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Characterizing knowledge diffusion of Nanoscience & Nanotechnology by citation analysis

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  • Guang Yu

    (Harbin Institute of Technology)

  • Ming-Yang Wang

    (Harbin Institute of Technology)

  • Da-Ren Yu

    (Harbin Institute of Technology)

Abstract

This study investigates the knowledge diffusion patterns of Nanoscience & Nanotechnology (N&N) by analyzing the overall research interactions between N&N and nano-related subjects through citation analysis. Three perspectives were investigated to achieve this purpose. Firstly, the overall research interactions were analyzed to identify the dominant driving forces in advancing the development of N&N. Secondly, the knowledge diffusion intensity between N&N and nano-related subjects was investigated to determine the areas most closely related to N&N. Thirdly, the diffusion speed was identified to detect the time distance of knowledge diffusion between N&N and nano-related subjects. The analysis reveals that driving forces from the outside environment rather than within N&N itself make the foremost contributions to the development of N&N. From 1998 to 2007, Material Science, Physics, Chemistry, N&N, Electrical & Electronic and Metallurgy & Metallurgical Engineering are the key contributory and reference subjects for N&N. Knowledge transfer within N&N itself is the quickest. And the speed of knowledge diffusion from other subjects to N&N is slower than that from N&N to other subjects, demonstrating asymmetry of knowledge diffusion in the development of N&N. The results indicate that N&N has matured into a relatively open, diffuse and dynamic system of interactive subjects.

Suggested Citation

  • Guang Yu & Ming-Yang Wang & Da-Ren Yu, 2010. "Characterizing knowledge diffusion of Nanoscience & Nanotechnology by citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(1), pages 81-97, July.
    • Handle: RePEc:spr:scient:v:84:y:2010:i:1:d:10.1007_s11192-009-0090-2
    DOI: 10.1007/s11192-009-0090-2
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    Cited by:

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    3. Ramiro H. Gálvez, 2017. "Assessing author self-citation as a mechanism of relevant knowledge diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1801-1812, June.
    4. Kang, Inje & Yang, Jiseong & Lee, Wonjae & Seo, Eun-Yeong & Lee, Duk Hee, 2023. "Delineating development trends of nanotechnology in the semiconductor industry: Focusing on the relationship between science and technology by employing structural topic model," Technology in Society, Elsevier, vol. 74(C).
    5. Young-Don Cho & Hoo-Gon Choi, 2013. "Principal parameters affecting R&D exploitation of nanotechnology research: a case for Korea," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 881-899, September.
    6. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
    7. Jiancheng Guan & Yuan Shi, 2012. "Transnational citation, technological diversity and small world in global nanotechnology patenting," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 609-633, December.
    8. Guijie Zhang & Luning Liu & Fangfang Wei, 2019. "Key nodes mining in the inventor–author knowledge diffusion network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 721-735, March.
    9. John McLevey & Alexander V. Graham & Reid McIlroy-Young & Pierson Browne & Kathryn S. Plaisance, 2018. "Interdisciplinarity and insularity in the diffusion of knowledge: an analysis of disciplinary boundaries between philosophy of science and the sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 331-349, October.
    10. Sebastian Galiani & Ramiro H. Gálvez, 2017. "The Life Cycle of Scholarly Articles across Fields of Research," NBER Working Papers 23447, National Bureau of Economic Research, Inc.
    11. Guijie Zhang & Yuqiang Feng & Guang Yu & Luning Liu & Yanqiqi Hao, 2017. "Analyzing the time delay between scientific research and technology patents based on the citation distribution model," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1287-1306, June.

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