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Magnetic nanoparticles research: a scientometric analysis of development trends and research fronts

Author

Listed:
  • Ping Liu

    (Wuhan University)

  • Bao-Li Chen

    (Wuhan University of Technology)

  • Kan Liu

    (Zhongnan University of Economics and Law)

  • Hao Xie

    (Wuhan University of Technology)

Abstract

The research on magnetic nanoparticles attracts scientists from broad disciplines including chemistry, physics, and biomedical science. It is a great challenge for scientists from different background to discover the development trends and research fronts that are embodied in publications from different disciplines. This article aims to portray the global research profile and detect research fronts of magnetic nanoparticles by taking advantages of scientometric approaches. A total of 13,464 publications regarding magnetic nanoparticles indexed by Web of Science during 2000–2015 were used for a detailed analysis of the global magnetic nanoparticles research performance. The 500 most-cited publications on magnetic nanoparticles were analyzed for the temporal–spatial distribution characteristics as well as co-citation networks and co-word networks to identify research fronts and development trends. This study revealed that ‘block-copolymers’ attracted most attentions in high quality research of MNPs. Researches on yadh-bound MNPs were among the most hot MNPs topics. Recently, researches on catalysis characteristics emerged as the hot MNPs topics.

Suggested Citation

  • Ping Liu & Bao-Li Chen & Kan Liu & Hao Xie, 2016. "Magnetic nanoparticles research: a scientometric analysis of development trends and research fronts," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1591-1602, September.
    • Handle: RePEc:spr:scient:v:108:y:2016:i:3:d:10.1007_s11192-016-2017-z
    DOI: 10.1007/s11192-016-2017-z
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    References listed on IDEAS

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    1. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
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    3. Bernhard Gleich & Jürgen Weizenecker, 2005. "Tomographic imaging using the nonlinear response of magnetic particles," Nature, Nature, vol. 435(7046), pages 1214-1217, June.
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    Cited by:

    1. S. Lozano & L. Calzada-Infante & B. Adenso-Díaz & S. García, 2019. "Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 609-629, August.
    2. Carlos Olmeda-Gómez & Maria-Antonia Ovalle-Perandones & Antonio Perianes-Rodríguez, 2017. "Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 195-217, October.
    3. Mao, Jin & Liang, Zhentao & Cao, Yujie & Li, Gang, 2020. "Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes," Journal of Informetrics, Elsevier, vol. 14(4).

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