Edge states of two-dimensional transition-metal dichalcogenides (TMDCs) are crucial to quantum circuits and optoelectronics. However, their dynamics are pivotal but remain unclear due to the edge states being obscured by their bulk counterparts. Herein, we study the state-resolved transient absorption spectra of ball-milling-produced MoS nanosheets with 10 nm lateral size with highly exposed free edges. Electron energy loss spectroscopy and first-principles calculations confirm that the edge states are located in the range from 1.23 to 1.78 eV. Upon above bandgap excitations, excitons populate and diffuse toward the boundary, where the potential gradient blocks excitons and the edge states are formed through interband transitions within 400 fs. With below bandgap excitations, edge states are slowed down to 1.1 ps due to the weakened valence orbital coupling. These results shed light on the fundamental exciton dissociation processes on the boundary of functionalized TMDCs, enabling the ground work for applications in optoelectronics and light-harvesting.
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http://dx.doi.org/10.1021/acs.nanolett.1c04987 | DOI Listing |
Am J Sports Med
January 2025
Department of Orthopaedics, University of Utah, Salt Lake City, Utah, USA.
Background: Intraoperative hip capsule management is increasingly recognized as an important component of hip arthroscopy for the prevention of capsular-related instability. The periportal capsulotomy, relative to the interportal capsulotomy, has been proposed as a minimally invasive technique for decreasing postarthroscopy hip instability; however, the biomechanical effects of this technique are not well established.
Purpose/hypothesis: This study aimed to provide a biomechanical characterization of interportal and periportal capsulotomies, helping inform surgeon choice of capsulotomy type and repair, potentially guiding clinical practice in hip arthroscopy.
Sensors (Basel)
January 2025
Department of Mechanical Engineering, University of Siegen, Paul-Bonatz-Straße 9-11, 57076 Siegen, Germany.
This work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI) models. It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model. To reduce the number of trainable parameters, a physical signal processing approach is applied to the raw data before passing the data to the model.
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January 2025
United States Department of Agriculture-Agriculture Research Service, Grassland Soil and Water Research Laboratory, Temple, TX 76502, USA.
Efficient and reliable corn ( L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this study targets a production-scale area, better representing real-world agricultural conditions and offering more practical relevance for farmers.
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January 2025
Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, Greece.
The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution from early innovations to the latest state-of-the-art developments. It also analyzes the progression of architectural designs and learning paradigms that have significantly enhanced the ability to process and interpret complex visual data.
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January 2025
Department of Civil Engineering, Myongji College, Seoul 03656, Republic of Korea.
Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure.
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