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Photocatalytic water splitting for hydrogen production is an attractive renewable energy technology, but the oxygen evolution reaction (OER) at the anode is severely constrained by a high overpotential. The two-dimensional vdW ferromagnetic material FeGeTe, with its good stability and excellent metallic conductivity, has potential as an electrocatalyst, but its sluggish surface catalytic reactivity limits its large-scale application. In this work, we adapted DFT calculations to introduce surface Te vacancies to boost OER performance of the FeGeTe (001) surface. Te vacancies induce the charge redistribution of active sites, optimizing the adsorption and desorption of oxygen-containing intermediates. Consequently, the overpotential of the rate-determining step in the OER process of FeGeTe is reduced to 0.34 V, bringing the performance close to that of the benchmark IrO catalyst (0.56 V). Notably, the vacancies' concentration and configuration significantly modify the electronic structure and thus influence OER activity. This study provides important theoretical evidence for defect engineering in OER catalysis and offers new design strategies for developing efficient and stable electrocatalysts for sustainable energy conversion.
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http://dx.doi.org/10.3390/nano15161272 | DOI Listing |
Neural Netw
September 2025
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
3D shape defect detection plays an important role in autonomous industrial inspection. However, accurate detection of anomalies remains challenging due to the complexity of multimodal sensor data, especially when both color and structural information are required. In this work, we propose a lightweight inter-modality feature prediction framework that effectively utilizes multimodal fused features from the inputs of RGB, depth and point clouds for efficient 3D shape defect detection.
View Article and Find Full Text PDFRetin Cases Brief Rep
September 2025
Doheny Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, California, USA.
Purpose: To report the examination and multimodal imaging findings of a patient with unilateral bull's eye maculopathy.
Methods: A retrospective chart review of a 77-year-old patient with unilateral bull's eye maculopathy who presented to a tertiary retinal practice was performed. The patient's history, visual acuity, examination and multimodal imaging findings over five years of follow-up were described.
J Phys Chem Lett
September 2025
School of Chemical Sciences, National Institute of Science Education and Research (NISER), An OCC of Homi Bhabha National Institute Jatni, Khurda, Bhubaneswar 752050, Odisha, India.
Quantum-confined perovskites represent an emerging class of materials with great potential for optoelectronic applications. Specifically, zero-dimensional (0D) perovskites have garnered significant attention for their unique excitonic properties. However, achieving phase-pure, size-tunable 0D perovskite materials and gaining a clear understanding of their photophysical behavior remains challenging.
View Article and Find Full Text PDFMed Sci (Paris)
September 2025
CIRI, Centre international de recherche en infectiologie Université de Lyon, Inserm U1111, Université Claude Bernard Lyon 1, CNRS UMR5308, ENS de Lyon, Lyon, France.
The accumulated knowledge on the biology of the HIV-1 virus has led to the emergence of technologies that exploit the architecture of retroviruses and their integration or vectorization properties. This field of study constitutes retroviral vectorology, democratized in laboratories by the use of lentiviral vectors. By hijacking retroviral assembly, other systems are emerging and are increasingly mentioned in recent literature.
View Article and Find Full Text PDFPLoS One
September 2025
Symbiosis Institute of Technology, Symbiosis International University, Pune, India.
With the rapid development of industrial automation and intelligent manufacturing, defect detection of electronic products has become crucial in the production process. Traditional defect detection methods often face the problems of insufficient accuracy and inefficiency when dealing with complex backgrounds, tiny defects, and multiple defect types. To overcome these problems, this paper proposes Y-MaskNet, a multi-task joint learning framework based on YOLOv5 and Mask R-CNN, which aims to improve the accuracy and efficiency of defect detection and segmentation in electronic products.
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