3D metamaterials combine metal (Au) nanopillars and alternating metal-nitride (Au-TiN and Au-TaN) nanolayers, epitaxially grown on Si substrates. The ultrafine Au nanopillars ( d ≈ 3 nm) continuously grow throughout all the nanolayers with high epitaxial quality. Novel optical properties, such as highly anisotropic optical property, high absorbance covering the entire visible spectrum regime, and hyperbolic property in the visible regime, are demonstrated. Furthermore, a waveguide based on a silicon nitride (Si 3 N 4 ) ridge with a multilayer structure is successfully fabricated. The demonstration of 3D nanoscale metamaterial design integrated on Si opens up a new route toward tunable metamaterials nanostructure designs with versatile material selection for various optical components in Si integrated photonics.

36 MATERIALS SCIENCE↗