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Au nanoclusters (NCs), with discrete electronic structures and tunable properties, are promising building blocks for nanoelectronics. However, maximizing their potential requires a deeper understanding of precise control over intercluster interactions, which is crucial for charge transport. Here, we leverage coordination chemistry to fine-tune intercluster distances in NC-based frameworks by incorporating coordinating metal ions (Mg, Co, Ni, or Cu) into [Au(-HMBA)] NCs. Single-crystal X-ray diffraction reveals that four isostructural frameworks have systematic lattice parameter variations, driving a 31-fold enhancement in the electrical conductivity. Density functional theory calculations further reveal semiconductor-like electronic structures and highlight that Cu-coordinated frameworks exhibit more efficient charge transport due to the presence of Cu 3d states near the Fermi level. This atomic-level investigation of intercluster distance effects and electrical property tuning through coordinating ion incorporation establishes design principles for engineering electronic properties in NC frameworks, enabling the development of precisely tunable and high-performance nanoelectronic materials.
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http://dx.doi.org/10.1021/jacs.5c06695 | DOI Listing |
Neural Netw
September 2025
School of Mathematics and Information Science, Guangxi University, Nanning, 530004, China. Electronic address:
This study presents a novel variable gain intermittent boundary control (VGIBC) approach for stabilizing delayed stochastic reaction-diffusion Cohen-Grossberg neural networks (SRDCGNN). In contrast to traditional constant gain intermittent boundary control (CGIBC) methods, the proposed VGIBC framework dynamically adjusts the control gain based on the operational duration within each control cycle, thereby improving adaptability to variations in work interval lengths. The time-varying control gain is designed using a piecewise interpolation method across work intervals, defined by a finite set of static gain matrices.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Electronic Science and Engineering, Nanjing University, China. Electronic address:
The Segment Anything Model (SAM) is a cornerstone of image segmentation, demonstrating exceptional performance across various applications, particularly in autonomous driving and medical imaging, where precise segmentation is crucial. However, SAM is vulnerable to adversarial attacks that can significantly impair its functionality through minor input perturbations. Traditional techniques, such as FGSM and PGD, are often ineffective in segmentation tasks due to their reliance on global perturbations that overlook spatial nuances.
View Article and Find Full Text PDFNeural Netw
September 2025
organization=Chongqing Key Laboratory of Computer Network and Communication Technology, School of Computer Science and Technology (National Exemplary Software School), Chongqing University of Posts and Telecommunications, city=Chongqing, postcode=400065, country=China. Electronic address: tianh519@1
Image deblurring and compression-artifact removal are both ill-posed inverse problems in low-level vision tasks. So far, although numerous image deblurring and compression-artifact removal methods have been proposed respectively, the research for explicit handling blur and compression-artifact coexisting degradation image (BCDI) is rare. In the BCDI, image contents will be damaged more seriously, especially for edges and texture details.
View Article and Find Full Text PDFNeural 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 PDFJ Biomech
August 2025
Lampe Joint Department of Biomedical Engineering, UNC Chapel Hill & NC State University, Chapel Hill, NC, USA. Electronic address:
Walking is essential for maintaining independence and quality of life, yet aging may impair the neuromuscular function required for stable gait over time. This study sought to quantify age-related differences in step-to-step control during prolonged walking using detrended fluctuation analysis (DFA). We hypothesized that step-to-step changes in step length and step width would exhibit reduced temporal persistence over time, with more pronounced effects in older than in younger adults.
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