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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Lattice chain theories for dynamics of acoustic flexural phonons in nonpolar nanomaterials

Knowledge of lattice dynamics of acoustic flexural phonons is critically important in understanding the mechanical, electrical, and thermal properties of nanomaterials. However, the general nature of acoustic flexural phonons for the span of possible nanomaterials and the dependence of these on microscopic material properties remain unclear. Therefore, we develop general lattice chain theories to understand acoustic flexural vibrations in covalent and van der Waals lattices. Furthermore, the analytic theories suggest that the flexure branch has quadratic dispersion for low dimensional lattices in the long-wavelength limit, quite different from linear branches in bulk van der Waals bonded lattices like graphite. Surprisingly, interlayer van der Waals interactions in multilayer lattices do not determine the behavior of the flexural dispersion. The bonds of covalent interactions in multilayer and buckled monolayer lattices play different roles in determining the flexural dispersion with those in flat monolayer lattices, e.g., graphene. Further, based on the correspondence between microscopic and continuum dynamics, we provide a universal and effective approach to characterize the intrinsic bending rigidity of low dimensional nanomaterials using dispersion data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Machine Learning Approaches for Nuclear Material Accounting Data from Irradiation and Reprocessing

We are currently exploring data analysis methods for their ability to strengthen the synthesis and evaluation of information generated within domestic and international safeguard regimes. Safeguard data typically includes rich heterogenous datasets amenable to advanced data analytics methods, such as machine learning. We have converted transactional data containing both numerical and categorical attributes from a domestic nuclear material control and accountability (NMC&A) system into a low-dimensional numerical structure via linear principal component analysis. This data representation allows for global structure discovery via cluster analysis, which can characterize the typical behavior of each of the primary types of transaction events. Furthermore, the structure of the top principal components captures the “typical behavior” of the data and is thus amenable to anomaly detection through statistical hypothesis testing. We explored this capability by generating erroneous permutations of the data and computing the Q-residual quantity increase associated with the information loss when these data are projected into the low-dimensional principal component analysis space representative of typical transactions.Future work will focus on identifying data transformations (e.g., graph networks) that more closely align with the inherent structure of these transactions to explain more salient information and disambiguate the underlying nuclear process—in this case, irradiation and reprocessing—from artifacts of NMC&A system transactional record keeping data.

Drescher, Adam↗
DOE OSTI2022

Entangled two-plasmon generation in carbon nanotubes and graphene-coated wires

Here, we investigate the two-plasmon spontaneous decay of a quantum emitter near single-walled carbon nanotubes (SWCNTs) and graphene-coated wires (GCWs). We demonstrate efficient, enhanced generation of two-plasmon entangled states in SWCNTs due to the strong coupling between tunable guided plasmons and the quantum emitter. We predict two-plasmon emission rates more than twelve orders of magnitude higher than in free space, with average lifetimes of a few dozen nanoseconds. Given their low dimensionality, these systems could be more efficient for generating and detecting entangled plasmons in comparison to extended graphene. Indeed, we achieve a tunable spectrum of emission in GCWs, where sharp resonances occur precisely at the plasmons' minimum excitation frequencies. We show that by changing the material properties of the GCW's dielectric core, one could tailor the dominant modes and frequencies of the emitted entangled plasmons while keeping the decay rate ten orders of magnitude higher than in free space. By unveiling the unique properties of two-plasmon spontaneous emission processes in the presence of low-dimensional carbon-based nanomaterials, our findings set the basis for a novel material platform with applications to on-chip quantum information technologies.

36 MATERIALS SCIENCE↗
NASA NTRS1999

Jet Mixing Enhancement by Feedback Control

The objective of this work has been to produce methodologies for high speed jet noise reduction based on natural mechanisms and enhanced feedback control to affect frequencies and structures in a prescribed manner. In this effort the two-point hot wire measurements obtained in the Langley jet facility by Ukeiley were used in conjuction with linear stochastic estimation (LSE) to implement the LSE component of the complementary technique. This method combines the Proper Orthogonal Decomposition (POD) and LSE to provide an experimental low dimensional time dependent description of the flow field. From such a description it should be possible to identify short time high strain rate events in the jet which contribute to the noise. The main task completed for this effort is summarized: LSE experiments were performed at the downstream locations where the two point hot wire measurements have been obtained by Ukeiley. These experiments involved sampling simultaneously hot wire signals from a relatively course spatial grid in gamma and theta. From this simultaneous data, coupled with the two-point measurements of Ukeiley via the LSE components of the complementary technique, an experimental low dimensional description of the jet at 4, 5, 6, 7 and 8 diameters downstream was obtained for Mach numbers of 0.3 and 0.6. We first present an overview of the theory involved. We finish up with a statement of the work performed and finally provide charts from a 1999 APS talk which summarizes the results.

Glauser, Mark↗
DOE OSTI2024

High-temperature quantum valley Hall effect with quantized resistance and a topological switch

Edge states of a topological insulator can be used to explore fundamental science emerging at the interface of low dimensionality and topology. Achieving a robust conductance quantization, however, has proven challenging for helical edge states. In this work, we show wide resistance plateaus in kink states—a manifestation of the quantum valley Hall effect in Bernal bilayer graphene—quantized to the predicted value at zero magnetic field. The plateau resistance has a very weak temperature dependence up to 50 kelvin and is flat within a dc bias window of tens of millivolts. We demonstrate the electrical operation of a topology-controlled switch with an on/off ratio of 200. These results demonstrate the robustness and tunability of the kink states and its promise in constructing electron quantum optics devices.

Science & Technology - Other Topics↗
DOE OSTI2022

Thixotropic spectra and Ashby-style charts for thixotropy

There is no universal model for thixotropy, and comparing thixotropic effects between different fluids is a subtle yet challenging problem. We introduce a generalized (model-insensitive) framework for comparing thixotropic properties based on thixotropic spectra. A superposition of exponential stress modes distributed over thixotropic time scales is used to quantify buildup and breakdown times and mode strengths in response to step-change input. This mathematical framework is tested with several experimental step-shear rate data on colloidal suspensions. Low-dimensional metrics based on moments of the distribution reveal characteristic average thixotropic properties, which are visualized on Ashby-style diagrams. This method outlines a framework for describing thixotropy across a diverse range of microstructures, supporting scientific studies as well as material selection for engineering design applications.

Mechanics↗
DOE OSTI2023

Nanomechanical vibrational response from electrical mixing measurements

Driven nanomechanical resonators based on low-dimensional materials are routinely and efficiently detected with electrical mixing measurements. However, the measured signal is a non-trivial combination of the mechanical eigenmode displacement and an electrical contribution, which makes the extraction of the driven mechanical response challenging. Here, we report a simple yet reliable method to extract solely the driven mechanical vibrations by eliminating the contribution of pure electrical origin. This enables us to measure the spectral mechanical response as well as the driven quadratures of motion. This method is crucial for nanomechanical vibrations in the nonlinear regime, since the shape of the mechanical response depends on the physics at work. We further show how to calibrate the measured signal into units of displacement. Our method marks a key step forward in the study of nanoelectromechanical resonators based on low-dimensional materials in the nonlinear regime.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Nonvolatile Electric Control of the Anomalous Hall Effect in an Ultrathin Magnetic Metal

Abstract Low‐dimensional magnetism has been boosted by the recent discovery of the ferromagnetism in layered two‐dimensional (2D) semiconductors. Although the macroscopic magnetic moments of 2D ferromagnets are weak, the anomalous Hall effect (AHE) can serve as a versatile electric probe to their magnetic properties. Here, the nonvolatile electric‐field manipulation of the AHE in an ultrathin metallic ferromagnet with perpendicular magnetic anisotropy at room temperature is reported, which is achieved by the electrostatic modulation of the longitudinal resistivity via a ferroelectric substrate without varying magnetization. Therefore, this work demonstrates an electric‐field‐controlled room‐temperature memory device based on the zero‐magnetic‐field anomalous Hall resistance of an ultrathin ferromagnet. More importantly, the experimental results disentangle magnetization and anomalous Hall resistance. As a result, the study reveals a linear decrease of anomalous Hall conductivity with normal conductivity, which is distinct from previous scaling relations. Accordingly, this work manifests a universe avenue to harnessing the AHE in low‐dimensional magnetic materials.

Feng, Zexin↗
NASA NTRS2006

Ultrasonication of Bismuth Telluride Nanocrystals Fabricated by Solvothermal Method

The objective of this study is to evaluate the effect of ultrasonication on bismuth telluride nanocrystals prepared by solvothermal method. In this study, a low dimensional nanocrystal of bismuth telluride (Bi2Te3) was synthesized by a solvothermal process in an autoclave at 180 C and 200 psi. During the solvothermal reaction, organic surfactants effectively prevented unwanted aggregation of nanocrystals in a selected solvent while controlling the shape of the nanocrystal. The atomic ratio of bismuth and tellurium was determined by energy dispersive spectroscopy (EDS). The cavitational energy created by the ultrasonic probe was varied by the ultrasonication process time, while power amplitude remained constant. The nanocrystal size and its size distribution were measured by field emission scanning electron microscopy (FESEM) and a dynamic light scattering system. When the ultrasonication time increased, the average size of bismuth telluride nanocrystal gradually increased due to the direct collision of nanocrystals. The polydispersity of the nanocrystals showed a minimum when the ultrasonication was applied for 5 min. Keywords: bismuth telluride, nanocrystal, low-dimensional, ultrasonication, solvothermal

Chu, Sang-Hyon↗
DOE OSTI2023

Spin disorder in a stacking polytype of a layered magnet

Strongly correlated ground states and exotic quasiparticle excitations in low-dimensional systems are central research topics in the solid-state research community. The present work develops a layered material and explores the physical properties. Single crystals of 3⁢R–Na 2 ⁢MnTe⁢O 6 were synthesized via a flux method. Single-crystal x-ray diffraction and transmission electron microscopy reveal a crystal structure with ABC-type stacking and an R–3 space group, which establishes this material as a stacking polytype to previously reported 2⁢H–Na 2 ⁢MnTe⁢O 6 . Magnetic- and heat-capacity measurements demonstrate dominant antiferromagnetic interactions, the absence of long-range magnetic order down to 0.5 K, and field-dependent short-range magnetic correlations. A structural transition at ~23 K observed in dielectric measurements may be related to displacements of the Na positions. In conclusion, our results demonstrate that 3⁢R–Na 2 ⁢MnTe⁢O 6 displays low-dimensional magnetism, disordered structure and spins, and the system displays a rich structure variety.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Tuning Phase Purity in Chiral 2D Perovskites

The introduction of chiral organic spacers in low-dimensional metal-halide perovskites triggers chiroptical activity, which is appealing for spintronic applications. However, a comprehensive understanding of structure formation and the ability to control phase purity in such materials have yet to be developed. Herein, the impact of processing conditions on the phase purity, microstructure, and chiroptical properties of chiral 2D perovskites is explored. The anisotropic emergence of a 1D perovskite inside the 2D matrix and its dependence on the organic cation chirality, solvent, and thermal annealing conditions are shown. By controlling these parameters, the in-plane conductivity of the films is nearly doubled. Furthermore, it is demonstrated, for the first time, that solvent choice and the spatial configuration of the organic cation can have an impact on the residual lattice strain and the energetic disorder of the system. The fundamentals presented here can help to improve the film deposition methods for low-dimensional chiral perovskites, offering strategies to control phase purity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Phenomenological Model of Nonlinear Dynamics and Deterministic Chaotic Gas Migration in Bentonite: Experimental Evidence and Diagnostic Parameters

Understanding gas migration in compacted clay materials, e.g., bentonite and claystone, is important for the design and performance assessment of an engineered barrier system of a radioactive waste repository system, as well as many practical applications. Existing field and laboratory data on gas migration processes in low-permeability clay materials demonstrate the complexity of flow and transport processes, including various types of instabilities, caused by nonlinear dynamics of coupled processes of liquid–gas exchange, dilation, fracturing, fracture healing, etc., which cannot be described by classical models of fluid dynamics in porous media. We here show that the complexity of gas migration processes can be explained using a phenomenological concept of nonlinear dynamics and deterministic chaos theory. To do so, we analyzed gas pressure and gas influx (i.e., input) and outflux (i.e., output), recorded during the gas injection experiment in the compact Mx80-D bentonite sample, and calculated a set of the diagnostic parameters of nonlinear dynamics and chaos, such a global embedding dimension, a correlation dimension, an information dimension, and a spectrum of Lyapunov exponents, as well as plotted 2D and 3D pseudo-phase-space strange attractors, based on the univariate influx and outflux time series data. These results indicate the presence of phenomena of low-dimensional deterministic chaotic behavior of gas migration in bentonite. In particular, during the onset of gas influx in the bentonite core, before the breakthrough, the development of gas flow pathways is characterized by the process of chaotic gas diffusion. After the breakthrough, with inlet-to-outlet movement of gas, the prevailing process is chaotic advection. During the final phase of the experiment, with no influx to the sample, the relaxation pattern of gas outflux is resumed back to a process of chaotic diffusion. The types of data analysis and a proposed phenomenological model can be used to establish the basic principles of experimental data-gathering, modeling predictions, and a research design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Surface reconstructions and electronic structure of metallic delafossite thin films

The growing interest in the growth and study of thin films of low-dimensional metallic delafossites, with the general formula ABO2, is driven by their potential to exhibit electronic and magnetic characteristics that are not accessible in bulk systems. The layered structure of these compounds introduces unique surface states as well as electronic and structural reconstructions, making the investigation of their surface behavior pivotal to understanding their intrinsic electronic structure. In this work, we study the surface phenomena of epitaxially grown PtCoO2, PdCoO2, and PdCrO2 films, utilizing a combination of molecular-beam epitaxy and angle-resolved photoemission spectroscopy. Through precise control of surface termination and treatment, we discover a pronounced 3×3 surface reconstruction in PtCoO2 films and PdCoO2 films, alongside a 2 × 2 surface reconstruction observed in PdCrO2 films. These reconstructions have not been reported in prior studies of delafossites. Furthermore, our computational investigations demonstrate the BO2 surface’s relative stability compared to the A-terminated surface and the significant reduction in surface energy facilitated by the reconstruction of the A-terminated surface. These experimental and theoretical insights illuminate the complex surface dynamics in metallic delafossites, paving the way for future explorations of their distinctive properties in low-dimensional studies.

Materials Science↗
DOE OSTI2019

Quantum simulations of excited states with active-space downfolded Hamiltonians

Many-body techniques based on the double unitary coupled cluster ansatz (DUCC) can be used to downfold electronic Hamiltonians into low-dimensional active spaces. It can be shown that the resulting dimensionality reduced Hamiltonians are amenable for quantum computing. Recent studies performed for several benchmark systems using quantum phase estimation (QPE) algorithms demonstrated that these formulations can recover a significant portion of ground-state dynamical correlation effects that stem from the electron excitations outside of the active space. These results have also been confirmed in studies of ground-state potential energy surfaces using quantum simulators. In this letter, we study the effectiveness of the DUCC formalism in describing excited states. We also emphasize the role of the QPE formalism and its stochastic nature in discovering/identifying excited states or excited-state processes in situations when the knowledge about the true configurational structure of a sought after excited state is limited or postulated (due to the specific physics driving excited-state processes of interest). In this context, we can view the QPE algorithm as an engine for verifying various hypotheses for excited-state processes and providing statistically meaningful results that correspond to the electronic state(s) with the largest overlap with a postulated configurational structure. We illustrate these ideas on examples of strongly cor- related molecular systems, characterized by small energy gaps and high density of quasi-degenerate states around the Fermi level.

quantum chemistry, quantum computing, computationa↗
DOE OSTI2022

Size Effect of Local Current-Voltage Characteristics of MX 2 Nanoflakes: Local Density of States Reconstruction from Scanning Tunneling Microscopy Experiments

Local current-voltage characteristics for low-dimensional transition-metal dichalcogenides (LDTMD), as well as the reconstruction of their local density of states (LDOS) from scanning tunneling microscopy (STM) experiments, are of fundamental interest and can be useful for advanced applications. Most of the existing models either have limited applicability for complex-shaped LDTMDs (e.g., those based on the Simmons approach) or require solving of an ill-defined integral equation to deconvolute the unknown LDOS (e.g., those based on the Tersoff approach). Using a serial expansion of the Tersoff formulas, we propose a flexible method to reconstruct the LDOS from local current-voltage characteristics measured in STM experiments. We establish a set of key physical parameters, which characterize the tunneling current of a STM-probe–sample contact and the sample LDOS expanded in Gaussian functions. Using a direct variational method coupled with probabilistic analysis, we determine these parameters from the STM experiments for MoS2 nanoflakes with different numbers of layers. The main result is the reconstruction of the LDOS in a relatively wide energy range around the Fermi level, which allows us to gain insight into the local band structure of LDTMDs. The reconstructed LDOS reveal pronounced size effects for the single-layer, two-layer, and three-layer MoS 2 nanoflakes, which we relate to the low dimensionality and strong bending or corrugation of the nanoflakes. We hope that the proposed elaboration of the Tersoff approach, allowing LDOS reconstruction, will be of critical interest for the quantitative description of STM experiments and also of use to better understand the microscopic physical aspects of the surface, strain, and bending contributions to the LDTMDs’ electronic properties.

42 ENGINEERING↗
DOE OSTI2022

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗
DOE OSTI2025

Nonintrusive projection-based reduced order modeling using stable learned differential operators

Nonintrusive projection-based reduced order models (ROMs) are essential for dynamics prediction in multi-query applications where underlying governing equations are known but the access to the source of the underlying full order model (FOM) is unavailable; that is, FOM is a glass-box. This article proposes a learn-then-project approach for nonintrusive model reduction. In the first step of this approach, high-dimensional stable sparse learned differential operators (S-LDOs) are determined using the generated data. In the second step, the ordinary differential equations, comprising these S-LDOs, are used with suitable dimensionality reduction and low-dimensional subspace projection methods to provide equations for the evolution of reduced states. This approach allows easy integration into the existing intrusive ROM framework to enable nonintrusive model reduction while allowing the use of Petrov–Galerkin projections. The applicability of the proposed approach is demonstrated for Galerkin and LSPG projection-based ROMs through four numerical experiments: 1-D scalar advection, 1-D Burgers, 2-D scalar advection and 1-D scalar advection–diffusion–reaction equations. In conclusion, the results indicate that the proposed nonintrusive ROM strategy provides accurate and stable dynamics prediction.

42 ENGINEERING↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗