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A simple chemical vapor deposition technique has been employed to realize Zn1−x Mn x O hollow spherical structures with room temperature ferromagnetism. We propose a special experimental scheme for the appropriate growth temperature, which could not only realize effective evaporation of the source materials, but also make further vaporization of the Zn enclosed in the Mn-doped ZnO layer and avoid the formation of Mn-related oxides on the substrates. A series of controlled experiments have been carried out for better understanding the formation mechanism. The morphology, structure, and composition have been investigated by field-emission scanning electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, and energy dispersive X-ray analysis, indicating that Mn2+ was successfully substituted into the Zn2+ site. Raman results show an additional mode (520−530 cm−1) in the doped samples due to the lattice defects triggered by Mn doping. Multipeak Gaussian fitting of the photoluminscence spectra exhibits an emission related to the d−d transition of Mn2+ from the ground state 6A1(G) to the excited state 4E(G). The origin of the room temperature ferromagnetism has been attributed to the magnetic coupling between Mn atoms.

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Opinion leadership of social network plays an important role in the fields of knowledge spreading, public sentiment controlling, marketing, etc. The opinions of users are derived from the reviews of topics, and the analysis of users' sentiment is helpful in recognizing users' emotional preference of opinion leaders. Thus, it is necessary to classify the forum posts and refine the posts with highly professional knowledge. We then improve the sentiment analysis due to the imbalanced datasets, and establish a comprehensive attention and emotion weight matrix. Accordingly, in this paper, we are going to propose a Leader-PageRank algorithm, which is based on the social network structure and emotional tendency. We do the comparative experiments on the automotive forum, and the results show that the Leader-PageRank algorithm can identify the positive opinion leaders in the professional fields effectively through connecting with the interactions in social networks.