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| 1 | Experimental realization of honeycomb borophene显示文摘We report the successful preparation of a purely honeycomb,graphene-like borophene,by using an Al(11 1) surface as the substrate and molecular beam epitaxy(MBE) growth in ultrahigh vacuum.Scanning tunneling microscopy(STM) images reveal perfect monolayer borophene with planar,non-buckled honeycomb lattice similar as graphene.Theoretical calculations show that the honeycomb borophene on Al(1 1 1) is energetically stable.Remarkably,nearly one electron charge is transferred to each boron atom from the Al(1 1 1) substrate and stabilizes the honeycomb borophene structure,in contrast to the negligible charge transfer in case of borophene/Ag(1 1 1).The existence of honeycomb 2 D allotrope is important to the basic understanding of boron chemistry,and it also provides an ideal platform for fabricating boron-based materials with intriguing electronic properties such as Dirac states. | Wenbin Li Longjuan Kong Caiyun Chen Jian Gou Shaoxiang Sheng Weifeng Zhang Hui Li Lan Chen Peng Cheng Kehui Wu | 2018 | Science Bulletin2018，63，5： | 7 |
| 2 | Recent progress on borophene: Growth and structures显示文摘硼是不平常的物理特征从电子缺乏的、高度去除的共有原子价契约导出的周期表和展览上的碳的邻居。作为碳的最近的邻居,硼以类似于碳的许多方法,例如有采用 sp 2 杂交的短共有原子价半径和灵活性。因此,硼能能够形成单层 graphene 的结构的类似物。尽管许多理论报纸报导了发现硼的二维的同素异形体,为如此的原子薄的硼 nanostructures 没有试验性的证据直到 2016。最近,单个层的硼的成功的合成(叫作 borophene ) 在 Ag (111 ) 上,底层打开硼 nanostructures 的时代。在这简短评论,我们将讨论以合成技术,描述和原子模型在 borophene 上被做了的进步。然而, borophene 只在幼年期;更多的努力被期望在优秀样品的控制合成上在未来被作并且定制它的物理性质。 | Longjuan Kong Kehui Wu Lan Chen | 2018 | Frontiers of physics2018，13，3： | 1 |
| 3 | Celastrol inhibits vasculogenesis by suppressing the VEGF-induced functional activity of bone marrow-derived endothelial progenitor cells显示文摘 | Shuai Huang Yubo Tang Xingdong Cai Xinsheng Peng Xingmo Liu Longjuan Zhang Yucheng Xiang Dechun Wang Xi Wang Tao Pan | 2012 | Biochemical and Biophysical Research Communications2012，，： | 1 |
| 4 | License Plate Recognition via Attention Mechanism显示文摘License plate recognition technology use widely in intelligent trafficmanagement and control. Researchers have been committed to improving thespeed and accuracy of license plate recognition for nearly 30 years. This paperis the first to propose combining the attention mechanism with YOLO-v5and LPRnet to construct a new license plate recognition model (LPR-CBAMNet).Through the attention mechanism CBAM(Convolutional Block AttentionModule), the importance of different feature channels in license platerecognition can be re-calibrated to obtain proper attention to features. Forceinformation to achieve the purpose of improving recognition speed andaccuracy. Experimental results show that the model construction methodis superior in speed and accuracy to traditional license plate recognitionalgorithms. The accuracy of the recognition model of the CBAM model isincreased by two percentage points to 97.2%, and the size of the constructedmodel is only 1.8 M, which can meet the requirements of real-time executionof embedded low-power devices. The codes for training and evaluating LPRCBAM-Net are available under the open-source MIT License at: https://github.com/To2rk/LPR-CBAM-Net. | Longjuan Wang Chunjie Cao Binghui Zou Jun Ye Jin Zhang | 2023 | Computers, Materials & Continua2023，，4： | 0 |
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