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We investigate the photovoltaic behaviors of magnetic graphene interconnect junctions, which are constructed by zigzag graphene nanoribbons (ZGNRs), with the aim to produce pure spin current by photogalvanic effect (PGE). Two kinds of interconnect junctions are designed by connecting two 6-ZGNR with a carbon hexagon (C6) and a carbon tetragon (C4), respectively. It is found that zero charge current is produced under irradiation of light in both structures due to the presence of spatial inversion symmetry. Nevertheless, behind the zero charge current, net pure spin current is produced in the structure with a C6, but not in the structure with a C4. This difference originates from their different edge state distribution and different spatial inversion symmetry of the spin density. However, interestingly, local edge pure spin current can be obtained in both structures. More importantly, the pure spin current generation is independent of the photon energy, polarization type or polarization angle, suggesting a robust way of generating pure spin current with PGE and new possibility of graphene’s applications in spintronics.

The recognizing underwater targets is a crucial component of autonomous underwater vehicle patrols and detection efforts. In the process of visual image recognition in real underwater environment, the spatial and semantic features of the target often appear to different degrees of loss, and the scarcity of specific types of underwater samples leads to unbalanced data on categories. This kind of problem makes the target features appear weak and seriously affects the accuracy of underwater target recognition. Traditional deep learning methods based on data and feature enhancement cannot achieve ideal recognition effect. Based on the above difficulties, this paper proposes an improved feature enhancement network for weak feature target recognition. Firstly, a multi-scale spatial and semantic feature enhancement module is constructed to extract the feature information of the extraction target accurately. Secondly, this paper solves the influence of target feature distortion on classification through multi-scale feature comparison of positive and negative samples. Finally, the Rank & Sort Loss function was used to train the depth target detection to solve the problem of recognition accuracy under highly unbalanced sample data. Experimental results show that the recognition accuracy of the proposed method is 2.28% and 3.84% higher than that of the existing algorithms in the recognition of underwater fuzzy and distorted target images, which demonstrates the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this work, we study the photogalvanic effect of a zigzag graphene nanoribbon junction with a centro-symmetrical structure which consists of 8 zigzag chains by density functional calculations. Specifically, we focus on the cases where the irradiated region is just part of the central region and located at different positions, with an aim to see how the spin dependent photocurrents will change and whether pure spin current can be obtained. It is found that the magnitude of the spin-dependent photocurrents increases with a gradual increase of the irradiated region and pure spin current is achieved when and only when the entire central region is irradiated. In addition, we studied the additive effect in this device to see that if we divide the central region into two parts, whether the sum of the spin current generated by irradiating the two parts individually is equal to that produced when the entire central region is irradiated. It is found that the sum of the spin currents produced by irradiating the two parts individually is smaller than that obtained by irradiating the whole central region, which means that the rule of "1 + 2 = 3" does not hold and the coupling effect between the two parts is important in photocurrent generation. [ABSTRACT FROM AUTHOR]