Carbon materials exhibit diverse mechanical properties, from hard diamond to soft graphite. However, carbon materials with high ductility are rare, because of strong covalent bonds between carbon atoms. Here, we propose that the structures of triangular lattice have higher ductility than those of hexagonal or quadrangle lattice. A two-dimensional (2D) carbon network, named a carbon Kagome lattice (CKL), is used as an example to verify the point. The carbon structure has a Kagome lattice similar to the triangular lattice. Because empirical potentials cannot well simulate mechanical properties of carbon structures with triangular carbon rings, we work out a neuroevolution potential (NEP) based on a machine learning method. Structural evolution and phase transition under strain have been studied based on the NEP. The results indicate that the ductility of 2D CKL can approach 80%, and even at a high temperature, the ductility can reach 48%. The ductile values are the highest in all 2D crystal materials except the molecular materials. The high ductility is attributed to the phase transition of 2D CKL under tensile strain. It transits to another carbon allotrope, named Carbon Ene-Yne graphyne, which can also sustain a large tensile strain. Our work not only proposes that the materials with triangular lattice have high ductile ability but also finds a 2D carbon material with the highest ductility, extending mechanical applications of materials.

Current CNN-based infrared small target detection (IRSTD) methods generally overlook the heterogeneity between shallow and deep features, leading to inefficient collaboration between shallow fine-grained structural information and deep high-level semantic representations. Additionally, the dependency relationships and fusion mechanisms across different feature hierarchies lack systematic modeling, which fails to fully exploit the complementarity of multilevel features. These limitations hinder IRSTD performance while incurring substantial computational costs. To address these challenges, this paper proposes a shallow-deep synergistic detection network (SDS-Net) that efficiently models multilevel feature representations to increase both the detection accuracy and computational efficiency in IRSTD tasks. SDS-Net introduces a dual-branch architecture that separately models the structural characteristics and semantic properties of features, effectively preserving shallow spatial details while capturing deep semantic representations, thereby achieving high-precision detection with significantly improved inference speed. Furthermore, the network incorporates an adaptive feature fusion module to dynamically model cross-layer feature correlations, enhancing overall feature collaboration and representation capability. Comprehensive experiments on three public datasets (NUAA-SIRST, NUDT-SIRST, and IRSTD-1K) demonstrate that SDS-Net outperforms state-of-the-art IRSTD methods while maintaining low computational complexity and high inference efficiency, showing superior detection performance and broad application prospects. Our code will be made public at https://github.com/PhysiLearn/SDS-Net.

Infrared images have the advantages of being employed in all weather conditions and exhibiting strong anti-interference abilities and are widely used in many fields. However, there are also problems of low contrast, high noise and blurred details, for which a high dynamic infrared image enhancement method based on wavelet transform is proposed. First, multi-resolution analysis is performed using wavelet transform, and the image is decomposed into a low-frequency information layer and high-frequency information layer, which are processed separately. The low-frequency information layer is subjected to contrast-constrained adaptive histogram equalisation to improve the contrast. The high-frequency information layer is enhanced with adaptive coefficients, and the gain coefficients are determined using gamma transform, which is designed to increase the applicability. Wavelet inverse transform is performed on the processed image to meet the desired requirements. It is shown that the algorithm can effectively enhance the high dynamic image contrast and improve the image detail information.

[[abstract]]Growth of GaxIn1−xAsyP1−y on (001) InP by molecular beam epitaxy employing solid phosphorus and arsenic sources supplied via valved cracking cells was investigated. For growth temperatures between 430–525 °C, the incorporation of As and P was found to be strongly dependent upon the Ga mole fraction, substrate temperature, and incident As and P fluxes. The relative incorporation of As and P is nonlinearly related to the incident column V fluxes, with P incorporation being enhanced with increasingly higher growth temperatures. The data show that the quaternary could be grown lattice matched to InP across the entire wavelength range 1.15–1.65 μm, with strong photoluminescence emission. Heterojunction laser diodes subsequently fabricated with a broad range of emission wavelengths had threshold current densities as low as 1.7 kA/cm2 with differential quantum efficiencies as high as 28%.[[fileno]]2030161010083[[department]]電機工程學

Efficiently generating energetically stable crystal structures has long been a challenge in material design, primarily due to the immense arrangement of atoms in a crystal lattice. To facilitate the discovery of stable materials, we present a framework for the generation of synthesizable materials leveraging a point cloud representation to encode intricate structural information. At the heart of this framework lies the introduction of a diffusion model as its foundational pillar. To gauge the efficacy of our approach, we employed it to reconstruct input structures from our training datasets, rigorously validating its high reconstruction performance. Furthermore, we demonstrate the profound potential of point cloud-based crystal diffusion (PCCD) by generating materials, emphasizing their synthesizability. Our research stands as a noteworthy contribution to the advancement of materials design and synthesis through the cutting-edge avenue of generative design instead of conventional substitution or experience-based discovery.