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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.
In the version of this Article previously published, co-author Ryan L. Lee was missing the middle initial. This has now been corrected in the online versions.
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.
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.
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.
The anti-soiling (AS) performance of highly reflective, superhydrophilic (SPH, 0° water contact angle) coated mirrors was characterized and compared with that of superhydrophobic (SP, >165° water contact angle) coated mirrors.
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.
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
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.
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This review summarizes recent works, all using a specific atomistic approach, that predict and explain the occurrence of key features for neuromorphic computing in three archetypical dipolar materials, when they are subject to THz excitations. The main ideas behind such atomistic approach are provided, and illustration of model relaxor ferroelectrics, antiferroelectrics, and normal ferroelectrics are given, highlighting the important potential of polar materials as candidates for neuromorphic computing. Some peculiar emphases are made in this Review, such as the connection between neuromorphic features and percolation theory, local minima in energy path, topological transitions and/or anharmonic oscillator model, depending on the material under investigation. By considering three different and main polar material families, this work provides a complete and innovative toolbox for designing polar-based neuromorphic systems.
An ideal layered $\hat{x}$-polarized antiferromagnet (AFM) between two antialigned $±\hat{z}$ polarized ferromagnetic (FM) contacts transmits no current due to a π phase difference of the matrix elements coupling the spin degenerate states to the two FM contacts. Inserting a normal-metal layer or tunnel barrier layer between one FM contact and the AFM alters this phase difference, and due to the unequal weighting of the two spins at the interface, it also breaks the spin degeneracy of the two AFM states. Further, the broken symmetry of the matrix elements combined with the broken degeneracy of the AFM states results in a Fano resonance in the transmission and a turn-on of the $T_{↑,↓}$ transmission channel. Such a magnetic tunnel junction geometry with two antialigned $±\hat{z}$ FM contacts can electrically detect an AFM skyrmion. The AFM skyrmion serves as an analog of the oblique polarizer in the triple-polarizer experiment. Resistances and resistance ratios are calculated and compared for FM and AFM skyrmions in a magnetic tunnel junction.
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