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The golden eras of graphene science and technology: Bibliographic evidences from journal and patent publications

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  • Nguyen, Ai Linh
  • Liu, Wenyuan
  • Khor, Khiam Aik
  • Nanetti, Andrea
  • Cheong, Siew Ann

Abstract

Today’s scientific research is an expensive enterprise funded primarily by taxpayers’ and corporate groups’ monies. All nations want to discover fields of study that promise to create future industries, and dominate these by building up and securing scientific and technological expertise early. However, the conversion of scientific leadership into market dominance remains very much an alchemy. To gain insights into how science becomes technology, we focused on graphene (which shows promise in batteries, sensors, flexible displays and other technologies) as a case study. In particular, we asked whether research on the material is on track to deliver all its technological promises. To answer this question, we analyzed in this paper bibliometric records of scientific journal publications and patents related to graphene. While performing straightforward analyses at the aggregate and temporal level to do so, we stumbled upon evidences that suggest ‘Golden Eras’ of graphene science and technology in the recent past. To confirm this unexpected finding, we developed a novel simulation-based method to determine how the interest levels in graphene science and technology change with time. We then found compelling evidences that these interest levels peaked in 2010 and 2012 respectively, despite the continued growth of journal and patent publications in this area. This suggests that publication numbers in a research topic could sometimes give rise to false positives concerning its importance.

Suggested Citation

  • Nguyen, Ai Linh & Liu, Wenyuan & Khor, Khiam Aik & Nanetti, Andrea & Cheong, Siew Ann, 2020. "The golden eras of graphene science and technology: Bibliographic evidences from journal and patent publications," Journal of Informetrics, Elsevier, vol. 14(4).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:4:s1751157719303542
    DOI: 10.1016/j.joi.2020.101067
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    Cited by:

    1. Xiaoli Wang & Yun Liu & Lingdi Chen & Yifan Zhang, 2022. "Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network," SAGE Open, , vol. 12(4), pages 21582440221, December.
    2. Wang, Chang & Geng, Hongjun & Sun, Rui & Song, Huiling, 2022. "Technological potential analysis and vacant technology forecasting in the graphene field based on the patent data mining," Resources Policy, Elsevier, vol. 77(C).
    3. Ai Linh Nguyen & Wenyuan Liu & Khiam Aik Khor & Andrea Nanetti & Siew Ann Cheong, 2022. "Strategic differences between regional investments into graphene technology and how corporations and universities manage patent portfolios," Papers 2208.03719, arXiv.org.

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