3.4. Author Group Analysis
To gain a deeper understanding of the fundamental structure of this field, it is crucial to identify its core authors.
Table 3 presents the top six author groups ranked by their publication output. The statistical analysis was conducted in accordance with the Price formula
, where M represents the number of publications by core authors and
represents the number of publications by the author with the highest output in that year. The selection criteria must be greater than or equal to the number of publications by core authors [
15,
16]. In the WOS database, there were 71 core authors who had published more than five articles. Only one author, Li Yi’s group from the University of Manchester, had published more than 20 articles. Li Yi is considered a prolific author, with a total of 35 publications.
In the WOS database, there are 54 distinct clusters of core authors based on publication volume (see
Figure 5), and the number of papers published by a maximum of one author per cluster was selected as the standard. Li Yi’s group (35 articles), Wang Zhong Lin’s group (23 articles), Qu Lijun’s group (20 articles), Chen Jun’s group (18 articles), and Zheng Zijian’s group (15 articles) each form a cluster. Each cluster contains more than two authors. There are nine closely cooperative teams, several of which have long-term collaborations with high publication volumes. This forms a stable cross-team communication and cooperation network. The most prominent team is Li Yi’s group at the University of Manchester, whose collaborative network is the largest in this field. Their research focuses on characterizing wearable tensile strain sensors [
17].
3.6. Citations Analysis
Citation analysis is an important indicator in bibliometric studies [
18,
19]. A total of 254 co-cited references were visualized using CiteSpace 6.2.R4, with the time slice set as one year and the time span spanning from 2012 to 2022.
Table 5 showed the top ten most cited papers, including four review studies (Stoppa M et al., 2014 [
20]; Zeng W et al., 2014 [
21]; Heo JS et al., 2018 [
22]; and Amjadi M, 2016 [
23]) and six experimental studies (Dong K et al., 2020 [
24]; Weng W et al., 2016 [
25]; Shi JD et al., 2020 [
26]; Pu X et al., 2016 [
27]; Lee J et al., 2015 [
28]; Chen J et al., 2016 [
29]), which focused on the electronic components and multi-functions of smart textiles. The most-cited papers were written by Stoppa M’s group, who work at the University of Istituto Italiano di Tecnologia (IIT), reflecting their great influence in the field. Citation bursts refer to references that caught the attention of scholars in a specific field at a specific time interval and whose analysis can be used to observe the evolution of a field of knowledge and to predict frontier trends. In
Table 6, the timeline is shown in a circle, and the burst time interval is shown in a solid black circle, indicating the start year, end year, and duration of the burst. Of these burst citations, the shortest burst duration for intelligent textiles and garments was one year, and the longest was four years. Notably, 40% of the citation bursts ended close to 2019, focusing on advances in flexible sensitive strain sensors and supercapacitors (Amjadi M et al., 2016 [
23]; Wen Z et al., 2016 [
30]; Pu X et al., 2016 [
27]; Ren JS et al., 2017 [
31]; Kou L et al., 2014 [
32]). Additionally, 15% of the citation bursts ending in 2022 or later focused on smart textiles that integrate microelectronic systems and functional textiles (Hsu PC et al., 2016 [
33]; Zhao ZZ et al., 2016 [
34]; Shi JD et al., 2020 [
26]), suggesting that these research topics have been receiving attention in recent years and are expected to be a focus of research in the future.
3.7. Keywords Analysis
This paper used VOSviewer 1.6.19 to visualize keywords in the literature and employed a keyword co-occurrence analysis to explore research hotspots in smart textiles and apparel. To gain a deeper understanding of this field, we constructed visual maps for 2398 English literature, as shown in
Figure 6.
The keywords were analyzed based on literature indexed in the Web of Science database. The most frequently occurring terms included smart textiles, fibers, performance, sensors, composites, textile design, fabrication techniques, and nanocomposites. After excluding self-referential terms, the frequently occurring keywords included fibers, performance, sensors, composites, design, fabrication, and nanocomposites. Based on these identified keywords, it is evident that research on smart textiles and clothing can be classified into two distinct directions. The first direction pertains to material development, which includes smart fiber materials [
49,
50] such as composite materials, nanocomposites, phase change fibers, and shape memory fibers. The chemical fibers can be purified and deodorized by adding nano-level ZnO, SiO
2, or other chemicals. Adding nanometer-sized ZnO to polyester fiber can increase the material’s anti-ultraviolet and anti-bacterial abilities. Adding nano-sized metal particles to the chemical fibers can enhance the antistatic ability of the material. Adding nanoscale silver ions to the chemical fibers can enhance the material’s own bactericidal ability. In addition, adding carbon black nanoparticles to rubber materials can also greatly improve the strength and anti-wear properties of rubber materials, thereby improving their service life. The second aspect concerns garment design, which involves dividing smart garment design into areas such as garment structure and fabric elasticity to create functional garments that address specific issues [
51].
A keyword clustering analysis facilitates the identification of relevant studies in a given field. A visual clustering analysis of these keywords was conducted using the LLR test algorithm within the CiteSpace 6.2.R4 software. We identified the key research areas in intelligent textiles and clothing from 2012 to 2021, and their clustering is illustrated in
Figure 7.
The clustering module (Q) for the WOS literature keywords is 0.768, with an average contour value (S) of 0.906, as depicted in
Figure 7. The top nine keywords with the highest frequency are “wearable electronics”, “smart textiles”, “flexible antennas”, “energy storage”, “textile actuators”, “mechanical properties”, “asymmetric supercapacitors”, “carbon nanotubes,” and “fiber extrusion”. It can be seen that the English literature also focuses on smart wearable products and smart textiles. English literature places more emphasis on mechanical properties, capacitors, and other electronic devices in smart textiles and garments. This indicates that the interaction design between electronic devices and clothing is a research hotspot.
By importing the data into Gephi 0.9.7 and utilizing Scimago Graphica software for timeline graphical analysis of literature keywords, we can correlate and analyze different clusters over time, illustrating the development and coherence of research content within each cluster. This approach clearly demonstrates trends in intelligent textile and clothing research.
A time zone map provides a view that represents the evolution of knowledge in the time dimension, which can intuitively show the changes and mutual influences of research hotspots [
52]. In the time zone diagram of WOS keywords, as depicted in
Figure 8, the literature clustering of smart textiles, carbon nanotubes, sensors, and wearable devices continue to be the present throughout the time span and have good time continuity. The pivotal node of smart textiles emerged in 2012. The most frequently cited literature on this topic within the experimental database [
53] reveals that researchers have developed a flexible and stretchable electronic circuit technology that integrates electronic systems into elastomeric materials to produce complex functional, stretchable, and flexible electronic modules. The primary focus in 2013 was on biological materials. Since 2015, yarn supercapacitors have become a prominent topic in the field and are expected to remain so until 2022. In summary, research on smart textiles and carbon nanotubes began in 2012. Carbon nanotubes can optimize the cooperative loss mechanism of multiple components and the absorbing performance by combining with magnetic metals and metal compounds. This is an effective ways to achieve thinness, a light weight, a wide frequency band, and strong absorption of the absorbing materials. For example, using cellulose fibers as raw materials can realize the preparation of carbon nanotube absorbing materials. Using carbon nanotubes and metal materials as functional particles, textile absorbing materials can be prepared through electrospinning and finishing [
54]. The timeline of smart materials research shows that in 2013, there was a notable focus on this area. Starting in 2014, there has been a focus on wearable electronics and energy storage within the realm of smart textile and clothing research. The research focus in 2015 was mainly on posture pressure, while from 2016 to 2018, the emphasis shifted towards wearable strain sensors and 3D printing, both of which are within the scope of intelligent textile clothing research. From 2019 to 2022, the main directions for intelligent textile clothing research will include shape memory and electronic skin development, as well as wearable strain sensing. The term “emergent keyword” refers to a word that experiences a sudden increase in frequency within a specific time period, with the growth rate of this word intensifying. This intensity can serve as an indicator of research hotspots and trends during the aforementioned period. By utilizing CiteSpace 6.2.R4 software to track emerging keywords in the literature from the WOS, we can gain insights into the evolutionary dynamics of research hotspots in intelligent textiles and clothing, ultimately enabling us to predict future development trends.
Figure 9 displays the emerging foreign research terms in the field of smart textiles and clothing from 2012 to 2022. By conducting a keyword emergence analysis of the WOS literature, we identified a total of 15 keywords with the highest emergence intensity. These can be roughly divided into two phases based on time: (1) From 2012 to 2018, fifteen emerging research hotspots were identified, including “circuit”, “smart fabric”, “yarn supercapacitor”, and “flexible supercapacitor”. Among these hotspots, the research on “yarn” has been continuously pursued for three to four years. Currently, foreign scholars are focusing their attention on smart fabrics and electronic components. Smart textile garments use yarn supercapacitors as energy storage devices. Large, high-stretch yarn electrodes are manufactured using CNI impregnation and PPy electrodeposition processes [
55]. Flexible supercapacitors have unique advantages in terms of flexibility, shape, and weight due to the development of carbon-based materials, composite materials, and flexible micro-supercapacitors [
56,
57]. In addition, research on smart fabrics has make a significant contribution to the development of smart clothing. For example, superhydrophobic-coated fabrics have facilitated the creation of smart oil and water separators, microfluidic valves, and chip experimental devices [
58]. (2) In the years 2019–2022, foreign scholars primarily focused on the development of pressure sensors in wearable devices and conductive textile research. For instance, thin-film flexible wireless pressure sensors can provide a wireless monitoring platform [
59]. Conductive textiles, including graphene-based textiles, offer technical advantages in wearable products such as improved conductivity, ultra-flexibility, and machine washability [
60]. The current focus of scholarly research is on wearable devices, which require a multidisciplinary approach to information collection, processing, storage, battery technology, intelligent operating systems, and human–computer interaction design. The integration of data processing, software, and haptic technologies enables the achievement of specific intelligent functions [
61,
62].