Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021
Abstract
:1. Introduction
- Q1: How many research articles were annually published between 2017 and 2021 in material science linked to industry 4.0?
- Q2: Who are the most cited authors in studies associated with industry 4.0?
- Q3: Which papers are the most cited in material science combined with industry 4.0?
- Q4: What journals host the highest quantities of papers in this research area?
- Q5: What are the leaders’ institutions in the focused research field?
- Q6: What are the most active sponsor institutions in the selected period?
- Q7: What are the top ten countries publishing on this subject?
2. Materials and Methods
2.1. Study Design
2.2. Data Source
2.3. Search Strategy
2.4. Bibliometric Analysis
2.5. Limitations
3. Results and Discussion
3.1. Trends in the Annual Production of Original Papers
3.2. Most Cited Authors and Their Collaborations
3.3. Most Cited Research Articles
3.4. Journals That Host a Higher Number of Articles
3.5. Most Productive Institutions and Their Collaborations
3.6. Most Participative Funding Agencies
3.7. Most Contributing Countries and Their Collaborations
4. Conclusions
- The production of original papers in the explored field is exponentially growing.
- A minimum of 14 published papers are required to become one of the most cited authors on the tracked type of research.
- Most cited articles in these fields deal with artificial intelligence and big data applications in manufacturing industries.
- The top journals preferred to spread initiatives of industry 4.0 in conjunction with the material science field count with a JCR higher than 2.5.
- The most productive institutions delivered at least 22 documents to be part of the top ten.
- Funding agencies pursuing the top ten of given awards need to support a minimum of 16 papers.
- China and the United States are the most implicated countries regarding the fourth industrial revolution applied to material science, whose success stems from the incorporation of specific public policies.
- Deep learning represents the most attractive technology in machine learning to perform new studies in material science.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Scopus | WoS | |||||
---|---|---|---|---|---|---|
Rank | Author | No. of Paper | T. Link Strength | Author | No. of Paper | T. Link Strength |
1st | Zhang Y | 44 | 60 | Wen C | 5 | 8 |
2nd | Li J | 32 | 51 | Su Y | 5 | 8 |
3rd | Wang J | 37 | 48 | Xue D | 6 | 8 |
4th | Liu Y | 38 | 44 | Liang H | 5 | 5 |
5th | Li Y | 33 | 42 | Qiao Z | 5 | 5 |
Appendix B
Scopus | WoS | |||||
---|---|---|---|---|---|---|
Rank | Institution | No. of Paper | T. Link Strength | Institution | No. of Paper | T. Link Strength |
1st | Technical University of Berlin | 8 | 14 | Chinese academy of sciences | 27 | 58 |
2nd | University of Zilina | 10 | 12 | University of Chinese academy of sciences | 14 | 32 |
3rd | Cadi Ayyad University | 3 | 8 | Northwestern polytechnic university | 12 | 32 |
4th | The institute of smart big data analytics | 5 | 8 | University of science and technology Beijing | 18 | 31 |
5th | University of Chinese academy of sciences | 7 | 8 | Georgia institute of technology | 8 | 26 |
Appendix C
Scopus | WoS | |||
---|---|---|---|---|
Rank | Country | Total, Link Strength | Country | Total, Link Strength |
1st | United States | 252 | China | 154 |
2nd | China | 176 | United States | 143 |
3rd | United Kingdom | 63 | United Kingdom | 70 |
4th | Germany | 56 | Germany | 45 |
5th | India | 33 | Australia | 31 |
Appendix D
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Scopus | WoS | |||||||
---|---|---|---|---|---|---|---|---|
Rank | Author | h-Index | TC | No. of Paper | Author | h-Index | TC | No. of Paper |
1st | Zhang Y | 16 | 1452 | 44 | Zhang Y | 12 | 886 | 23 |
2nd | Liu Y | 16 | 1606 | 39 | Li Y | 9 | 321 | 21 |
3rd | Wang J | 18 | 2453 | 37 | Li H | 9 | 314 | 20 |
4th | Wang Y | 15 | 690 | 33 | Zhang Z | 8 | 230 | 19 |
5th | Li J | 14 | 797 | 32 | Liu Y | 8 | 897 | 18 |
6th | Li Y | 14 | 624 | 31 | Li J | 8 | 159 | 17 |
7th | Zhang Z | 11 | 348 | 30 | Wang J | 11 | 956 | 15 |
8th | Zhang J | 12 | 862 | 28 | Wang Y | 8 | 216 | 14 |
9th | Li X | 11 | 470 | 27 | Zhang L | 6 | 755 | 14 |
10th | Li H | 11 | 364 | 25 | Zhang X | 9 | 181 | 14 |
Autor, Year | Document Title and Journal Name | Journal Name | TC Scopus | TC WoS |
---|---|---|---|---|
Zhong RY, 2017 | Intelligent Manufacturing in the Context of Industry 4.0: A Review | Engineering | 1207 | N/A |
Tao F, 2018 | Digital twin-driven product design, manufacturing, and service with big data | Int J Adv Manuf Technol | 1136 | 822 |
Frank AG, 2019 | Industry 4.0 technologies: Implementation patterns in manufacturing companies | Int J Prod Econ | 795 | 633 |
Wang J, 2018 | Deep learning for smart manufacturing: Methods and applications | J Manuf Syst | 747 | 583 |
Wu Z, 2018 | MoleculeNet: a benchmark for molecular machine learning | Chem Sci | 637 | N/A |
Qi Q, 2018 | Digital twin and big data towards smart manufacturing and industry 4.0: 360-degree comparison | IEEE Access | 595 | 434 |
Schütt KT, 2018 | SchNet—A deep learning architecture for molecules and materials | J Chem Phys | 579 | N/A |
Ghobakhloo M, 2018 | The future of manufacturing industry: a strategic roadmap toward Industry 4.0 | J Manuf Technol Manage | 503 | N/A |
Liu Y, 2017 | Materials discovery and design using machine learning | J Materiomics | NA | 469 |
Chmiela S, 2017 | Machine learning of accurate energy-conserving molecular force fields | Sci Adv | 461 | N/A |
Scopus | WoS | |||||
---|---|---|---|---|---|---|
Rank | Journal Name | No. of Papers (%)N = 2157 | Impact Factor SJR (2021) | Journal Name | No. of Papers (%) N = 937 | Impact Factor JCR (2021) |
1st | Computational Materials Science | 58 (2.69) | 0.777 | Computational Materials Science | 45 (4.80) | 3.572 |
2nd | IEEE Access | 38 (1.76) | 0.927 | IEEE Access | 26 (2.77) | 3.476 |
3rd | Journal of Physical Chemistry Letters | 32 (1.48) | 2.009 | International Journal of Advanced Manufacturing Technology | 19 (2.03) | NA |
4th | Journal of Chemical Information and Modeling | 31 (1.44) | 1.223 | ACS Applied Materials & Interfaces | 15 (1.60) | 10.383 |
5th | Journal of Physical Chemistry C | 30 (1.39) | 1.103 | Journal of Intelligent Manufacturing | 15 (1.60) | 7.136 |
6th | NPJ Computational Materials | 29 (1.34) | 2.967 | Advanced Theory and Simulations | 14 (1.49) | 4.105 |
7th | Sustainability (Switzerland) | 26 (1.21) | 0.664 | Journal of Manufacturing Systems | 13 (1.39) | 9.498 |
8th | Chemistry of Materials | 24 (1.11) | 2.93 | Materials & Design | 13 (1.39) | 9.417 |
9th | Journal of Chemical Physics | 24 (1.11) | 1.103 | Acta Materialia | 11 (1.17) | 9.209 |
10th | ACS Applied Materials & Interfaces | 23 (1.07) | 2.143 | Applied Sciences-Basel | 11 (1.17) | 2.838 |
Scopus | WoS | |||||
---|---|---|---|---|---|---|
Rank | Affiliations | Country | No. of Paper | Affiliations | Country | No. of Paper |
1st | University of Science and Technology Beijing | China | 59 | University of Science and Technology Beijing | China | 55 |
2nd | University of California | United States | 47 | Shanghai University | China | 42 |
3rd | Shanghai University | China | 42 | University of Chinese Academy of Sciences | China | 28 |
4th | Massachusetts Institute of Technology | United States | 38 | Nanyang Technological University | Singapore | 26 |
5th | Zhejiang University | China | 33 | Chongqing University | China | 25 |
6th | Shanghai Jiao Tong University | China | 30 | Beihang University | China | 24 |
7th | University of Chinese Academy of Sciences | China | 28 | Northwestern Polytech University | China | 24 |
8th | Chongqing University | China | 24 | University of Illinois | United States | 24 |
9th | South China University of Technology | China | 24 | Guangzhou University | China | 22 |
10th | Tsinghua University | China | 24 | Zhejiang University | China | 22 |
Scopus | WoS | |||||
---|---|---|---|---|---|---|
Rank | Affiliations | Country | No. of Paper | Affiliations | Country | No. of Paper |
1st | National Natural Science Foundation of China | China | 350 | National Natural Science Foundation of China | China | 184 |
2nd | National Science Foundation | United States | 182 | National Science Foundation | United States | 68 |
3rd | U.S. Department of Energy | United States | 122 | National Key Research and Development Program of China | China | 44 |
4th | National Key Research and Development Program of China | China | 94 | Fundamental Research Funds for The Central Universities | China | 35 |
5th | Office of Science | United States | 79 | U.S. Department of Energy | United States | 33 |
6th | Fundamental Research Funds for the Central Universities | China | 72 | Ministry of Education Culture Sports Science and Technology | Japan | 25 |
7th | Japan Society for the Promotion of Science | Japan | 52 | European Commission | Belgium | 23 |
8th | Ministry of Science and Technology of the People’s Republic of China | China | 50 | Japan Society for the Promotion of Science | Japan | 22 |
9th | Horizon 2020 Framework Programme | Belgium | 49 | German Research Foundation | Germany | 18 |
10th | Basic Energy Sciences | United States | 48 | Grants-in-Aid for Scientific Research | Japan | 16 |
Scopus | Wos | |||||
---|---|---|---|---|---|---|
Rank | Country | Frequency | Total Citations | Country | Frequency | Total Citations |
1st | China | 1453 | 11514 | China | 1226 | 7238 |
2nd | United States | 1318 | 12957 | United States | 784 | 4661 |
3rd | Japan | 311 | 1310 | Japan | 210 | 698 |
4th | Germany | 246 | 1448 | Germany | 175 | 684 |
5th | United Kingdoms | 229 | 1673 | India | 162 | 325 |
6th | India | 227 | 795 | South Korea | 158 | 413 |
7th | South Korea | 210 | 1158 | United Kingdoms | 149 | 998 |
8th | Canada | 107 | 545 | Australia | 92 | 393 |
9th | Australia | 106 | 524 | Spain | 87 | 208 |
10th | Spain | 103 | 462 | Singapore | 76 | 547 |
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Alviz-Meza, A.; Orozco-Agamez, J.; Quinayá, D.C.P.; Alviz-Amador, A. Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021. ChemEngineering 2023, 7, 2. https://doi.org/10.3390/chemengineering7010002
Alviz-Meza A, Orozco-Agamez J, Quinayá DCP, Alviz-Amador A. Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021. ChemEngineering. 2023; 7(1):2. https://doi.org/10.3390/chemengineering7010002
Chicago/Turabian StyleAlviz-Meza, Anibal, Juan Orozco-Agamez, Diana C. P. Quinayá, and Antistio Alviz-Amador. 2023. "Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021" ChemEngineering 7, no. 1: 2. https://doi.org/10.3390/chemengineering7010002