Multiferroic nanostructures have been attracting tremendous attention over the past decade, due to their rich cross-coupling effects and prospective electronic applications. In particular, the emergence of some exotic phenomena in size-confined multiferroic systems, including topological domain states such as vortices, center domains, and skyrmion bubble domains, has opened a new avenue to a number of intriguing physical properties and functionalities, and thus underpins a wide range of applications in future nanoelectronic devices. It is also highly appreciated that nano-domain engineering provides a pathway to control the magnetoelectric properties, which is promising for future energy-efficient spintronic devices. In recent years, this field, still in its infancy, has witnessed a rapid development and a number of challenges too. In this article, we shall review the recent advances in the emergent domain-related exotic phenomena in multiferroic nanostructures. Specific attention is paid to the topological domain structures and related novel physical behaviors as well as the electric-field-driven magnetic switching via domain engineering. This review will end with a discussion of future challenges and potential directions.
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http://dx.doi.org/10.1093/nsr/nwz100 | DOI Listing |
Nat Commun
January 2025
State Key Laboratory of Extreme Photonics and Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China.
Topological phases have prevailed across diverse disciplines, spanning electronics, photonics, and acoustics. Hitherto, the understanding of these phases has centred on energy (frequency) bandstructures, showcasing topological boundary states at spatial interfaces. Recent strides have uncovered a unique category of bandstructures characterised by gaps in momentum, referred to as momentum bandgaps or k gaps, notably driven by breakthroughs in photonic time crystals.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria; Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
Background And Objectives: Automated, anatomically coherent retinal layer segmentation in optical coherence tomography (OCT) is one of the most important components of retinal disease management. However, current methods rely on large amounts of labeled data, which can be difficult and expensive to obtain. In addition, these systems tend often propose anatomically impossible results, which undermines their clinical reliability.
View Article and Find Full Text PDFStructural variations (SVs) play important roles in genetic diversity, evolution, and carcinogenesis and are, as such, important for human health. However, it remains unclear how spatial proximity of double-strand breaks (DSBs) affects the formation of SVs. To investigate if spatial proximity between two DSBs affects DNA repair, we used data from 3C experiments (Hi-C, ChIA-PET, and ChIP-seq) to identify highly interacting loci on six different chromosomes.
View Article and Find Full Text PDFSpatial transcriptomics data analysis integrates gene expression profiles with their corresponding spatial locations to identify spatial domains, infer cell-type dynamics, and detect gene expression patterns within tissues. However, the current spatial transcriptomics analysis neglects the multiscale cell-cell interactions that are crucial in biology. To fill this gap, we propose multiscale cell-cell interactive spatial transcriptomics (MCIST) analysis.
View Article and Find Full Text PDFSci Adv
January 2025
Center for Physical Genomics and Engineering, Northwestern University, Evanston, IL 60208, USA.
In single cells, variably sized nanoscale chromatin structures are observed, but it is unknown whether these form a cohesive framework that regulates RNA transcription. Here, we demonstrate that the human genome is an emergent, self-assembling, reinforcement learning system. Conformationally defined heterogeneous, nanoscopic packing domains form by the interplay of transcription, nucleosome remodeling, and loop extrusion.
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