Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021

被引:11
作者
Alviz-Meza, Anibal [1 ,2 ,3 ]
Orozco-Agamez, Juan [4 ]
Quinaya, Diana C. P. [5 ]
Alviz-Amador, Antistio [6 ]
机构
[1] Univ Senor Sipan, Fac Ingn Arquitectura & Urbanismo, Grp Invest Deterioro Mat Trans Energet & Ciencia, Chiclayo 14001, Peru
[2] Univ Senor Sipan, Fac Enginering Arquitecture & Urbanism, Semillero Invest Corros Met Energias Sostenibles, Consulado 48 152, Cartagena 14001, Peru
[3] Univ Cartagena, Dept Chem Engn, Sede Piedra Bolivar, Ave Consulado 48 152, Cartagena 130001, Colombia
[4] Univ Ind Santander, Escuela Ingn Met, Grp Invest Corros, Parque Tecnol Guatiguara, Barranco 681011, Lima, Colombia
[5] Univ Ingn & Tecnol, Dept Ingn Quim, UTEC, Jr Medrano Silva 165, Barranco 15011, Lima, Peru
[6] Univ Cartagena, Fac Ciencias Farmaceut, Grp Farmacol & Terapeut, Zaragocilla Cra 50 24120, Cartagena 130014, Colombia
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
bibliometric; material science; industry; 4; 0; Scopus; Web of Science; Biblioshiny; VOSviewer; CONTEXT; DECAY;
D O I
10.3390/chemengineering7010002
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Material science is a broad discipline focused on subjects such as metals, ceramics, polymers, electronics, and composite materials. Each of these fields covers areas associated with designing, synthesizing, and manufacturing, materials. These are tasks in which the use of technology may constitute paramount importance, reducing cost and time to develop new materials and substituting try-and-error standard procedures. This study aimed to analyze, quantify and map the scientific production of research on the fourth industrial revolution linked to material science studies in Scopus and Web of Science databases from 2017 to 2021. For this bibliometric analysis, the Biblioshiny software from RStudio was employed to categorize and evaluate the contribution of authors, countries, institutions, and journals. VOSviewer was used to visualize their collaboration networks. As a result, we found that artificial intelligence represents a hotspot technology used in material science, which has become usual in molecular simulations and manufacturing industries. Recent studies aim to provide possible avenues in the discovery and design of new high-entropy alloys as well as to detect and classify corrosion in the industrial sector. This bibliometric analysis releases an updated perspective on the implementations of technologies in material science as a possible guideline for future worldwide research.
引用
收藏
页数:19
相关论文
共 53 条
  • [1] Artificial Intelligence-Related Research Funding by the US National Science Foundation and the National Natural Science Foundation of China
    Abadi, Hamidreza Habibollahi Najaf
    He, Zhou
    Pecht, Michael
    [J]. IEEE ACCESS, 2020, 8 : 183448 - 183459
  • [2] Deep materials informatics: Applications of deep learning in materials science
    Agrawal, Ankit
    Choudhary, Alok
    [J]. MRS COMMUNICATIONS, 2019, 9 (03) : 779 - 792
  • [3] Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022
    Ali, Luqman
    Alnajjar, Fady
    Khan, Wasif
    Serhani, Mohamed Adel
    Al Jassmi, Hamad
    [J]. BUILDINGS, 2022, 12 (04)
  • [4] bibliometrix: An R-tool for comprehensive science mapping analysis
    Aria, Massimo
    Cuccurullo, Corrado
    [J]. JOURNAL OF INFORMETRICS, 2017, 11 (04) : 959 - 975
  • [5] Austin Tim, 2016, Materials Discovery, V3, P1, DOI 10.1016/j.md.2015.12.003
  • [6] Industry 4.0 technologies assessment: A sustainability perspective
    Bai, Chunguang
    Dallasega, Patrick
    Orzes, Guido
    Sarkis, Joseph
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2020, 229
  • [7] A review of research on the teaching and learning of thermodynamics at the university level
    Bain, Kinsey
    Moon, Alena
    Mack, Michael R.
    Towns, Marcy H.
    [J]. CHEMISTRY EDUCATION RESEARCH AND PRACTICE, 2014, 15 (03) : 320 - 335
  • [8] Sustainable and Dynamic Competitiveness towards Technological Leadership of Industry 4.0: Implications for East African Community
    Bongomin, Ocident
    Nganyi, Eric Oyondi
    Abswaidi, Mfanga Ramadhani
    Hitiyise, Emmanuel
    Tumusiime, Godias
    [J]. JOURNAL OF ENGINEERING, 2020, 2020
  • [9] A Bibliometric Analysis in Industry 4.0 and Advanced Manufacturing: What about the Sustainable Supply Chain?
    Borregan-Alvarado, Jon
    Alvarez-Meaza, Izaskun
    Cilleruelo-Carrasco, Ernesto
    Garechana-Anacabe, Gaizka
    [J]. SUSTAINABILITY, 2020, 12 (19)
  • [10] Artificial-Intelligence and Machine-Learning Technique for Corrosion Mapping
    Carpenter, Chris
    [J]. JPT, Journal of Petroleum Technology, 2022, 74 (01): : 99 - 102