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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Ultrastrong magnon-magnon coupling and chiral spin-texture control in a dipolar 3D multilayered artificial spin-vortex ice

Strongly-interacting nanomagnetic arrays are ideal systems for exploring reconfigurable magnonics. They provide huge microstate spaces and integrated solutions for storage and neuromorphic computing alongside GHz functionality. These systems may be broadly assessed by their range of reliably accessible states and the strength of magnon coupling phenomena and nonlinearities. Increasingly, nanomagnetic systems are expanding into three-dimensional architectures. This has enhanced the range of available magnetic microstates and functional behaviours, but engineering control over 3D states and dynamics remains challenging. Here, we introduce a 3D magnonic metamaterial composed from multilayered artificial spin ice nanoarrays. Comprising two magnetic layers separated by a non-magnetic spacer, each nanoisland may assume four macrospin or vortex states per magnetic layer. This creates a system with a rich 16 N microstate space and intense static and dynamic dipolar magnetic coupling. The system exhibits a broad range of emergent phenomena driven by the strong inter-layer dipolar interaction, including ultrastrong magnon-magnon coupling with normalised coupling rates of $\frac{Δf}{v}$ = 0.57, GHz mode shifts in zero applied field and chirality-control of magnetic vortex microstates with corresponding magnonic spectra.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Cubic double perovskites host noncoplanar spin textures

Magnetic materials with noncoplanar magnetic structures can show unusual physical properties driven by nontrivial topology. Topologically-active states are often multi-q structures, which are challenging to stabilize in models and to identify in materials. Here, we use inelastic neutron-scattering experiments to show that the insulating double perovskites Ba 2 YRuO 6 and Ba 2 LuRuO 6 host a noncoplanar 3-q structure on the face-centered cubic lattice. Quantitative analysis of our neutron-scattering data reveals that these 3-q states are stabilized by biquadratic interactions. Our study identifies double perovskites as a highly promising class of materials to realize topological magnetism, elucidates the stabilization mechanism of the 3-q state in these materials, and establishes neutron spectroscopy on powder samples as a valuable technique to distinguish multi-q from single-q states, facilitating the discovery of topologically-nontrivial magnetic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A closer look at spin textures

Here, Lorentz electron ptychography, a coherent diffractive imaging method, unveils magnetization singularities in a skyrmion lattice in FeGe and captures subtle internal structures near the skyrmion cores, boundaries, and dislocations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Picosecond volume expansion drives a later-time insulator–metal transition in a nano-textured Mott insulator

There is significant technological interest in developing ever faster switching between different electronic and magnetic states of matter. Manipulating properties at terahertz rates requires accessing the intrinsic timescales of both electrons and associated phonons, which is possible with short-pulse photoexcitation. However, in many Mott insulators, the electronic transition is accompanied by the nucleation and growth of percolating domains of the changed lattice structure, leading to empirical timescales dominated by slowly coarsening dynamics. Here, in this study, we use time-resolved X-ray diffraction and reflectivity measurements to show that the photoinduced insulator-to-metal transition in an epitaxially strained Mott insulating thin film occurs without observable domain formation and coarsening effects, allowing the study of the intrinsic electronic and lattice dynamics. Above a fluence threshold, the initial electronic excitation drives a fast lattice rearrangement, which is followed by a slower electronic evolution into a metastable nonequilibrium state. Microscopic model calculations based on time-dependent dynamical mean-field theory and semiclassical lattice dynamics explain the threshold behaviour and elucidate the delayed onset of the electronic phase transition. This work highlights the importance of combined electronic and structural studies in unravelling the physics of dynamic transitions and the timescales of photoinduced processes. During a photoinduced phase transition, electronic rearrangements are usually faster than lattice ones. Time-resolved measurements now show that the insulator-to-metal transition in a thin-film Mott insulator is preceded by lattice reconfiguration.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Extrapolating dynamic leidenfrost principles to metallic nanodroplets on asymmetrically textured surfaces

In an effort to enhance our knowledge on how to control the movement of metallic nanodroplets, here we have used classical molecular dynamics simulations to investigate whether Cu nanostructures deposited on nanopillared substrates can be made to jump at desired angles. We find that such control is possible, especially for Cu nanostructures that are symmetric; for asymmetric nanostructures, however, control is more uncertain. The work presented here borrows ideas from two seemingly different fields, metallic droplets and water droplets in the dynamic Leidenfrost regime. Despite the differences in the respective systems, we find common ground in their behavior on nanostructured surfaces. As a result, we suggest that the ongoing research in Leidenfrost droplets is a fertile area for scientists working on metallic nanodroplets.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

Impact of chabazite SSZ-13 textural properties and chemical composition on CO 2 adsorption applications

Chabazite SSZ-13 samples with varying silica content (Si/Al from 6 to 35) were synthesized in both stirring and static conditions to obtain material with changing particle size and morphology and thoroughly analysed with various characterization techniques. The role of particle size and chemical compositions of SSZ-13 chabazite on CO2 and N2 adsorption measurements was investigated. The Si/Al ratio played a major role for CO2 adsorption with Al-rich SSZ-13 showing a higher CO2 uptake than Al-poor material. This was attributed to the high density of active charged species in the chabazite cage. Particle size also played an important role in the sorption capacities with smaller particles, obtained in stirring conditions, showing enhanced CO2 uptakes compared to larger particles of same chemical composition. This was associated with an increased density of surface active sites and shorter diffusion pathways.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2014

Superconductivity in textured Bi clusters/Bi{sub 2}Te{sub 3} films

We report superconductivity at an onset critical temperature below 3.1 K in topological insulator ∼200-nm-thick Bi{sub 2}Te{sub 3} thin films grown by pulsed laser deposition. Using energy-dispersive X-ray spectroscopy elemental mapping and Auger electron spectroscopy elemental depth profiling, we clearly identified bismuth (Bi) precipitation and Bi cluster signatures. Superconductivity in the Bi{sub 2}Te{sub 3} films was attributed to the proximity effect of Bi clusters precipitated on the surface of the Bi{sub 2}Te{sub 3} films.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine learning-based multidomain processing for texture-based image segmentation and analysis

Atomic and molecular resolved atomic force microscopy (AFM) images offer unique insights into materials' properties such as local ordering, molecular orientation, and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis, thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. The usage of supervised learning and feature extraction allows us to retain the consideration of specific problem-dependent features (such as types of periodical structures observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for the segmentation of molecular resolved AFM images based on the crystal orientation of the observed domains, automated selection of boundaries, and collection of relevant statistics. Finally, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique

36 MATERIALS SCIENCE↗
DOE OSTI2025

Achieving designed texture and flows in bulk active nematics using optimal control theory

Being intrinsically nonequilibrium, active materials can potentially perform functions that would be thermodynamically forbidden in passive materials. However, active systems have diverse local attractors that correspond to distinct dynamical states, many of which exhibit chaotic turbulent-like dynamics and thus cannot perform work or useful functions. Designing such a system to choose a specific dynamical state is a formidable challenge. Motivated by recent advances enabling optogenetic control of experimental active materials, we describe an optimal control theory framework that identifies a spatiotemporal sequence of light-generated activity that drives an active nematic system toward a prescribed dynamical steady state. Active nematics are unstable to spontaneous defect proliferation and chaotic streaming dynamics in the absence of control. We demonstrate that optimal control theory can compute activity fields that redirect the dynamics into a variety of alternative dynamical programs and functions. This includes dynamically reconfiguring between states, selecting and stabilizing emergent behaviors that do not correspond to attractors, and are hence unstable in the uncontrolled system. Furthermore, our results provide a roadmap to leverage optical control methods to rationally design structure, dynamics, and function in a wide variety of active materials.

Complex systems theory↗