Published May 2021 | Version v1
Journal article

Towards smart cities powered by nanogenerators: Bibliometric and machine learning–based analysis

  • 1. Department of Mechanical Engineering, GMR Institute of Technology, Rajam 532127, Andhra Pradesh (India)
  • 2. Renewable Energy and Micro/Nano Sciences Lab, Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad (Iran, Islamic Republic of)
  • 3. School of Chemical Engineering and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049 (China)
  • 4. Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj (Iran, Islamic Republic of)
  • 5. Microbial Biotechnology Department, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Karaj (Iran, Islamic Republic of)
  • 6. Biofuel Research Team (BRTeam), Terengganu (Malaysia)
  • 7. Henan Province Forest Resources Sustainable Development and High-value Utilization Engineering Research Center, School of Forestry, Henan Agricultural University, Zhengzhou 450002 (China)
  • 8. Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries (AKUATROP), Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu (Malaysia)
  • 9. National Science and Technology Development Agency (NSTDA), Pathum Thani 12120 (Thailand)
  • 10. Fluid Mechanics, Thermal Engineering and Multiphase Flow Research Lab. (FUTURE), Department of Mechanical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangmod, Bangkok 10140 (Thailand)
  • 11. School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0245 (United States)

Description

Highlights: • Piezoelectric, triboelectric, ‎and pyroelectric nanogenerators are generally described. • Progress in nanogenerators is visualized by bibliographic and multivariate methods. • The technical/environmental performance of the nanogenerators' materials is analyzed. • Efficient, inexpensive, and green materials should be used in future nanogenerators. • The analysis results indicate that China is forefront in nanogenerators research. Nanogenerators have attracted particular attention during the last decade. A nanogenerator harness mechanical or thermal energy to produce electricity without any need for battery. In this paper, general descriptions of different types of nanogenerators, including piezoelectric, triboelectric, and pyroelectric, are presented. Next, bibliometric analysis and unsupervised machine learning–based analysis (principle component analysis) are carried out to determine the trends in this field of science. The current developments and directions for technology commercialization are briefly discussed. Additionally, the output performance and environmental consequences of active materials used in nanogenerators are analyzed by principle component analysis. The analysis reveals China's sensible investment in the nanogenerator field during the last five years. The number of articles published by Chinese scholars is over 1000 during 2015–2020 with a considerable distance from the USA. It is also deduced that European countries need to pay more attention to this field if they want to compete with the USA, China, and South Korea. Overall, due to the huge amount of waste thermal energy worldwide, more efforts and resources have to be focused and invested in developing pyroelectric nanogenerators. Moreover, there is a need to find low-cost materials having a favorable environmental profile to fabricate nanogenerators.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.nanoen.2021.105844

Additional details

Identifiers

DOI
10.1016/j.nanoen.2021.105844;
PII
S2211285521001026;

Publishing Information

Journal Title
Nano Energy (Print)
Journal Volume
83
Journal Page Range
vp.
ISSN
2211-2855

INIS

Country of Publication
Netherlands
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
54017222
Subject category
S97: MATHEMATICAL METHODS AND COMPUTING; S42: ENGINEERING;
Descriptors DEI
MACHINE LEARNING; MATERIALS; MULTIVARIATE ANALYSIS; PERFORMANCE; PIEZOELECTRICITY
Descriptors DEC
ALGORITHMS; ARTIFICIAL INTELLIGENCE; ELECTRICITY; LEARNING; MATHEMATICAL LOGIC; MATHEMATICS; STATISTICS

Optional Information

Copyright
Copyright (c) 2021 Elsevier Ltd. All rights reserved.