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Systematic Review

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

1
Grupo de Investigación en Deterioro de Materiales, Transición Energética y Ciencia de Datos DANT3, Facultad de Ingenieria, Arquitectura y Urbanismo, Universidad Señor de Sipán, Chiclayo 14001, Peru
2
Semillero de Investigación en Corrosión de Metales, Energías Sostenibles y Análisis de Datos—COMD3S, Faculty of Enginering, Arquitecture and Urbanism, Universidad Señor de Sipán, Chiclayo 14001, Peru
3
Department of Chemical Engineering, Universidad de Cartagena, Sede Piedra de Bolívar, Avenida del Consulado 48-152, Cartagena 130001, Colombia
4
Grupo de Investigaciones en Corrosión, Escuela de Ingeniería Metalúrgica, Universidad Industrial de Santander, Parque Tecnológico Guatiguará, Piedecuesta 681011, Colombia
5
Departamento de Ingeniería Química, Universidad de Ingeniería y Tecnología—UTEC, Jr. Medrano Silva 165, Barranco, Lima 15011, Peru
6
Grupo de Farmacología y Terapéutica, Facultad de Ciencias Farmacéuticas, Universidad de Cartagena, Zaragocilla Cra. 50 #24120, Cartagena 130014, Colombia
*
Author to whom correspondence should be addressed.
ChemEngineering 2023, 7(1), 2; https://doi.org/10.3390/chemengineering7010002
Submission received: 3 October 2022 / Revised: 15 November 2022 / Accepted: 6 December 2022 / Published: 5 January 2023

Abstract

:
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.

1. Introduction

Material science is fundamental for discovering, designing, and casting the human world. It encompasses diverse disciplines such as chemistry, metallurgy, and solid-state physics, among others, to correlate materials’ properties with their composition and structure. These efforts serve as raw materials for engineers to develop applications in electronics, nuclear, construction, communications, food, and other industries. However, successful results are troublesome since try-and-error methodologies take time and resources. Hence, material science relies on computational techniques to increase design reliability and precision, opening space for a new industrial automatization era [1,2,3].
The fourth industrial revolution, also known as Industry 4.0, is composed of many technologies from which data mining [4] and artificial intelligence (AI) stands out in material science [5], which can be incorporated together [6]. Machine learning [7,8] and its deep learning branch [9,10] are the most used algorithms from AI. For these emerging tools to function, having data of quality is essential. Therefore, pretreatment steps are always required regardless of the data source; experimental, computational, or industrial [11]. Experimental data supply information about the chemical composition, material properties, testing conditions, processing parameters, etc. Computational data provide algorithm types, chemical details, computation constraints, etc. Industrial data cover property information, equipment applied, brand names, material names, and others. The stages necessary for introducing an AI model in industrial production include data management, model learning, model verification, model deployment, and cross-cutting aspects [12].
The application of technologies from industry 4.0 in material science is diverse, as shown in the following research works. Data mining has been used to classify martensite, pearlite, and bainite microstructures from their morphological parameters [13]. Machine learning algorithms have been introduced to predict stable lead-free hybrid organic-inorganic perovskites from unexplored perovskite data, identifying new stable compounds [14]. Deep learning -a preferred algorithm from machine learning- is gaining relevance in the design of photonic devices through deep neural networks, synthesizing multilayer structures based on the thickness of each layer as input parameters [15].
Bibliometrics has been found very useful to describe the impact and growth of a research field and determine its protagonists. It can be used with short or wide timeframes as observed in the work of Nandiyanto et al. [16], Vukić et al. [17], and Modak et al. [18] regarding chemical engineering issues. Likewise, the scientific community recommends using Scopus and WoS databases to carry out this type of review due to their known quality and flexibility to elaborate a robust query equation [19,20].
Few articles deal with the bibliometric analysis of industry 4.0 applied to material science. Advanced and smart manufacturing have been studied separately based on Scopus or Web of Science (WoS) databases, proving that industry 4.0 is exponentially increasing and just emerging, correspondingly [21,22]. Artificial neural network applications have been explored through Scopus, highlighting their importance in engineering fields [23]. Other studies have focused on quantifying the use of deep learning in structural crack detection [24] and AI in the textile industry [25] through WoS. These bibliometric studies have only covered certain areas of material science or industry 4.0 and have mainly employed only one database. This research aims to provide a wider and complete scientometric vision of industry 4.0 applied to material science. We took the recent 2017–2021 period, Scopus and WoS databases, covering several emerging topics, and utilized bibliometrix from RStudio for data mining. The following research questions were addressed:
  • 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?
Bibliometric research can lead to the development and discovery of trends in a field, helping the scientific community to identify new hotbeds of innovation based on a recent window of observation [26]. This bibliometric analysis provides an updated perspective of the implementations of technologies from industry 4.0 in material science as a scientific reference for subsequent research.

2. Materials and Methods

2.1. Study Design

We opted for a bibliometric analysis to numerically measure the scientific activity of industry 4.0 as it is applied to materials science. This decision was made based on the high number of scientific articles collected from Scopus and WoS between 2017 and 2021.

2.2. Data Source

The Scopus and Web of Science databases were selected due to their widespread reputation for hosting high-quality journals and research documents. Institutional access was required to download and corroborate the content of the study files.

2.3. Search Strategy

We introduced an extended list of keywords in both databases, covering material science and industry 4.0 topics (see Figure 1). In search of articles that are relevant to industry 4.0, the words used were the following: data science, industry 4.0, augmented reality, computer science, remote sensing, artificial intelligence, 3D scanning, data mining, data analytics, data handling, data processing, big data, data visualization, internet of things, and machine learning. The selected words to represent material science were: material, alloys, polymers, metals, nanomaterials, minerals, plastics, ceramics, catalyst, biomaterials, molecular, organic materials, inorganic materials, corrosion, material synthesis, and manufacturing. These keywords were obtained in a cyclical process in which, starting from the articles returned by the databases, more words were incorporated, covering the initially unforeseen topics. The established timeline covered data between 2017 and 2021, while the search was narrowed down to titles and keywords to increase the effectiveness of the search equation. Besides, only original articles were considered as the document type. Both web pages were consulted for the last time on 9 September 2022.

2.4. Bibliometric Analysis

Plots and tables combined the separately processed and analyzed data downloaded in BibTeX files from the Scopus and WoS databases. The Biblioshiny app from the RStudio cloud was used as a tool to obtain and organize both databases before manual manipulation. Biblioshiny offers data about the most productive countries, institutions, authors, research fields, and journals, as well as about keywords, h-index, impact factor, total citations, etc. [27]. Moreover, VOSviewer was included for data mining, mapping, and visualization of collaborative networks [28].

2.5. Limitations

The Scopus and WoS databases are not perfectly adapted to bibliometric analyses; therefore, they tend to return a certain amount of erroneous data that limits the conclusions to be drawn from them. In bibliometric studies, qualitative statements tend to be subjective since these analyses are essentially quantitative [29]. This type of review offers a short-term forecast of the area under investigation [30].

3. Results and Discussion

The findings delivered from each of the previously mentioned objectives will be presented in the following subsections based on the data taken from WoS and Scopus. Figure 1 shows that Scopus is the most used database to publish articles related to industry 4.0 in material science, more than doubling the number of documents hosted in WoS.

3.1. Trends in the Annual Production of Original Papers

Figure 2a shows that the introduction of technologies from industry 4.0 within material science areas slightly increased from 2017 to 2019; however, it showed sharp growth after this point. One of the technologies that has presented an important increase in recent years is machine learning; however, it is still a developing tool that requires a higher degree of fine-tuning before we can see its complete potential [31].
There has been rapid adoption of digital technologies during the COVID-19 pandemic, triggering the additional implementation of artificial intelligence in material science [32]. As seen in Figure 2a, the number of published papers pertaining to the assessed subjects has grown exponentially in the past five years.
The mean total citation per year reached a higher peak in 2018, after which it decreased similarly in both databases (see Figure 2b). The decay of said metric may respond to the time required by the researchers to identify newly published works, their novelty, their accessibility, and the spreadability of science, among other reasons [33,34,35].

3.2. Most Cited Authors and Their Collaborations

The Scopus and WoS databases highlight Zhang Yan as one of the most productive researchers in issues related to industry 4.0 applicated to materials (see Table 1). As a general observation, Wang J. and Liu J. produced a higher quantity of TC, whereas Zhang Yan delivered the largest number of scientific papers. In addition, as shown in Figure 3a, Zhang Yan is the researcher with the most collaborations (60) in Scopus, followed by Li J. (51) and Wang J. (48) (see Table A1 from Appendix A). Although Wen C., Xue D., and Su Y. are the authors with the most link strengths from WoS, with 8 collaborations each. As such, Zhang Yan can be considered the most influential author in the studied subjects. In his latest research, he has used machine learning for material design and microstructure evolution prediction [36,37]. It is worth noting that images from Figure 2 are not tailored to the data of Table 1 since these networks’ charts are focused on searching for collaborations—total link strength—and they depend on the minimum number of articles per author and on the decision of presenting the interconnection of nodes. In this case, the node size is proportional to the number of associations per author. The same logic applies to the following VOSviewer figures and their interpretations, along with the document.

3.3. Most Cited Research Articles

As shown in Table 2, the three most-cited papers were written by Zhong RY. (2017), Tao F. (2018), and Frank AG. (2019). The work of Zhong is a review only hosted in Scopus that counts the highest number of citations and is about intelligent manufacturing [38]. The paper written by Tao deals with the use of big data in product life cycle management, proposing a new method for its design, manufacture, and service driven by digital twins [39]. Frank surveyed 92 manufacturing companies to study the implementation of the internet of things, cloud services, big data, and analytics in smart manufacturing, smart products, smart supply chain, and smart working. This research work highlighted the need for named technologies in Smart manufacturing since they play a central role within companies [40]. Furthermore, 60% of the topics extracted from Table 2 are associated with manufacturing in the industrial sector. At the same time, machine learning (ML) is the preferred technology, followed by its deep learning (DL) branch and big data. As previously argued, ML and DL -artificial intelligence- have become usual techniques for the discovery and design of materials at a molecular level. Regarding the use of big data, the so-called 5 V model has been found to be essential for data management and data preservation in the material science context. This model considers the variability of unstructured data, volume of data in zettabytes, velocity in streaming data, noise removal veracity, and added value [41].

3.4. Journals That Host a Higher Number of Articles

Table 3 shows that the journals with more participation in material science linked to technologies from the fourth revolution are Computational Materials Science, IEEE Access, and Journal of Physical Chemistry Letters. Even though, this latter source isn’t part of the WoS’ top three. The cumulate average of research works hosted in these three journals is below 8%, therefore, there exists a large spectrum of journals (>92%) publishing articles regarding material science addressing technologies from industry 4.0.
On the other hand, by introducing Bradford’s law, it was feasible to classify sources into core areas, related areas, and non-relevant areas concerning the targeted field, as observed in Equation (1).
r 0 = 2 ln ( e γ Y )
where r 0 represents the number of journals that make up the core area, γ is the Euler’s constant (~0.577), and Y is the number of papers published in the journal with the most hosted documents [42]. In this case, since we this study involves two databases, Y 1 = 58   in Scopus and Y 2 = 45 in WoS. Thus, r 0 1 ( Scopus )   9 and r 0 2 ( WoS )   9 . As a result, only the source Applied Sciences-Basel from WoS is removed from the core collection, bearing in mind that ACS Applied Materials & Interfaces from Scopus is inside the WoS list of publications with the higher JCR impact factor. Also, it is noteworthy that Computational Materials Science is the preferred journal to publish articles around industry 4.0 connected with material sciences.

3.5. Most Productive Institutions and Their Collaborations

Considering an array of only interconnected nodes in VOSviewer, Scopus and WoS delivered the same result regarding the most productive institutions. The top 3 universities in Scopus publish more papers than those in WoS. Accordingly, the University of Science and Technology Beijing is positioned as the most contributive institution, followed by the University of California and Shanghai University (see Table 4). Meanwhile, from the collaborative viewpoint, the Chinese Academy of Sciences is positioned as the most collaborative institution with a total link strength of 58, as counted in Table A2 from Appendix B and as appreciated in Figure 4b. Other research studies have highlighted the implementation of technologies from industry 4.0 at the Chinese Institute of Computer Science [43].
In this regard, Scopus did not provide visual evidence regarding the leader institution, (see Figure 4a) but indicated partial partnerships among most universities. Table A2 shows that below the Chinese Academy of Sciences, its managed institution, the University of Chinese Academy of Sciences, hosts 22 fewer partnerships. The dominance of China and the United States is visible in Table 4. Nevertheless, Figure 3b suggests that new organizations, such as Curtin University from Australia, have been increasing their teamwork in the past few years.

3.6. Most Participative Funding Agencies

The studied databases were consistent in finding the National Natural Science Foundation of China (NSFC) and the National Science Foundation (NSF) from the United States as the principal funding agencies (see Table 5). Both institutions have been compared according to their influence on the development of artificial intelligence-associated research in the past decade [44]. The main findings suggest that from 2010 to 2019, the NSF supported more AI research than the NSFC, injecting 1.7 billion more dollars into research. Despite the greater volume of published works by the NSFC, reaching 15 thousand more papers than the NSF, it awarded less money. However, it is expected that by 2023 the NSFC will surpass the NSF in the quantity of money awarded. In the addition to these two mighty powers, organizations from Belgium and Germany are financing research in industry 4.0 to sustain the growth of the field in Europe countries.

3.7. Most Contributing Countries and Their Collaborations

Scopus and WoS coincide in attributing the leading countries in materials science studies developed under the perspective of industry 4.0. China ranks as the country that has successfully incorporated ideas, technology, and innovation from computers to the architecture of materials and additive manufacturing [45]. In spite of the higher number of citations received by the studies carried out by the United States rendering to Scopus (see Table 6). Two rungs below, countries like Japan, Germany, and the United Kingdom, appear with standardized scientific productions.
The esteemed position of China in this field is not a matter of luck since this country thoroughly planned today’s ubication by introducing the “Made in China 2025” plan, which was directed to catch up with industry 4.0 technologies. This plan was ten years ahead of planning that pursued to become the country a global manufacturing powerhouse [46]. However, despite Chinese expectations, only 57% of their companies are adequately prepared for Industry 4.0 technologies. This low average of prepared companies to receive these technologies in China is surpassed by the United States (71%) and Germany (68%) [47].
According to the databases explored, among the most collaborative countries are the United States, China, and United Kingdom (see Table A3 in Appendix C). The first two countries, however, are interchanged in the first places in Scopus and WoS. This perhaps demonstrates a preference by China to publish in WoS journals (see Figure 5) as a result of its policy to measure research excellence [48]. Otherwise, Figure 5 shows that India, Turkey, and the Czech Republic are countries that have increased their collaborations in recent years.
Keywords are loyal representations of scientific research works in articles, and their frequent implementations may reflect the hotspots of a particular study field. The word-cloud visualization of Scopus and WoS database in Biblioshiny (see Figure 6) allowed us to define the most relevant keyword introduced by authors in material science associated with industry 4.0 technologies.
We highlight smart manufacturing, additive manufacturing, and molecular dynamics as part of the material science keywords while machine learning, deep learning, and big data for industry 4.0. As previously mentioned most productive institutions may fluctuate, yet, more generally, machine learning and deep learning are the most prioritized technologies in material science studies (see Figure A1 in Appendix D). Machine learning and deep learning are recognized as the brains behind smart manufacturing. In this regard, both technologies are used for decision-making support systems, fault diagnosis, predictive analytics, advanced robotics, and scheduling [49]. Additive manufacturing, also known as 3D printing, is used to determine fabrication parameters, quality of the workpieces, and processing time. Furthermore, it is expected that additive manufacturing will achieve 5d-printing, through the implementation of time and artificial intelligence tools as the fourth and fifth dimensions, respectively [50]. In the case of molecular dynamics, artificial intelligence is employed to contribute to understanding materials’ properties by simulating the interaction of atoms and molecules. Even though some studies lead to considerable differences between accuracy and efficiency, machine learning and deep learning are still considered helpful tools to match efficiency with ac-curacy in molecular simulation [51].
On the other hand, Figure 7 shows possible new trends for technologies from the fourth industrial revolution based on the keywords extracted from both studied databases. While AI technologies continue to be tightly associated with new trends, it is notable that materials science fields, such as high entropy alloys and corrosion, are gaining traction in computer sciences. The predictive properties of high entropy alloys may allow for the design of new materials by selecting key-related features of alloys [52]. Additionally, the detection and classification of corrosive issues from images of industrial facilities have been successfully performed through AI [53]. We believe these applications belong to a new pathway of industry 4.0 as applied to material science and serve as a guide for future routes to be explored by scientists.

4. Conclusions

This bibliographic review brings us closer to the recent growing interest shown by institutions, journals, researchers, countries, and funding agencies in the study of material science linked to the emerging technological tools provided by industry 4.0. The main conclusions delivered by responding to each one of the settled research questions are the following:
  • 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.
Even though AI is the research hotspot technology in material science studies, and it has become commonly used in molecular simulations and manufacturing issues, opportunities stills exist to discover and design new high entropy alloys and corrosion detection. In general terms, this bibliometric analysis offers an updated viewpoint regarding material science for developing subsequent research and generating consciousness about the impact of introducing new technologies in the promotion, discovery, design, management, and operation of materials used by companies.

Author Contributions

A.A.-M. and J.O.-A. carried out the literature review, data analysis, and discussion of results, and drafted the manuscript. D.C.P.Q. and A.A.-A. helped with extracting data and manipulating the used software. A.A.-M. communicates with the editor. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The used databases can be freely downloaded from the Web of Science and Scopus websites.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. The top 5 most collaborative authors in industry 4.0 applied to material science from 2017 to 2021.
Table A1. The top 5 most collaborative authors in industry 4.0 applied to material science from 2017 to 2021.
ScopusWoS
RankAuthorNo. of PaperT. Link StrengthAuthorNo. of PaperT. Link Strength
1stZhang Y4460Wen C58
2ndLi J3251Su Y58
3rdWang J3748Xue D68
4thLiu Y3844Liang H55
5thLi Y3342Qiao Z55

Appendix B

Table A2. The top 5 most collaborative institutions in industry 4.0 applied to material science from 2017 to 2021.
Table A2. The top 5 most collaborative institutions in industry 4.0 applied to material science from 2017 to 2021.
ScopusWoS
RankInstitutionNo. of PaperT. Link StrengthInstitutionNo. of PaperT. Link Strength
1stTechnical University of Berlin814Chinese academy of sciences2758
2ndUniversity of Zilina1012University of Chinese academy of sciences1432
3rdCadi Ayyad University38Northwestern polytechnic university1232
4thThe institute of smart big data analytics58University of science and technology Beijing1831
5thUniversity of Chinese academy of sciences78Georgia institute of technology826

Appendix C

Table A3. The top 5 countries in industry 4.0 applied to material science from 2017 to 2021.
Table A3. The top 5 countries in industry 4.0 applied to material science from 2017 to 2021.
ScopusWoS
RankCountryTotal, Link StrengthCountryTotal, Link Strength
1stUnited States252China154
2ndChina176United States143
3rdUnited Kingdom63United Kingdom70
4thGermany56Germany45
5thIndia33Australia31

Appendix D

Figure A1. Relationship between institutions, countries and most used keywords in industry 4.0 applied to material science from 2017 to 2021 from (a) Scopus and (b) WoS.
Figure A1. Relationship between institutions, countries and most used keywords in industry 4.0 applied to material science from 2017 to 2021 from (a) Scopus and (b) WoS.
Chemengineering 07 00002 g0a1aChemengineering 07 00002 g0a1b

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Figure 1. Flowchart of used bibliometric methodology.
Figure 1. Flowchart of used bibliometric methodology.
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Figure 2. The year-wise of publication (a) and total citation (TC) (b) from WoS and Scopus from 2017 to 2021.
Figure 2. The year-wise of publication (a) and total citation (TC) (b) from WoS and Scopus from 2017 to 2021.
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Figure 3. The most collaborative authors in material science combined with industry 4.0 from 2017 to 2021 in (a) Scopus and (b) WoS, considering a minimum of 12 and 5 documents in VOSviewer, respectively. Each node’s size is proportional to the number of associations per author.
Figure 3. The most collaborative authors in material science combined with industry 4.0 from 2017 to 2021 in (a) Scopus and (b) WoS, considering a minimum of 12 and 5 documents in VOSviewer, respectively. Each node’s size is proportional to the number of associations per author.
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Figure 4. The most collaborative institutions in material science combined with industry 4.0 from 2017 to 2021 in (a) Scopus and (b) WoS, considering a minimum of 1 document in VOSviewer. Each node’s size is proportional to the number of associations per institution.
Figure 4. The most collaborative institutions in material science combined with industry 4.0 from 2017 to 2021 in (a) Scopus and (b) WoS, considering a minimum of 1 document in VOSviewer. Each node’s size is proportional to the number of associations per institution.
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Figure 5. The most collaborative countries in material science joined with industry 4.0 from 2017 to 2021 in (a) Scopus and (b) WoS, considering a minimum of seven documents in VOSviewer. Each node’s size is proportional to the number of associations per country.
Figure 5. The most collaborative countries in material science joined with industry 4.0 from 2017 to 2021 in (a) Scopus and (b) WoS, considering a minimum of seven documents in VOSviewer. Each node’s size is proportional to the number of associations per country.
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Figure 6. Word clouds of authors’ keywords in industry 4.0 applied to material science from 2017 to 2021 from (a) Scopus and (b) WoS.
Figure 6. Word clouds of authors’ keywords in industry 4.0 applied to material science from 2017 to 2021 from (a) Scopus and (b) WoS.
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Figure 7. Keywords most used by authors in material science studies combined with industry 4.0 from 2017 to 2021 in (a) Scopus and (b) WoS, considering a minimum of eight and five occurrences in VOSviewer, respectively. Each node’s size is proportional to the keywords’ frequency of use.
Figure 7. Keywords most used by authors in material science studies combined with industry 4.0 from 2017 to 2021 in (a) Scopus and (b) WoS, considering a minimum of eight and five occurrences in VOSviewer, respectively. Each node’s size is proportional to the keywords’ frequency of use.
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Table 1. The top 10 most-cited authors in material science coupled with industry 4.0 from 2017 to 2021.
Table 1. The top 10 most-cited authors in material science coupled with industry 4.0 from 2017 to 2021.
ScopusWoS
RankAuthorh-IndexTCNo. of PaperAuthorh-IndexTCNo. of Paper
1stZhang Y16145244Zhang Y1288623
2ndLiu Y16160639Li Y932121
3rdWang J18245337Li H931420
4thWang Y1569033Zhang Z823019
5thLi J1479732Liu Y889718
6thLi Y1462431Li J815917
7thZhang Z1134830Wang J1195615
8thZhang J1286228Wang Y821614
9thLi X1147027Zhang L675514
10thLi H1136425Zhang X918114
Table 2. The top 10 most-cited articles in industry 4.0 applied to material science from 2017 to 2021 [14,38,39,40].
Table 2. The top 10 most-cited articles in industry 4.0 applied to material science from 2017 to 2021 [14,38,39,40].
Autor, YearDocument Title and Journal NameJournal NameTC ScopusTC WoS
Zhong RY, 2017Intelligent Manufacturing in the Context of Industry 4.0: A ReviewEngineering1207N/A
Tao F, 2018Digital twin-driven product design, manufacturing, and service with big dataInt J Adv Manuf Technol1136822
Frank AG, 2019Industry 4.0 technologies: Implementation patterns in manufacturing companiesInt J Prod Econ795633
Wang J, 2018Deep learning for smart manufacturing: Methods and applicationsJ Manuf Syst747583
Wu Z, 2018MoleculeNet: a benchmark for molecular machine learningChem Sci637N/A
Qi Q, 2018Digital twin and big data towards smart manufacturing and industry 4.0: 360-degree comparisonIEEE Access595434
Schütt KT, 2018SchNet—A deep learning architecture for molecules and materialsJ Chem Phys579N/A
Ghobakhloo M, 2018The future of manufacturing industry: a strategic roadmap toward Industry 4.0J Manuf Technol Manage503N/A
Liu Y, 2017Materials discovery and design using machine learningJ MateriomicsNA469
Chmiela S, 2017Machine learning of accurate energy-conserving molecular force fieldsSci Adv461N/A
Table 3. Top 10 most articles hosted by the journal in industry 4.0 applied to material science from 2017 to 2021.
Table 3. Top 10 most articles hosted by the journal in industry 4.0 applied to material science from 2017 to 2021.
ScopusWoS
RankJournal NameNo. of Papers (%)N = 2157Impact Factor
SJR (2021)
Journal NameNo. of Papers (%)
N = 937
Impact Factor
JCR (2021)
1stComputational Materials Science58 (2.69)0.777Computational Materials Science45 (4.80)3.572
2ndIEEE Access38 (1.76)0.927IEEE Access26 (2.77)3.476
3rdJournal of Physical Chemistry Letters32 (1.48)2.009International Journal of Advanced Manufacturing Technology19 (2.03)NA
4thJournal of Chemical Information and Modeling31 (1.44)1.223ACS Applied Materials & Interfaces15 (1.60)10.383
5thJournal of Physical Chemistry C30 (1.39)1.103Journal of Intelligent Manufacturing15 (1.60)7.136
6thNPJ Computational Materials29 (1.34)2.967Advanced Theory and Simulations14 (1.49)4.105
7thSustainability (Switzerland)26 (1.21)0.664Journal of Manufacturing Systems13 (1.39)9.498
8thChemistry of Materials24 (1.11)2.93Materials & Design13 (1.39)9.417
9thJournal of Chemical Physics24 (1.11)1.103Acta Materialia11 (1.17)9.209
10thACS Applied Materials & Interfaces23 (1.07)2.143Applied Sciences-Basel11 (1.17)2.838
Table 4. The top 10 most-productive institutions in industry 4.0 applied to material science from 2017 to 2021.
Table 4. The top 10 most-productive institutions in industry 4.0 applied to material science from 2017 to 2021.
ScopusWoS
RankAffiliationsCountryNo. of PaperAffiliationsCountryNo. of Paper
1stUniversity of Science and Technology BeijingChina59University of Science and Technology BeijingChina55
2ndUniversity of CaliforniaUnited States47Shanghai UniversityChina42
3rdShanghai UniversityChina42University of Chinese Academy of SciencesChina28
4thMassachusetts Institute of TechnologyUnited States38Nanyang Technological UniversitySingapore26
5thZhejiang UniversityChina33Chongqing UniversityChina25
6thShanghai Jiao Tong UniversityChina30Beihang UniversityChina24
7thUniversity of Chinese Academy of SciencesChina28Northwestern Polytech UniversityChina24
8thChongqing UniversityChina24University of IllinoisUnited States24
9thSouth China University of TechnologyChina24Guangzhou UniversityChina22
10thTsinghua UniversityChina24Zhejiang UniversityChina22
Table 5. The top 10 most-participative funding agencies in industry 4.0 applied to material science from 2017 to 2021.
Table 5. The top 10 most-participative funding agencies in industry 4.0 applied to material science from 2017 to 2021.
ScopusWoS
RankAffiliationsCountryNo. of PaperAffiliationsCountryNo. of Paper
1stNational Natural Science Foundation of ChinaChina350National Natural Science Foundation of ChinaChina184
2ndNational Science FoundationUnited States182National Science FoundationUnited States68
3rdU.S. Department of EnergyUnited States122National Key Research and Development Program of ChinaChina44
4thNational Key Research and Development Program of ChinaChina94Fundamental Research Funds for The Central UniversitiesChina35
5thOffice of ScienceUnited States79U.S. Department of EnergyUnited States33
6thFundamental Research Funds for the Central UniversitiesChina72Ministry of Education Culture Sports Science and Technology Japan25
7thJapan Society for the Promotion of ScienceJapan52European CommissionBelgium23
8thMinistry of Science and Technology of the People’s Republic of ChinaChina50Japan Society for the Promotion of ScienceJapan22
9thHorizon 2020 Framework ProgrammeBelgium49German Research FoundationGermany18
10thBasic Energy SciencesUnited States48Grants-in-Aid for Scientific ResearchJapan16
Table 6. The top 10 countries in industry 4.0 applied to material science from 2017 to 2021.
Table 6. The top 10 countries in industry 4.0 applied to material science from 2017 to 2021.
ScopusWos
RankCountryFrequencyTotal CitationsCountryFrequencyTotal Citations
1stChina145311514China12267238
2ndUnited States131812957United States7844661
3rdJapan3111310Japan210698
4thGermany2461448Germany175684
5thUnited Kingdoms2291673India162325
6thIndia227795South Korea158413
7thSouth Korea2101158United Kingdoms149998
8thCanada107545Australia92393
9thAustralia106524Spain87208
10thSpain103462Singapore76547
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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

AMA Style

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 Style

Alviz-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

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