Elsevier

Nano Energy

Volume 71, May 2020, 104636
Nano Energy

Full paper
Monitoring and forecasting the development trends of nanogenerator technology using citation analysis and text mining

https://doi.org/10.1016/j.nanoen.2020.104636Get rights and content

Highlights

  • A framework to monitoring the evolutionary path of nanogenerator technology and forecast its trends.

  • Five detailed evolutionary paths of nanogenerator technology.

  • Six significant technology trends of nanogenerator technology.

  • The leading players in the nanogenerator research field are identified.

Abstract

The rapid development of nanogenerator technology is seen as potentially having a major impact on people's lives and would transform the existing energy industry. Monitoring the emergence of nanogenerator technology is essential to understanding and detecting its changing trends at early stages. This information is crucial for academic and government research, the development of strategic planning, social investment and enterprise practices. Therefore, this paper proposes a framework that uses academic papers and patents as data resources and integrates citation analysis and text mining to monitor the evolutionary path of nanogenerator technology and forecast its trends. We began by using citation analysis to mine the technical knowledge contained in academic papers and to monitor the evolutionary path of nanogenerator technology. This was followed by employing the applied Hierarchical Dirichlet Process (HDP) topic model which is a kind of text mining method used to extract the technical topics contained in academic papers. We then analyzed the differences from the results of the HDP topic model and citation analysis to improve the evolutionary path of nanogenerator technology missing details due to the time lag of citation analysis. Further application of the HDP topic model allowed us to extract the technical topics contained in patents. Finally, we carried out the analysis of gaps between science and technology, combining them with expert knowledge and the evolutionary path of nanogenerator technology to forecast development trends. This paper contributes to our understanding of how nanogenerator technology emerges and develops and will be of specific interest to nanogenerator technology R&D experts.

Graphical abstract

This study proposed a framework that used academic papers and patents as data resources and integrated the citation analysis and text mining to monitoring the evolutionary path of nanogenerator technology and forecast its trends. Five detailed evolutionary paths of the nanogenerator technology were constructed, and six significant technology trends of nanogenerator technology were found. The leading players in the nanogenerators research field were identified. Fig. 1 Clustering visualization for nanogenerators research.

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Introduction

In recent years, emerging technologies with disruptive characteristics, such as artificial intelligence, information technology, biotechnology and nanotechnology have been surfacing rapidly, with major advancements. This not only changed an existing industry but also created new industries and has had a significant impact on socio-economic structure [1]. As an emerging technology with disruptive characteristics, nanogenerator (NG) technology presents a promising application for areas including but not limited to self-powered systems, mechanical or thermal energy harvesting and smart wearable devices [2]. The evolving NG technologies have a potentially disruptive impact on self-powered devices and future development of health monitoring, mobile communication, biomedical sensing, environmental protection and homeland security [3,4]. How to identify and understand the evolutionary path of nanogenerator technology and capture future trends and development opportunities as early as possible is crucial for strategic R&D planning for governments and enterprises seeking to gain first-mover advantage in the competitive business environment [[5], [6], [7], [8]].

Some experts have studied the evolutionary path and trends of nanogenerator technology [[9], [10], [11], [12], [13]]. These studies are based on expert knowledge and literature reviews, and the research contents involve nanogenerators research directions or a sub-field of nanogenerators. Aiming at a current research hotspot in the field of NG technology, the work of some scholars has been limited to reviewing related research work to discuss its theory, development and future directions [[9], [10], [11], [12]]. Or they reviewed the field of nanogenerators but did not comprehensively summarize the detailed evolutionary path and trends of NG technology. These studies sort out the evolution process of different sub-domains of NGs and indicate future trends for corresponding research directions, which are important for promoting academic research in this field [13].

With the rapid development of nanogenerators and the corresponding increase in the number of academic papers and patents related to them, the sheer volume of this technical information makes it difficult to perform a quick analysis of technology trends based solely on expert knowledge. Therefore, based on public technical literature, technology trends in forecasting activities tend to use quantitative approaches to explore trends and provide early indications of potential changes and developments for creating anticipatory policy and strategy [8]. Scholars have begun to use quantitative methods such as bibliometrics, patent analysis, technology roadmaps and text mining to study cooperation in the field of NGs, research hotspots, topics evolution and potential application areas [[2], [3], [4], [5], [6]]. However, with few scholars studying the detailed evolutionary path of NG technology and, as the trends prediction is based only on expert knowledge, it follows that not many have used the objective data analysis method to study and predict these trends. Therefore, this paper proposes a framework that integrates the citation analysis and text mining to monitor the evolutionary path of nanogenerator technology and forecast its trends.

The rest of this paper is organised as follows. Section 2 briefly presents the related work. Section 3 provides the proposed method. Section 4 analyzes the case study of nanogenerator technology. Section 5 concludes the paper.

Section snippets

Technology trend

A technology trend is considered as a continuously growing technology area with a certain pattern, one that should have existed for a certain period of time [8,14]. Many approaches have been developed to discover patterns and identify and forecast technology trends. One that many researchers have tried is to discover the pattern from the relationships between science and technology.

These relationships have long been and continue to be the subject of intense debate, both within academia and in

Methodology

A framework is proposed here whereby academic papers and patents are used as data resources and citation analysis and text mining are integrated to monitor the evolutionary path of nanogenerator technology and forecast its trends. The main ideas of this framework are as follows: to begin with, using the academic papers of nanogenerator technology as data resources, we applied citation analysis to mine the technical knowledge contained in academic papers, and to monitor the evolutionary path of

Data collection and preprocessing

In this paper, Web of Science (WOS) and Derwent Innovations Index (DII) databases were used as sources for collecting data. The term “nanogenerator*” was used as the query to search academic papers from WOS. In all, 3024 papers were retrieved from the database from 2006 to 2018, performed on February 17th, 2019. The term “(nanogenerator*) OR (PENG) OR (TENG) OR (nanometer generator)” was used as the query to search the patent data from DII, with the result of 431 patents being collected from

Conclusions

This study attempted to move the nanogenerator technology research field forward by using citation analysis and a text mining method. To do so, we proposed a framework that used academic papers and patents as data resources and integrated the citation analysis and text mining to monitor the evolutionary path of nanogenerator technology and forecast trends in the technology. In the framework, citation analysis and text mining approaches were applied to mine the technical knowledge and

Credit Author Statement

Xin Li: Conceptualization, Methodology, Writing - original draft. Mingjie Fan: Data analysis, Visualization. Yuan Zhou: Research design and writing. Jing Fu: Results analysis. Fei Yuan: Reviewing and editing. Lucheng Huang:Research design.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

A previous version of this paper was presented in the 2019 Global TechMining Conference. We are grateful to attendee for their helpful comments and suggestions. Many thanks to Dr. Liang Xu who is from Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences for his helpful consultations. We are grateful to the two anonymous reviewers for their valuable comments. This paper is supported by the National Natural Science Foundation of China (Grant 71673018), and international

Xin Li is an associate researcher in College of Economics and Management, Beijing University of Technology. From September, 2010 to September, 2011, he was a visiting Ph. D candidate student in Department of Engineering and Technology Management, Portland State University (USA). He has long been engaged in patent informatics, emerging technologies, and innovation management. He has published many papers on international journals, such as, Technological Forecasting and Social Change, Science and

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  • Cited by (0)

    Xin Li is an associate researcher in College of Economics and Management, Beijing University of Technology. From September, 2010 to September, 2011, he was a visiting Ph. D candidate student in Department of Engineering and Technology Management, Portland State University (USA). He has long been engaged in patent informatics, emerging technologies, and innovation management. He has published many papers on international journals, such as, Technological Forecasting and Social Change, Science and Public Policy, International Journal of Technology Management. He is also the editorial board member of IEEE Transactions on Engineering Management, International Journal of Innovation and Technology Management.

    Mingjie Fan is currently working toward the master's degree under the supervision of associate researcher Xin Li in College of Economics and Management, Beijing University of Technology. Her research interests include data mining and technology forecasting.

    Yuan Zhou (Joseph) is the Associate Professor with Tenure at the School of Public Policy and Management, Tsinghua University (China). He also serves as the Assistant Director of China Institute for Engineering Development Strategies. His recent research interests include public policy, innovation policy, and innovation management. He has published several papers in Nano Energy, Science and Public Policy, Technological Forecasting & Social Change (TFSC), Engineering, IEEE Transactions on Engineering Management (TEM), Energy Policy, Scientometrics, etc. He serves as the Department Editor of IEEE TEM, as well as the guest editor of IEEE TEM, TFSC, R&D Management, and Scientometrics.

    Jing Fu is currently working toward the Ph.D. degree under the supervision of Prof. Yudong Hou in College of Materials Science and Engineering, Beijing University of Technology. His research interests include ferroelectric ceramics, polymer-based composites, new nanomaterials for energy storage, conversion and their applications. He has published many papers on international journals, such as, Nanoenergy, ACS Appl Mater Interfaces.

    Fei Yuan is an assistant professor in the College of Economics and Management at Beijing University of Technology. He holds a PhD in Innovation Management from Tokyo Institute of Technology and serves as the editorial review board member of IEEE Transactions on Engineering Management. He has been engaged in the Chinese and Japanese key research projects in MOT filed. He served for Japanese and Chinese universities and the Chinese government. He has experience in many educational programs of international cooperation & exchanges. His recent research interest involves technological innovation in emerging industries, S&T policy, and strategic management of technology.

    Huang Lucheng is a professor in College of Economics and Management, Beijing University of Technology. He is an expert enjoying the special allowance of the China State Council. He has long been engaged in innovation management, future-oriented emerging technology analysis and management for decades, and has published more than 300 academic papers in academic journals and conferences. He has been rewarded with Scientific and Technological Progress Award in Provincial and Ministerial Level, Outstanding Social Science Achievement Award in Philosophy of the Social Sciences (First/Second/Third Prizes), in China.

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