We report the creation and manipulation of structural phase boundaries in the single-layer quantum spin Hall insulator 1T'WSe by means of scanning tunneling microscope tip pulses. We observe the formation of one-dimensional interfaces between topologically nontrivial 1T' domains having different rotational orientations, as well as induced interfaces between topologically nontrivial 1T' and topologically trivial 1H phases. Scanning tunneling spectroscopy measurements show that 1T'/1T' interface states are localized at domain boundaries, consistent with theoretically predicted unprotected interface modes that form dispersive bands in and around the energy gap of this quantum spin Hall insulator. We observe a qualitative difference in the experimental spectral line shape between topologically "unprotected" states at 1T'/1T' domain boundaries and protected states at 1T'/1H and 1T'/vacuum boundaries in single-layer WSe.
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http://dx.doi.org/10.1021/acs.nanolett.9b02157 | DOI Listing |
Adv Mater
January 2025
State Key Laboratory for Manufacturing Systems Engineering, Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education, School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Large low-field magnetoresistance (LFMR, < 1 T), related to the spin-disorder scattering or spin-polarized tunneling at boundaries of polycrystalline manganates, holds considerable promise for the development of low-power and ultrafast magnetic devices. However, achieving significant LFMR typically necessitates extremely low temperatures due to diminishing spin polarization as temperature rises. To address this challenge, one strategy involves incorporating Ruddlesden-Popper structures (ABO):AO, which are layered derivatives of perovskite structure capable of potentially inducing heightened magnetic fluctuations at higher temperatures.
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 PDFComput Biol Med
January 2025
UCL Mechanical Engineering, University College London, UK; Ri.MED Foundation, Palermo, Italy; University of Palermo, Department of Engineering, Palermo, Italy. Electronic address:
Aortic valve replacements, both surgical and transcatheter, are nowadays widely employed treatments. Although clinically effective, these procedures are correlated with potentially severe clinical complications which can be associated with the non-physiological haemodynamics that they establish. In this work, the fluid dynamics changes produced by surgical and transcatheter aortic valve replacements are analysed and compared with an ideal healthy native valve configuration, employing advanced fluid-structure interaction (FSI) simulations.
View Article and Find Full Text PDFHortic Res
January 2025
Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China, 100193.
Appropriate root system architecture (RSA) can improve alfalfa yield, yet its genetic basis remains largely unexplored. This study evaluated six RSA traits in 171 alfalfa genotypes grown under controlled greenhouse conditions. We also analyzed five yield-related traits in normal and drought stress environments and found a significant correlation (0.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
ETH Zurich, Zurich, Switzerland.
Background: The escalating global scarcity of skilled health care professionals is a critical concern, further exacerbated by rising stress levels and clinician burnout rates. Artificial intelligence (AI) has surfaced as a potential resource to alleviate these challenges. Nevertheless, it is not taken for granted that AI will inevitably augment human performance, as ill-designed systems may inadvertently impose new burdens on health care workers, and implementation may be challenging.
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