Next Article in Journal
American Sign Language Recognition and Translation Using Perception Neuron Wearable Inertial Motion Capture System
Previous Article in Journal
Durability Assessment of Bonded Piezoelectric Wafer Active Sensors for Aircraft Health Monitoring Applications
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 24
Issue 2
10.3390/s24020449
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Triboelectric Contact Localization Electronics: A Systematic Review

by
Wei Xu
1,
Qingying Ren
1,
Jinze Li
2,
Jie Xu
2,
Gang Bai
2,
Chen Zhu
2 and
Wei Li
1,2,*
1
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2
College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(2), 449; https://doi.org/10.3390/s24020449
Submission received: 10 November 2023 / Revised: 15 December 2023 / Accepted: 19 December 2023 / Published: 11 January 2024
(This article belongs to the Section Electronic Sensors)
Browse Figures
Versions Notes

Abstract

:
The growing demand from the extended reality and wearable electronics market has led to an increased focus on the development of flexible human-machine interfaces (HMI). These interfaces require efficient user input acquisition modules that can realize touch operation, handwriting input, and motion sensing functions. In this paper, we present a systematic review of triboelectric-based contact localization electronics (TCLE) which play a crucial role in enabling the lightweight and long-endurance designs of flexible HMI. We begin by summarizing the mainstream working principles utilized in the design of TCLE, highlighting their respective strengths and weaknesses. Additionally, we discuss the implementation methods of TCLE in realizing advanced functions such as sliding motion detection, handwriting trajectory detection, and artificial intelligence-based user recognition. Furthermore, we review recent works on the applications of TCLE in HMI devices, which provide valuable insights for guiding the design of application scene-specified TCLE devices. Overall, this review aims to contribute to the advancement and understanding of TCLE, facilitating the development of next-generation HMI for various applications.

1. Introduction

The rising consumer demand for enriched and immersive experiences in wearable devices, coupled with a burgeoning need across diverse industries such as gaming, healthcare, and education, has spurred a surge in research devoted to extended reality (XR) technologies [1] and wearable electronics [2,3,4]. The objectives of XR technologies are to deliver multiple sensations, including vision, hearing, touch, temperature, etc., to the user without perception distortion and collect the user’s multi-modal input via the human-machine interface (HMI) [5]. The seamless interaction and immersive experiences of the XR system heavily rely on the low latency and high accuracy recording of the user’s gesture, body movement, eye movement, voice, and facial expression [6,7]. The current challenges lie in seamlessly integrating with non-flat surfaces, conforming to the contours of the human body, and achieving a compact and space-efficient design. These challenges necessitate the development of flexible HMI [8]. One of the primary objectives of the intensively studied flexible HMI devices such as touch panels [9], electronic skin (e-skin) [10,11], and the Internet of Things (IoT) [12,13] is to achieve accurate spatial tracking to implement functions like contact detection [14], body movement detection [15,16], and handwriting character recognition [17]. Therefore, contact localization electronics that are crucial in obtaining spatial information have undergone rapid development.
Following the pioneering work of Wang et al. in 2012 [18], who introduced the triboelectric nanogenerator (TENG), the progressive advancements in TENG have demonstrated tremendous potential for application in high-entropy energy collection [19,20]. In addition to energy harvesting, triboelectric sensors also have demonstrated advantages such as low cost, easy fabrication, and a wide range of material options [21,22,23]. These advantages benefit the research on triboelectric contact localization electronics (TCLE). Wang et al. pioneered the construction of TCLEs by developing a 6 × 6 triboelectric sensor matrix for pressure mapping [24] and a 4 × 4 triboelectric sensor matrix for finger touch positioning [25] in 2013. Building on this foundation, subsequent research has introduced TCLEs utilizing rubber [26], plastic [27], and fiber [28] tribo-surfaces, thereby extending their application to various fields such as touch panels, e-skin, smart fabrics, etc. [29,30,31,32]. Furthermore, TCLE utilizes contact-induced triboelectric output for contact localization. Therefore, the triboelectric approach has the potential to reduce energy consumption and battery volume in HMI devices, allowing for the development of lightweight and long-endurance designs [33,34].
The key challenge of TCLE lies in improving the resolution while simultaneously addressing the issues of decreased accuracy and an increased number of signal channels. To overcome this challenge, novel working principles such as sensor matrix [35], parallel line array [36], and analog localization [37], and signal processing methods such as deep-learning signal processing [38] have been proposed. Additionally, as shown in Figure 1, TCLE has expanded its functionality beyond contact positioning to include sliding detection, input trajectory detection, and handwritten character recognition, stimulating its applications in diverse HMI devices [39,40,41]. The current reviews on triboelectric tactile sensors provide a thorough discussion of the theory, structure, and material aspects of the tactile sensor unit [42,43,44]. However, these reviews predominantly consider TCLE as an application example of the tactile sensor unit array, lacking in-depth analysis of the contact localization principle and design considerations. Therefore, here, we present a systematic review of TCLE with an arrangement as follows: Section 2 discusses the development path of the working principles of TCLE; Section 3 reviews the design considerations and achievable functions of TCLE; Section 4 presents the various application scenarios that have been developed in HMI devices; and Section 5 provides perspectives on future research of TCLE.

2. Working Mechanisms

2.1. Triboelectric Contact Localization

TENG operates on the principle of contact electrification, which involves the transfer of tribo-charges at the contact interface and subsequent electrostatic induction to generate charge flow within the external circuit [45]. The four basic working modes of TENG are shown in Figure 2a [46]. The relative displacement of the opposing tribo-charges on the surfaces of two tribo-materials results in a change in electrical potential at the electrode, subsequently generating current in the connected load.
The typical application scenario of TCLE involves locating the tapping or sliding motions of a moving object, such as a finger. Therefore, the single-electrode mode is highly suitable for TCLE as it eliminates the need to attach an electrode to the moving object. Figure 2b illustrates a typical working cycle of single-electrode mode TENG for tap detection. During a tap motion, a contact-separation process occurs. When two tribo-materials come into contact, the tribo-charges on the TCLE surface become screened, resulting in the transfer of charges from the electrode to the ground. Upon separation, the screening effect diminishes, leading to a reversed current in the connected load. Similarly, during a slide motion, as the moving object overlaps with the electrode position on the TCLE, the tribo-charges on the surface of the moving object screen the tribo-charges on the corresponding surface of the TCLE. This screening effect alters the potential distribution between the electrode and the ground, which can be detected as a signal indicating the slide motion. The V-Q-x governing equation (Equation (1)) proposed by Niu et al. [47]—where V is the voltage difference between the electrode and the ground, C(x) is the equivalent capacitance between the electrode and the ground, Q is the transferred charge between the electrode and the ground, and VOC(x) represents the contribution of polarized triboelectric charges to V—provides a mathematical understanding of the working mechanism of the single-electrode mode TENG.
V = − 1 C ( x ) Q + V O C ( x )
As illustrated in Figure 2, VOC(x) increases with the separation distance x between the tribo-materials, initiating the charge transfer process. This process reconstructs potential balance, generating current pulses upon touch and sliding motion. It is also evident that the location where the tap or slide motion occurs on the TCLE corresponds to the electrode that undergoes a potential change relative to the ground. This fundamental understanding serves as the basis for the design of TCLE.
The high voltage output characteristic of TENG [48] makes TCLE superior to signal strength. By employing appropriate tribo-material engineering, it is possible to achieve peak voltage outputs exceeding 50 V even with subtle contact pressure [49] (0.5 kPa, typical pressure value of finger touch [50]). However, the process of contact-transferred charge accumulation at the contact surface necessitates multiple contacts (typically ranging from a few to dozens) for the tribo-charge density to reach saturation [51,52]. At the unsaturation state, the tribo-signal exhibits a greater variance compared to the saturation state. Additionally, the dissipation of surface tribo-charge [53,54], particularly during long contact time intervals, leads to a change in the surface tribo-charge density and, consequently, affects the output triboelectric signal. Therefore, one of the major challenges for TCLE is to enhance signal consistency, which in turn reduces signal processing complexity and improves detection accuracy. Alternatively, a novel working principle or device design is anticipated to enable precise contact localization, irrespective of the inconsistent triboelectric signal strength.

2.2. Working Principles of TCLE

2.2.1. Sensor Matrix

Sensor matrix, parallel line array, and analog positioning design strategies have been successively proposed and have emerged as the primary approaches for TCLE. The sensor matrix design strategy, characterized by its simplicity in working principle, was the initial method employed in TCLE design [24]. Typically, triboelectric sensor units are uniformly distributed across the detection plane. One or more sensor units, decided by the sensor density and the contact area, will be triggered to output a triboelectric signal when contact occurs. By analyzing the coordinates of the triggered sensor units, the precise contact location can be directly determined. As shown in Figure 3a, Guo et al. proposed an 8 × 8 triboelectric sensor matrix for tactile pattern recognition [55]. The sensor unit, consisting of a silicon rubber triboelectric layer and a laser-induced graphene bottom electrode, operates in the single-electrode mode of TENG. Real-time touch positioning can be achieved by mapping the output voltage of the sensor units. The arrangement of sensor units can be tailored to suit diverse application scenes. For instance, Qu et al. [56] strategically positioned three sensor units, employing eutectic gallium–indium liquid metal electrodes and silicone tribo-material, in a triangular configuration on the fingertip. They established a direct correlation between touch positions on the finger and the triboelectric output differences in the three sensor units. This approach successfully achieved precise contact localization among five detection pixels on the fingertip. Moreover, the sensor matrix can be easily assembled using individual sensor units, allowing for a straightforward and convenient device design. Building upon the foundation of a sensor unit characterized by ultrahigh stretchability (1160%) and transparency (96.2% for visible light), which utilized elastomer tribo-material and ionic hydrogel electrodes, Pu et al. [57] introduced an artificial skin with a 3 × 3 sensor matrix. This innovative design enables both single- and multi-contact point localization.
Despite the convenient and flexible device design capability, a notable challenge associated with the sensor matrix strategy is that each sensor unit requires an individual signal line, introducing complexity to the signal processing aspect. Moreover, a substantial number of signal channel poses difficulties in the design of interconnection topology. In Figure 3a, it can be observed that the signal line needs to be routed close to other sensor units, potentially leading to unintended triggering of nearby sensors when the contact area covers the route of the signal line. This issue becomes more pronounced in high localization resolution scenarios.

2.2.2. Parallel Line Array

The parallel line array strategy, similar to the commonly used touch positioning method in commercial touch screens, provides a solution to the aforementioned challenges. Two mutually perpendicular arrays of parallel electrodes are utilized, forming a 2D coordinates system at the cross points of the arrays. When sliding or tapping occurs at these cross points, triboelectric signals are generated in the corresponding electrodes of the two arrays. The wiring for the parallel line array strategy can be conveniently routed on the ends of the electrode, eliminating the need for wiring within the detection area. Moreover, the geometric design of the electrode offers flexibility, allowing for various configurations such as using parallel serpentine lines to enhance stretchability [60]. However, the fundamental principle of contact localization remains unchanged. The work of Pratap et al. [61] exemplifies the effective utilization of the parallel line array strategy. A self-powered keypad was proposed that utilizes a 3 + 3 strip electrode array to locate finger touch within 9 pixels. Nanostructured polydimethylsiloxane was employed to enhance the tribo-output from human skin contact, resulting in a peak voltage signal of ~25 V. As shown in Figure 3b, Lin et al. reported an interface-independent contact localization panel with a 5 + 5 parallel electrode array [58]. A novel structure of two face-to-face TENGs at the internal interlayer was proposed to overcome the material limitation of the top protective layer. Triboelectric signals in the top and bottom electrode arrays determine the contact point within the 5 × 5 detection pixel array.
The double electrode layer structure design of the parallel line array strategy inevitably increases difficulties in device preparation. Nevertheless, to construct a TCLE with M × N detection pixels using the parallel line array strategy, only M + N signal channels are required. This highlights the advantages of the parallel line array strategy, such as a significantly reduced signal channel complexity and simplified wiring, underscoring its substantial potential for use in TCLE.

2.2.3. Analog Localization

Researchers continue to make progress in exploring novel design strategies, further simplifying device design and improving contact localization resolution. The analog positioning strategy was first proposed by Shi et al. for a self-powered smart skin [37] (Figure 3c). A voltage ratio, specifically the electrostatic induction voltage ratios of opposite electrodes positioned on the four sides of the detecting plane, was established to correspond to contact positions. This allows the determination of the contact position by measuring the voltage ratios on two pairs of electrodes and referencing a ratio-position look-up table. Consequently, only four signal channels are required to achieve touch positioning with an average resolution of 1.9 mm. Building on this approach, Zhang et al. proposed a GPS-inspired stretchable electronic skin to further reduce the number of signal channels to 3 [62]. Touch positioning with an average error sum of ~ 1 mm was achieved. To explore the function-driven designs utilizing the analog positioning strategy, Chen et al. proposed a nano-manipulation terminal for controlling the objects in three-dimensional space [59]. As shown in Figure 3d, the length changes of the strip can be determined by measuring the signal ratios from the two terminal electrodes. This enables the localization of the mobile object that connects three strips.
In the quest for superior contact localization resolution, the analog positioning strategy stands out with the highest theoretical resolution among the three discussed strategies. As outlined in Table 1, the challenges of wiring complexity and signal channel augmentation become more pronounced with the expansion of sensor units in both the sensor matrix and parallel line array strategies, essential for achieving higher resolution. Consequently, there exists a constraint on sensor density or line electrode density that imposes limitations on attainable resolution. Nevertheless, it is important to note that the analog positioning strategy is not exempt from challenges, as the environmental noise can introduce measurement error in the form of deviation between the measured tribo-voltage ratio and the reference ratio in the ratio-position look-up table [63,64]. Moreover, the validity of the pre-determined ratio-position look-up table is restricted to the present device configuration, indicating that recalibration of the look-up table is required in the event of device deformation. Timely recalibration poses challenges for wearable HMI where device deformation occurs frequently. Therefore, continuing research work is anticipated to improve the robustness of the analog positioning strategy.

3. Potential Functions Enabled by TCLE

3.1. Touch Positioning

Based on the device design strategies discussed in Section 2.2, advanced functions, including touch positioning, slide detection, and slide trajectory detection have been successfully achieved. Single-point and multi-point touch positioning is crucial for the touch operation of the keypad, touchpad, and e-skin [65,66]. The sensor matrix strategy is commonly employed to achieve the multi-point touch positioning function, owing to the independent signal lines of each sensor unit. As shown in Figure 4a, a 5 × 5 matrix consisting of all-printed triboelectric sensors and wrinkled silver electrodes was proposed by Li et al. for the detection of the multi-point contact of a plastic plate [67]. To address the issue of crosstalk between sensor units, Li et al. [68] proposed a 3D-printed TENG sensor matrix (size: 7.5 cm × 7.5 cm, Figure 4b) with 100 sensor units. The 3D patterned substrate effectively enhances independence between sensor units and suppresses the maximum crosstalk output to 10.8%.
Parallel line array strategy can be utilized for single-point contact detection. Figure 4c depicts the 3 × 3 cross-interaction tactile sensor array proposed by Zhang et al., which utilizes a MXene electrode and rough surface to enhance electrical output for single-point touch positioning [69].
However, when applying the parallel line array strategy, attention should be paid to the ‘ghost point’ problem that is also encountered in self-capacitance type capacitive touchpads [70]. In Section 2.1, as discussed earlier, contact at the electrode area produces a triboelectric voltage/current output. In the parallel line array design, detectable points are situated at the cross points of electrodes in two layers. The contact-induced output can be identified at both ends of the electrode. During operation, the output signals are scanned in the two electrode layers, and an electrode with output in both layers determines one of the contact position coordinates in a 2D system. However, the presence of “ghost points” can compromise accurate positioning. Figure 4d illustrates this issue. In a scenario where simultaneous touches occur at points A and B, triboelectric signals are generated in electrodes R1, R2, L1, and L2. Consequently, the obtained 2D coordinates are (R1, L1), (R1, L2), (R2, L1), and (R2, L2). The emergence of “ghost points” (R2, L1) and (R1, L2) results in incorrect localization. While fully synchronized touch is improbable in practice, there exists a small time interval between contacts at two points. This temporal distinction leads to two discernible triboelectric pulse peaks in the output signals, offering a potential solution to this issue through judicious signal processing.

3.2. Slide Detection

During the sliding motion, the TCLE and the moving object are constantly in contact. As discussed in Section 2.1, even while maintaining contact, the sliding of the moving object across the electrode leads to the generation of a triboelectric signal. Consequently, by employing a rationally designed electrode pattern, the trajectory points during the slide can be recorded to retrieve the spatiotemporal location or restore the sliding trajectory. This understanding paves the way for the realization of diverse advanced functions, such as slide direction detection, slide localization, and handwriting trajectory recognition [66,71,72].
The spatiotemporal sliding trajectory dot sequence can be recorded as a sequence of triboelectric pulses. By utilizing a customized electrode topology, both 1D and 2D slide detection can be achieved. As shown in Figure 5a, Yang et al. proposed a sliding detection TENG utilizing the polytetrafluoroethylene tribo-material and one-dimensional distributed Al electrodes [73]. By extracting the timing information of the triboelectric pulse on each electrode, 1D dynamic addressing of the stylus is achieved. Based on the results obtained from the 1D electrode array design, it can be inferred that the width and interval of the triboelectric pulses align with the width and interval of the electrodes. Hence, by employing a combination of 1D electrode arrays spanning various directions, it becomes possible to detect sliding directions through the characteristics of the triboelectric pulse sequence. As shown in Figure 5b, Qiu et al. proposed a control interface based on a gray code-inspired Cu electrode pattern [39]. Each sliding direction is associated with a distinctive combination of electrode array widths. Consequently, by considering the time node combination of the triboelectric pulses generated when the finger reaches each electrode, it becomes feasible to determine the sliding direction. Guo et al. adopted a similar design concept and proposed a soft HMI with an Archimede spiral electrode configuration. This design covers all possible sliding directions and successfully achieves the detection of multi-directional sliding movements [40].
By continuously recording the tracing point with TCLE, the handwriting trajectory can be restored for handwriting digits and character recognition. The sensor matrix design strategy is the optimal approach to realize tracing point detection with high accuracy and high reliability. Figure 5c shows the trajectory detection panel proposed by Cui et al. [74] which utilizes the conductive sponge electrode unit. The act of sliding the polypropylene brush on the sponge electrode units produces triboelectric signals, allowing for the restoration of the trajectory by addressing the activated sensor units. When applying the parallel line array strategy, the intersection points of the two line arrays correspond to the detectable sites. Simultaneous sliding-generated triboelectric pulses in the two electrode arrays indicate the presence of sliding at the detection site. However, the recognition accuracy may experience a decrease for certain trajectories. This decrease may occur when using a rectangular electrode, as the triboelectric signal strength may be too weak to be identified when sliding along the electrode. This is due to the limited variation in the overlap area between the moving object and the rectangular electrode. Therefore, pattern engineering is required to ensure the output strength. As shown in Figure 5d, Guo et al. proposed a self-powered e-skin utilizing the polydimethylsiloxane tribo-material and silver nanowire electrode [75]. The “line” electrode incorporates five quadrilateral valves to ensure that when the finger slides along it, there is a sufficient variation of the overlap area between the finger and electrode. By scanning the triboelectric signals on each electrode, the sliding trajectory is successfully extracted based on the output map. In addition to pattern engineering, working mode optimization is also a feasible approach for robust device designs. The self-powered trajectory-tracking panel proposed by Ba et al. [41] utilizes a sensor unit that combines two TENGs working in the contact-separation mode and single-electrode mode, respectively. The independent triboelectric signal excitation in two TENGs ensures signal strength in both the top and bottom signal output channels. It is also worth mentioning that the crosstalk reduction effect owing to the electrode miniaturization design discussed in Ref. [41] is valuable for the reference of TCLE, especially in cases with high sensor unit density.

4. Applications of TCLE

Touch positioning, sliding motion detection, and sliding trajectory detection functions of TCLE are indispensable for enabling the touch operation of HMI [76,77], tactile sensation of E-skin [78,79], and human behavior detection of IoT [80,81]. Additionally, the energy-saving ability of TCLE is anticipated to substantially enhance the lightweight and long-endurance design of wearable/portable HMIs, such as foldable screens, smart home devices, and flexible keystrokes. Therefore, the extensive research conducted on TCLE has resulted in the discovery of various applications in this field, such as HMI for robot control [82,83,84]. Moreover, there has been a surge in interdisciplinary research on artificial intelligence (AI) and tribo-electronics [85]. The signals obtained from TCLE not only encompass position information but also contain valuable data that reflects user habits and other relevant features. Numerous studies have presented evidence that AI can effectively extract these input features to realize advanced functions such as user identification [7,86].

4.1. HMI

Owing to the high accuracy and robust performance of AI-based image and signal recognition [87,88], AI techniques are widely used for the realization of handwriting character recognition and user identification of TCLE. As shown in Figure 6a, Yun et al. proposed a transparent flexible touch panel with a handwriting digit recognition accuracy of 93.6% and 91.8% at the bending angles of 0° and 165°, respectively [89]. The recorded input trajectory was classified by a pre-trained neural network utilizing the training samples from the Modified National Institute of Standards and Technology database (MNIST). Figure 6b shows the self-powered smart mats proposed by Wang et al. [90] utilizing conductive sponges with different filling rates. This approach allows for differentiated triboelectric signal strength and contact position to be determined from the step-detection units of a 3 × 2 sensor array. A CNN was then used to realize crew identification by learning and identifying the walking gait patterns, which is a video privacy-protected approach for security surveillance. These achievements indicate that the application of AI techniques in TCLE promises enhanced convenience and effectiveness owing to the developing AI platform and computing power on portable devices.

4.2. E-Skin

E-skin serves as a crucial platform for realizing the tactile perception of artificial limbs [95], intelligent robots [96], and wearable HMI terminals [97]. Achieving conformality and ensuring optimal wearing comfort are critical considerations in the application of TCLE in e-skin. Addressing these concerns requires further research in advanced materials and the development of novel fabrication methods. Based on a 3D-printed fiber composition comprising a sheath made of PTFE + PDMS mixture and a core consisting of PDMS + graphene mixture, a smart textile grid with good stretchability, breathability, and washability was proposed by Chen et al. [91] As shown in Figure 6c, a 6 × 6 contact localization mesh that can be easily attached to the skin and curved object surface was constructed utilizing the parallel line array strategy. To explore the system integration method, Zhao et al. proposed a touch operation panel [92] based on the sensor matrix strategy (Figure 6d). Sliding trajectory recognition is achieved by a 2 × 2 sensor matrix and a programmable control system consisting of data acquisition, signal processing, and operation control modules, which sets a high standard for system-level TCLE application.

4.3. IoT

The wide range of material options, combined with the cost-effective advantages of TENG [98], offers promising application prospects for TCLE in the low power consumption design of wireless IoT devices [99]. As shown in Figure 6e, Lei et al. proposed a sitting posture monitoring mat with a self-powered sensor incorporating a bamboo-inspired dielectric layer for remote healthcare [93]. The unique pressure distribution associated with the sitting posture can be identified by analyzing the voltage output map generated by the sensor array. Due to the larger size of the device, the large-scale and cost-effective fabrication methods pose a crucial challenge in the design of IoT for smart home applications. Shi et al. reported a simple treatment strategy for wood to prepare the triboelectric material, allowing for the TENG sensor units to be easily integrated with household facilities [94]. As shown in Figure 6f, smart floor and smart door locks were proposed utilizing the senor matrix design principle. By continuously recording the contact position of the human body and the smart floor, it becomes possible to detect the walking route as well as identify instances of falling events.
Table 2 provides a summary of the characteristics of three key aspects (size, stretchability, and resolution) of TCLEs across various application scenarios. First, in the application of IoT, such as smart floors, furniture, and health monitoring equipment, a larger device size is frequently required. A device size exceeding 1 m2 has been reported in ref. [100], which proposes a smart carpet for footstep detection. Due to the considerable device size, cable installation poses a manageable challenge in this scenario. However, ongoing efforts to enhance the preparation process are expected to streamline the production of larger devices.
Next, for wearable devices, flexibility and stretchability of TCLE are required to ensure that the device does not affect the body’s movement. The stretchability of the sensor unit of 200% in ref. [101], which surpasses the stretch limit of human skin, meets the requirement in the design of e-skin. It is also necessary to ensure the breathability, wearing comfort, and lightweight design of the device. The implementation of the ultra-thin, ultra-soft design is a potential path towards achieving a “wear without feeling” TCLE for e-skin. Moreover, the application of e-skin often demands wireless real-time signal transmission to eliminate the need for a rigid signal processing unit and to ensure the comfort of the e-skin.
Lastly, spatial resolution is an important performance indicator of TCLE, especially for touch panel applications. The average area occupied by a single sensor unit in a sensor matrix or a detection pixel in a parallel line array is utilized to represent the resolution. A resolution of 1.25 cm × 1.25 cm is achieved through a sensor matrix design, while a resolution of 1.6 cm × 1.6 cm is achieved through a parallel line array design. There exists a notable disparity when compared to the millimeter-range resolution of commercial capacitive touch screens [103]. A high-precision electrode preparation method is anticipated for the design of high-resolution TCLE. Meanwhile, with the substantially increased resolution, challenges in cabling are anticipated to escalate. Consequently, additional research is warranted to explore signal channel reduction and signal processing methods tailored for high signal line density conditions.

5. Overview and Future Perspectives

The practical design strategies of TCLE including sensor matrix, parallel line array, and analog positioning have been established over the last decade. The sensor matrix strategy demonstrates advantages in convenient and flexible device design capabilities. In contrast, the parallel line array strategy presents benefits such as significantly reduced signal channel complexity and simplified wiring. Meanwhile, the analog positioning strategy showcases advantages associated with the highest theoretical resolution. These design strategies pave the way for the development of devices that can perform various functions such as touch positioning, slide detection, and trajectory detection. TCLEs have gained research attention in the field of HMI due to their cost-effectiveness, ease of fabrication, and the availability of a wide range of material options. In addition, their energy-saving characteristics make them suitable for use in power consumption-restricted wearable electronics. Furthermore, the combination of TCLEs with AI techniques has the potential to enhance detection accuracy while offering advanced functionalities, such as user identification.
To ensure the attainment of optimal and robust performance, as well as the development of practical devices, extensive research endeavors are anticipated in the following domains:
(1)
In the development of the contact localization principle, the parallel line array strategy shows great potential in practical applications due to its manageable signal channel and cabling difficulties. Ongoing advancements in signal channel reduction methods are anticipated to further simplify signal processing and reduce power consumption. Conversely, the analog positioning strategy finds utility in specific scenarios where ultra-high resolution is essential; however, it necessitates noise-resistant and environment-insensitive designs to ensure robust performance under adverse working conditions.
(2)
In the function development of TCLE, a deeper exploration of the combination of TCLE and AI techniques might further improve the robustness and functional diversity of TCLE.
(3)
In TCLE applications, a flexible selection of the working principle is recommended, grounded in a deep understanding of the characteristics of different TCLE working principles and specific application requirements. Material engineering should align with application-specific needs, such as wearability, lightweight, and transparency, while innovative device designs are crucial for addressing challenges like cabling difficulties. Ongoing improvements in detection resolution, with an expected attainment of the millimeter level, are poised to open avenues for TCLE in commercial electronic applications. Simultaneously, large-area fabrication methods, coupled with corresponding device design optimizations, also offer significant practical value.

Funding

This research was funded by Natural Science Research Projects of Jiangsu Province University, grant number [20KJA510001], Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications, grant number [NY223033], and Middle-aged Academic Leader of Qing Lan Project in Jiangsu Province University.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Pyun, K.R.; Rogers, J.A.; Ko, S.H. Materials and Devices for Immersive Virtual Reality. Nat. Rev. Mater. 2022, 7, 841–843. [Google Scholar] [CrossRef] [PubMed]
  2. Shi, Q.; Dong, B.; He, T.; Sun, Z.; Zhu, J.; Zhang, Z.; Lee, C. Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things. InfoMat 2020, 2, 1131–1162. [Google Scholar] [CrossRef]
  3. Niu, Y.; Liu, H.; He, R.; Li, Z.; Ren, H.; Gao, B.; Guo, H.; Genin, G.M.; Xu, F. The new generation of soft and wearable electronics for health monitoring in varying environment: From normal to extreme conditions. Mater. Today 2020, 41, 219–242. [Google Scholar] [CrossRef]
  4. Zhao, Z.; Dai, Y.; Dou, S.X.; Liang, J. Flexible nanogenerators for wearable electronic applications based on piezoelectric materials. Mater. Today Energy 2021, 20, 100690. [Google Scholar] [CrossRef]
  5. Wang, K.; Yap, L.W.; Gong, S.; Wang, R.; Wang, S.J.; Cheng, W. Nanowire-Based Soft Wearable Human-Machine Interfaces for Future Virtual and Augmented Reality Applications. Adv. Funct. Mater. 2021, 31, 2008347. [Google Scholar] [CrossRef]
  6. Lam Po Tang, S. Recent Developments in Flexible Wearable Electronics for Monitoring Applications. Trans. Inst. Meas. Control 2007, 29, 283–300. [Google Scholar] [CrossRef]
  7. Sun, Z.; Zhu, M.; Lee, C. Progress in the Triboelectric Human-Machine Interfaces (HMIs)-Moving from Smart Gloves to AI/Haptic Enabled HMI in the 5G/IoT Era. Nanoenergy Adv. 2021, 1, 81–120. [Google Scholar] [CrossRef]
  8. Liu, J.; Tian, G.; Yang, W.; Deng, W. Recent progress in flexible piezoelectric devices toward human-machine interactions. Soft Sci. 2022, 2, 22. [Google Scholar]
  9. Kim, K.K.; Suh, Y.; Ko, S.H. Smart Stretchable Electronics for Advanced Human-Machine Interface. Adv. Intell. Syst. 2020, 3, 2000157. [Google Scholar] [CrossRef]
  10. Chen, Y.; Gao, Z.; Zhang, F.; Wen, Z.; Sun, X. Recent Progress in Self-Powered Multifunctional e-Skin for Advanced Applications. Exploration 2022, 2, 20210112. [Google Scholar] [CrossRef]
  11. Zheng, Y.; Yin, R.; Zhao, Y.; Liu, H.; Zhang, D.; Shi, X.; Zhang, B.; Liu, C.; Shen, C. Conductive MXene/Cotton Fabric Based Pressure Sensor with Both High Sensitivity and Wide Sensing Range for Human Motion Detection and E-Skin. Chem. Eng. J. 2021, 420, 127720. [Google Scholar] [CrossRef]
  12. Hu, Z.; Wang, J.; Wang, Y.; Wang, C.; Wang, Y.; Zhang, Z.; Xu, P.; Zhao, T.; Luan, Y.; Liu, C.; et al. A Robust and Wearable Triboelectric Tactile Patch as Intelligent Human-Machine Interface. Materials 2021, 14, 6366. [Google Scholar] [CrossRef] [PubMed]
  13. Hao, S.; Jiao, J.; Chen, Y.; Wang, Z.L.; Cao, X. Natural Wood-Based Triboelectric Nanogenerator as Self-Powered Sensing for Smart Homes and Floors. Nano Energy 2020, 75, 104957. [Google Scholar] [CrossRef]
  14. Jeon, S.-B.; Kim, W.-G.; Park, S.-J.; Tcho, I.-W.; Jin, I.-K.; Han, J.-K.; Kim, D.; Choi, Y.-K. Self-Powered Wearable Touchpad Composed of All Commercial Fabrics Utilizing a Crossline Array of Triboelectric Generators. Nano Energy 2019, 65, 103994. [Google Scholar] [CrossRef]
  15. Feng, Y.; Huang, X.; Liu, S.; Guo, W.; Li, Y.; Wu, H. A Self-Powered Smart Safety Belt Enabled by Triboelectric Nanogenerators for Driving Status Monitoring. Nano Energy 2019, 62, 197–204. [Google Scholar] [CrossRef]
  16. Zhang, Q.; Jin, T.; Cai, J.; Xu, L.; He, T.; Wang, T.; Tian, Y.; Li, L.; Peng, Y.; Lee, C. Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications. Adv. Sci. 2022, 9, 2103694. [Google Scholar] [CrossRef] [PubMed]
  17. Guo, H.; Wan, J.; Wang, H.; Wu, H.; Xu, C.; Miao, L.; Han, M.; Zhang, H. Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition. Research 2021, 2021, 4689869. [Google Scholar] [CrossRef] [PubMed]
  18. Fan, F.-R.; Tian, Z.-Q.; Lin Wang, Z. Flexible Triboelectric Generator. Nano Energy 2012, 1, 328–334. [Google Scholar] [CrossRef]
  19. Munirathinam, P.; Mathew, A.A.; Shanmugasundaram, V.; Vivekananthan, V.; Purusothaman, Y.; Kim, S.-J.; Chandrasekhar, A. A Comprehensive Review on Triboelectric Nanogenerators Based on Real-Time Applications in Energy Harvesting and Self-Powered Sensing. Mater. Sci. Eng. B 2023, 297, 116762. [Google Scholar] [CrossRef]
  20. Song, C.; Zhu, X.; Wang, M.; Yang, P.; Chen, L.; Hong, L.; Cui, W. Recent Advances in Ocean Energy Harvesting Based on Triboelectric Nanogenerators. Sustain. Energy Technol. Assess. 2022, 53, 102767. [Google Scholar] [CrossRef]
  21. Wei, Z.; Wang, J.; Liu, Y.; Yuan, J.; Liu, T.; Du, G.; Zhu, S.; Nie, S. Sustainable Triboelectric Materials for Smart Active Sensing Systems. Adv. Funct. Mater. 2022, 32, 2208277. [Google Scholar] [CrossRef]
  22. Lei, H.; Chen, Y.; Gao, Z.; Wen, Z.; Sun, X. Advances in Self-Powered Triboelectric Pressure Sensors. J. Mater. Chem. A 2021, 9, 20100–20130. [Google Scholar] [CrossRef]
  23. Yang, Y.; Guo, X.; Zhu, M.; Sun, Z.; Zhang, Z.; He, T.; Lee, C. Triboelectric Nanogenerator Enabled Wearable Sensors and Electronics for Sustainable Internet of Things Integrated Green Earth. Adv. Energy Mater. 2023, 13, 2203040. [Google Scholar] [CrossRef]
  24. Lin, L.; Xie, Y.; Wang, S.; Wu, W.; Niu, S.; Wen, X.; Wang, Z.L. Triboelectric Active Sensor Array for Self-Powered Static and Dynamic Pressure Detection and Tactile Imaging. ACS Nano 2013, 7, 8266–8274. [Google Scholar] [CrossRef] [PubMed]
  25. Yang, Y.; Zhang, H.; Lin, Z.-H.; Zhou, Y.S.; Jing, Q.; Su, Y.; Yang, J.; Chen, J.; Hu, C.; Wang, Z.L. Human Skin Based Triboelectric Nanogenerators for Harvesting Biomechanical Energy and as Self-Powered Active Tactile Sensor System. ACS Nano 2013, 7, 9213–9222. [Google Scholar] [CrossRef] [PubMed]
  26. Lee, Y.; Kim, J.; Jang, B.; Kim, S.; Sharma, B.K.; Kim, J.-H.; Ahn, J.-H. Graphene-Based Stretchable/Wearable Self-Powered Touch Sensor. Nano Energy 2019, 62, 259–267. [Google Scholar] [CrossRef]
  27. Liu, H.; Ji, Z.; Xu, H.; Sun, M.; Chen, T.; Sun, L.; Chen, G.; Wang, Z. Large-Scale and Flexible Self-Powered Triboelectric Tactile Sensing Array for Sensitive Robot Skin. Polymers 2017, 9, 586. [Google Scholar] [CrossRef]
  28. Lin, Z.; Yang, J.; Li, X.; Wu, Y.; Wei, W.; Liu, J.; Chen, J.; Yang, J. Large-Scale and Washable Smart Textiles Based on Triboelectric Nanogenerator Arrays for Self-Powered Sleeping Monitoring. Adv. Funct. Mater. 2018, 28, 1704112. [Google Scholar] [CrossRef]
  29. Jiang, X.-Z.; Sun, Y.-J.; Fan, Z.; Zhang, T.-Y. Integrated Flexible, Waterproof, Transparent, and Self-Powered Tactile Sensing Panel. ACS Nano 2016, 10, 7696–7704. [Google Scholar] [CrossRef]
  30. Wang, X.; Zhang, Y.; Zhang, X.; Huo, Z.; Li, X.; Que, M.; Peng, Z.; Wang, H.; Pan, C. A Highly Stretchable Transparent Self-Powered Triboelectric Tactile Sensor with Metallized Nanofibers for Wearable Electronics. Adv. Mater. 2018, 30, 1706738. [Google Scholar] [CrossRef]
  31. Chen, T.; Shi, Q.; Li, K.; Yang, Z.; Liu, H.; Sun, L.; Dziuban, J.A.; Lee, C. Investigation of Position Sensing and Energy Harvesting of a Flexible Triboelectric Touch Pad. Nanomaterials 2018, 8, 613. [Google Scholar] [CrossRef] [PubMed]
  32. Ganesh, R.S.; Yoon, H.; Kim, S. Recent trends of biocompatible triboelectric nanogenerators toward self-powered e-skin. EcoMat 2020, 2, e12065. [Google Scholar] [CrossRef]
  33. Zhou, Y.; Xiao, X.; Chen, G.; Zhao, X.; Chen, J. Self-Powered Sensing Technologies for Human Metaverse Interfacing. Joule 2022, 6, 1381–1389. [Google Scholar] [CrossRef]
  34. Lai, Z.; Xu, J.; Bowen, C.R.; Zhou, S. Self-powered and self-sensing devices based on human motion. Joule 2022, 6, 1501–1565. [Google Scholar] [CrossRef]
  35. Wang, L.; Liu, Y.; Liu, Q.; Zhu, Y.; Wang, H.; Xie, Z.; Yu, X.; Zi, Y. A metal-electrode-free, fully integrated, soft triboelectric sensor array for self-powered tactile sensing. Microsyst. Nanoeng. 2020, 6, 59. [Google Scholar] [CrossRef] [PubMed]
  36. Tao, J.; Bao, R.; Wang, X.; Peng, Y.; Li, J.; Fu, S.; Pan, C.; Wang, Z.L. Self-Powered Tactile Sensor Array Systems Based on the Triboelectric Effect. Adv. Funct. Mater. 2019, 29, 1806379. [Google Scholar] [CrossRef]
  37. Shi, M.; Zhang, J.; Chen, H.; Han, M.; Shankaregowda, S.A.; Su, Z.; Meng, B.; Cheng, X.; Zhang, H. Self-Powered Analogue Smart Skin. ACS Nano 2016, 10, 4083–4091. [Google Scholar] [CrossRef]
  38. Zhang, Z.; He, T.; Zhu, M.; Sun, Z.; Shi, Q.; Zhu, J.; Dong, B.; Yuce, M.R.; Lee, C. Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications. NPJ Flex. Electron. 2020, 4, 29. [Google Scholar] [CrossRef]
  39. Qiu, C.; Wu, F.; Lee, C.; Yuce, M.R. Self-Powered Control Interface Based on Gray Code with Hybrid Triboelectric and Photovoltaics Energy Harvesting for IoT Smart Home and Access Control Applications. Nano Energy 2020, 70, 104456. [Google Scholar] [CrossRef]
  40. Guo, H.; Wang, H.; Xiang, Z.; Wu, H.; Wan, J.; Xu, C.; Chen, H.; Han, M.; Zhang, H. Soft Human-Machine Interface with Triboelectric Patterns and Archimedes Spiral Electrodes for Enhanced Motion Detection. Adv. Funct. Mater. 2021, 31, 2103075. [Google Scholar] [CrossRef]
  41. Ba, Y.-Y.; Bao, J.-F.; Wang, Z.-Y.; Deng, H.-T.; Wen, D.-L.; Zhang, X.-R.; Tu, C.; Zhang, X.-S. Self-Powered Trajectory-Tracking Microsystem Based on Electrode-Miniaturized Triboelectric Nanogenerator. Nano Energy 2021, 82, 105730. [Google Scholar] [CrossRef]
  42. Saha, K.; Chatterjee, A.; Das, A.; Ghorai, A.; Jeong, U. Self-powered ionic tactile sensors. J. Mater. Chem. C 2023, 11, 7920–7936. [Google Scholar] [CrossRef]
  43. Liu, Y.; Bao, R.; Tao, J.; Li, J.; Dong, M.; Pan, C. Recent Progress in Tactile Sensors and Their Applications in Intelligent Systems. Sci. Bull. 2020, 65, 70–88. [Google Scholar] [CrossRef] [PubMed]
  44. Peng, Y.; Yang, N.; Xu, Q.; Dai, Y.; Wang, Z. Recent Advances in Flexible Tactile Sensors for Intelligent Systems. Sensors 2021, 21, 5392. [Google Scholar] [CrossRef] [PubMed]
  45. Kim, W.-G.; Kim, D.-W.; Tcho, I.-W.; Kim, J.-K.; Kim, M.-S.; Choi, Y.-K. Triboelectric Nanogenerator: Structure, Mechanism, and Applications. ACS Nano 2021, 15, 258–287. [Google Scholar] [CrossRef]
  46. Wu, C.; Wang, A.C.; Ding, W.; Guo, H.; Wang, Z.L. Triboelectric Nanogenerator: A Foundation of the Energy for the New Era. Adv. Energy Mater. 2019, 9, 1802906. [Google Scholar] [CrossRef]
  47. Niu, S.; Wang, Z.L. Theoretical systems of triboelectric nanogenerators. Nano Energy 2015, 14, 161–192. [Google Scholar] [CrossRef]
  48. Dharmasena, R.D.I.G.; Deane, J.H.B.; Silva, S.R.P. Nature of Power Generation and Output Optimization Criteria for Triboelectric Nanogenerators. Adv. Energy Mater. 2018, 8, 1802190. [Google Scholar] [CrossRef]
  49. Cai, Y.-W.; Zhang, X.-N.; Wang, G.-G.; Li, G.-Z.; Zhao, D.-Q.; Sun, N.; Li, F.; Zhang, H.-Y.; Han, J.-C.; Yang, Y. A Flexible Ultra-Sensitive Triboelectric Tactile Sensor of Wrinkled PDMS/MXene Composite Films for E-Skin. Nano Energy 2021, 81, 105663. [Google Scholar] [CrossRef]
  50. Hwang, B.-U.; Zabeeb, A.; Trung, T.Q.; Wen, L.; Lee, J.D.; Choi, Y.-I.; Lee, H.-B.; Kim, J.H.; Han, J.G.; Lee, N.-E. A transparent stretchable sensor for distinguishable detection of touch and pressure by capacitive and piezoresistive signal transduction. NPG Asia Mater. 2019, 11, 23. [Google Scholar] [CrossRef]
  51. Bai, Y.; Xu, L.; Lin, S.; Luo, J.; Qin, H.; Han, K.; Wang, Z.L. Charge Pumping Strategy for Rotation and Sliding Type Triboelectric Nanogenerators. Adv. Energy Mater. 2020, 10, 2000605. [Google Scholar] [CrossRef]
  52. Cui, N.; Dai, C.; Liu, J.; Gu, L.; Ge, R.; Du, T.; Wang, Z.; Qin, Y. Increasing the Output Charge Quantity of Triboelectric Nanogenerators via Frequency Multiplication with a Multigap-Structured Friction Layer. Energy Environ. Sci. 2020, 13, 2069–2076. [Google Scholar] [CrossRef]
  53. He, W.; Shan, C.; Wu, H.; Fu, S.; Li, Q.; Li, G.; Zhang, X.; Du, Y.; Wang, J.; Wang, X.; et al. Capturing Dissipation Charge in Charge Space Accumulation Area for Enhancing Output Performance of Sliding Triboelectric Nanogenerator. Adv. Energy Mater. 2022, 12, 2201454. [Google Scholar] [CrossRef]
  54. Wang, H.; Sakamoto, H.; Asai, H.; Zhang, J.-H.; Meboso, T.; Uchiyama, Y.; Kobayashi, E.; Takamura, E.; Suye, S.-I. An All-Fibrous Triboelectric Nanogenerator with Enhanced Outputs Depended on the Polystyrene Charge Storage Layer. Nano Energy 2021, 90, 106515. [Google Scholar] [CrossRef]
  55. Guo, W.; Xia, Y.; Zhu, Y.; Han, S.; Li, Q.; Wang, X. Laser-Induced Graphene Based Triboelectric Nanogenerator for Accurate Wireless Control and Tactile Pattern Recognition. Nano Energy 2023, 108, 108229. [Google Scholar] [CrossRef]
  56. Qu, X.; Xue, J.; Liu, Y.; Rao, W.; Liu, Z.; Li, Z. Fingerprint-shaped triboelectric tactile sensor. Nano Energy 2022, 98, 107324. [Google Scholar] [CrossRef]
  57. Pu, X.; Liu, M.; Chen, X.; Sun, J.; Du, C.; Zhang, Y.; Zhai, J.; Hu, W.; Wang, Z.L. Ultrastretchable, transparent triboelectric nanogenerator as electronic skin for biomechanical energy harvesting and tactile sensing. Sci. Adv. 2017, 3, e1700015. [Google Scholar] [CrossRef]
  58. Lin, Y.; Duan, S.; Zhu, D.; Li, Y.; Wang, B.; Wu, J. Self-Powered and Interface-Independent Tactile Sensors Based on Bilayer Single-Electrode Triboelectric Nanogenerators for Robotic Electronic Skin. Adv. Intell. Syst. 2023, 5, 2100120. [Google Scholar] [CrossRef]
  59. Chen, T.; Shi, Q.; Zhu, M.; He, T.; Yang, Z.; Liu, H.; Sun, L.; Yang, L.; Lee, C. Intuitive-Augmented Human-Machine Multidimensional Nano-Manipulation Terminal Using Triboelectric Stretchable Strip Sensors Based on Minimalist Design. Nano Energy 2019, 60, 440–448. [Google Scholar] [CrossRef]
  60. Yang, J.; Xu, W.; Liu, S.; Liu, S.; Feng, D.; Meng, Y.; Wang, M.; Li, X. Self-Powered Hybrid Wearable E-Skin for Artificial Intelligence Sensing System. In Proceedings of the 2021 IEEE 34th International Conference on Micro Electro Mechanical Systems (MEMS), Gainesville, FL, USA, 25–29 January 2021; pp. 107–110. [Google Scholar] [CrossRef]
  61. Pratap, A.; Gogurla, N.; Kim, S. Elastic and Skin-Contact Triboelectric Nanogenerators and Their Applicability in Energy Harvesting and Tactile Sensing. ACS Appl. Electron. Mater. 2022, 4, 1124–1131. [Google Scholar] [CrossRef]
  62. Zhang, J.; Song, Z.; Guo, H.; Song, Y.; Yu, B.; Zhang, H. GPS-Inspired Stretchable Self-Powered Electronic Skin. IEEE Trans. Nanotechnol. 2018, 17, 460–466. [Google Scholar] [CrossRef]
  63. Li, T.; Zou, J.; Xing, F.; Zhang, M.; Cao, X.; Wang, N.; Wang, Z.L. From dual-mode triboelectric nanogenerator to smart tactile sensor: A multiplexing design. ACS Nano 2017, 11, 3950–3956. [Google Scholar] [CrossRef] [PubMed]
  64. Cheng, Y.; Wu, D.; Hao, S.; Jie, Y.; Cao, X.; Wang, N.; Wang, Z.L. Highly stretchable triboelectric tactile sensor for electronic skin. Nano Energy 2019, 64, 103907. [Google Scholar] [CrossRef]
  65. Jiang, D.; Lian, M.; Xu, M.; Sun, Q.; Bin Xu, B.; Thabet, H.K.; El-Bahy, S.M.; Ibrahim, M.M.; Huang, M.; Guo, Z. Advances in Triboelectric Nanogenerator Technology—Applications in Self-Powered Sensors, Internet of Things, Biomedicine, and Blue Energy. Adv. Compos. Hybrid Mater. 2023, 6, 57. [Google Scholar] [CrossRef]
  66. Nguyen, J.T.; Cheng, W. A Review on Epidermal Nanogenerators: Recent Progress of the Future Self-Powered Skins. Small Struct. 2022, 3, 2200034. [Google Scholar] [CrossRef]
  67. Li, H.; Fang, X.; Li, R.; Liu, B.; Tang, H.; Ding, X.; Xie, Y.; Zhou, R.; Zhou, G.; Tang, Y. All-Printed Soft Triboelectric Nanogenerator for Energy Harvesting and Tactile Sensing. Nano Energy 2020, 78, 105288. [Google Scholar] [CrossRef]
  68. Li, J.; Liu, Y.; Wu, M.; Yao, K.; Gao, Z.; Gao, Y.; Huang, X.; Wong, T.H.; Zhou, J.; Li, D.; et al. Thin, Soft, 3D Printing Enabled Crosstalk Minimized Triboelectric Nanogenerator Arrays for Tactile Sensing. Fundam. Res. 2023, 3, 111–117. [Google Scholar] [CrossRef]
  69. Zhang, S.; Xiao, Y.; Chen, H.; Zhang, Y.; Liu, H.; Qu, C.; Shao, H.; Xu, Y. Flexible Triboelectric Tactile Sensor Based on a Robust MXene/Leather Film for Human-Machine Interaction. ACS Appl. Mater. Interfaces 2023, 15, 13802–13812. [Google Scholar] [CrossRef]
  70. Barrett, G.; Omote, R. Projected-Capacitive Touch Technology. Inf. Disp. 2010, 26, 16–21. [Google Scholar] [CrossRef]
  71. Hu, H.; Zhang, F. Rational design of self-powered sensors with polymer nanocomposites for human-machine interaction. Chin. J. Aeronaut. 2022, 35, 155–177. [Google Scholar] [CrossRef]
  72. Hu, C.; Wang, F.; Cui, X.; Zhu, Y. Recent progress in textile-based triboelectric force sensors for wearable electronics. Adv. Compos. Hybrid Mater. 2023, 6, 70. [Google Scholar] [CrossRef]
  73. Yang, S.; Zhang, H.; Sang, S. An In-Plane Sliding Triboelectric Nanogenerator with a Multielectrode Array for Self-Powered Dynamic Addressing and Trajectory Tracking. Energy Technol. 2020, 8, 2000155. [Google Scholar] [CrossRef]
  74. Cui, X.; Zhao, T.; Yang, S.; Xie, G.; Zhang, Z.; Zhang, Y.; Sang, S.; Lin, Z.-H.; Zhang, W.; Zhang, H. A Spongy Electrode-Brush-Structured Dual-Mode Triboelectric Nanogenerator for Harvesting Mechanical Energy and Self-Powered Trajectory Tracking. Nano Energy 2020, 78, 105381. [Google Scholar] [CrossRef]
  75. Guo, H.; Wan, J.; Wu, H.; Wang, H.; Miao, L.; Song, Y.; Chen, H.; Han, M.; Zhang, H. Self-Powered Multifunctional Electronic Skin for a Smart Anti-Counterfeiting Signature System. ACS Appl. Mater. Interfaces 2020, 12, 22357–22364. [Google Scholar] [CrossRef]
  76. Yu, S.; Park, T.H.; Jiang, W.; Lee, S.W.; Kim, E.H.; Lee, S.; Park, J.-E.; Park, C. Soft Human-Machine Interface Sensing Displays: Materials and Devices. Adv. Mater. 2023, 35, e2204964. [Google Scholar] [CrossRef]
  77. Singh, H.P.; Kumar, P. Developments in the Human Machine Interface Technologies and Their Applications: A Review. J. Med. Eng. Technol. 2021, 45, 552–573. [Google Scholar] [CrossRef]
  78. Nie, B.; Liu, S.; Qu, Q.; Zhang, Y.; Zhao, M.; Liu, J. Bio-Inspired Flexible Electronics for Smart E-Skin. Acta Biomater. 2022, 139, 280–295. [Google Scholar] [CrossRef]
  79. Bunea, A.-C.; Dediu, V.; Laszlo, E.A.; Pistriţu, F.; Carp, M.; Iliescu, F.S.; Ionescu, O.N.; Iliescu, C. E-Skin: The Dawn of a New Era of On-Body Monitoring Systems. Micromachines 2021, 12, 1091. [Google Scholar] [CrossRef]
  80. Bin Zikria, Y.; Ali, R.; Afzal, M.K.; Kim, S.W. Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions. Sensors 2021, 21, 1174. [Google Scholar] [CrossRef]
  81. Laghari, A.A.; Wu, K.; Laghari, R.A.; Ali, M.; Khan, A.A. A Review and State of Art of Internet of Things (IoT). Arch. Comput. Methods Eng. 2022, 29, 1395–1413. [Google Scholar] [CrossRef]
  82. Lee, Y.; Lim, S.; Song, W.J.; Lee, S.; Yoon, S.J.; Park, J.-M.; Lee, M.-G.; Park, Y.-L.; Sun, J.-Y. Triboresistive Touch Sensing: Grid-Free Touch-Point Recognition Based on Monolayered Ionic Power Generators. Adv. Mater. 2022, 34, e2108586. [Google Scholar] [CrossRef] [PubMed]
  83. Chen, T.; Shi, Q.; Zhu, M.; He, T.; Sun, L.; Yang, L.; Lee, C. Triboelectric self-powered wearable flexible patch as 3D motion control interface for robotic manipulator. ACS Nano 2018, 12, 11561–11571. [Google Scholar] [CrossRef] [PubMed]
  84. Shi, Q.; Qiu, C.; He, T.; Wu, F.; Zhu, M.; Dziuban, J.A.; Walczak, R.; Yuce, M.R.; Lee, C. Triboelectric single-electrode-output control interface using patterned grid electrode. Nano Energy 2019, 60, 545–556. [Google Scholar] [CrossRef]
  85. Alagumalai, A.; Shou, W.; Mahian, O.; Aghbashlo, M.; Tabatabaei, M.; Wongwises, S.; Liu, Y.; Zhan, J.; Torralba, A.; Chen, J.; et al. Self-Powered Sensing Systems with Learning Capability. Joule 2022, 6, 1475–1500. [Google Scholar] [CrossRef]
  86. Jiao, P. Emerging Artificial Intelligence in Piezoelectric and Triboelectric Nanogenerators. Nano Energy 2021, 88, 106227. [Google Scholar] [CrossRef]
  87. Wang, M.; Wang, T.; Luo, Y.; He, K.; Pan, L.; Li, Z.; Cui, Z.; Liu, Z.; Tu, J.; Chen, X. Fusing Stretchable Sensing Technology with Machine Learning for Human-Machine Interfaces. Adv. Funct. Mater. 2021, 31, 2008807. [Google Scholar] [CrossRef]
  88. Shafiq, M.; Gu, Z. Deep Residual Learning for Image Recognition: A Survey. Appl. Sci. 2022, 12, 8972. [Google Scholar] [CrossRef]
  89. Yun, J.; Jayababu, N.; Kim, D. Self-Powered Transparent and Flexible Touchpad Based on Triboelectricity towards Artificial Intelligence. Nano Energy 2020, 78, 105325. [Google Scholar] [CrossRef]
  90. Wang, Y.; Hu, Z.; Wang, J.; Liu, X.; Shi, Q.; Wang, Y.; Qiao, L.; Li, Y.; Yang, H.; Liu, J.; et al. Deep Learning-Assisted Triboelectric Smart Mats for Personnel Comprehensive Monitoring toward Maritime Safety. ACS Appl. Mater. Interfaces 2022, 14, 24832–24839. [Google Scholar] [CrossRef]
  91. Chen, Y.; Deng, Z.; Ouyang, R.; Zheng, R.; Jiang, Z.; Bai, H.; Xue, H. 3D Printed Stretchable Smart Fibers and Textiles for Self-Powered e-Skin. Nano Energy 2021, 84, 105866. [Google Scholar] [CrossRef]
  92. Zhao, X.; Zhang, Z.; Liao, Q.; Xun, X.; Gao, F.; Xu, L.; Kang, Z.; Zhang, Y. Self-powered user-interactive electronic skin for programmable touch operation platform. Sci. Adv. 2020, 6, eaba4294. [Google Scholar] [CrossRef] [PubMed]
  93. Lei, H.; Xiao, J.; Chen, Y.; Jiang, J.; Xu, R.; Wen, Z.; Dong, B.; Sun, X. Bamboo-Inspired Self-Powered Triboelectric Sensor for Touch Sensing and Sitting Posture Monitoring. Nano Energy 2022, 91, 106670. [Google Scholar] [CrossRef]
  94. Shi, X.; Luo, J.; Luo, J.; Li, X.; Han, K.; Li, D.; Cao, X.; Wang, Z.L. Flexible Wood-Based Triboelectric Self-Powered Smart Home System. ACS Nano 2022, 16, 3341–3350. [Google Scholar] [CrossRef] [PubMed]
  95. Dolbashid, A.S.; Mokhtar, M.S.; Muhamad, F.; Ibrahim, F. Potential applications of human artificial skin and electronic skin (e-skin): A review. Bioinspired Biomim. Nanobiomat. 2018, 7, 53–64. [Google Scholar] [CrossRef]
  96. Dahiya, R.; Yogeswaran, N.; Liu, F.; Manjakkal, L.; Burdet, E.; Hayward, V.; Jörntell, H. Large-Area Soft e-Skin: The Challenges Beyond Sensor Designs. Proc. IEEE 2019, 107, 2016–2033. [Google Scholar] [CrossRef]
  97. Sun, Q.; Lai, Q.; Tang, Z.; Tang, X.; Zhao, X.; Roy, V.A.L. Advanced Functional Composite Materials toward E-Skin for Health Monitoring and Artificial Intelligence. Adv. Mater. Technol. 2022, 8, 2201088. [Google Scholar] [CrossRef]
  98. Zhou, L.; Liu, D.; Wang, J.; Wang, Z.L. Triboelectric nanogenerators: Fundamental physics and potential applications. Friction 2020, 8, 481–506. [Google Scholar] [CrossRef]
  99. Hossein Motlagh, N.; Mohammadrezaei, M.; Hunt, J.; Zakeri, B. Internet of Things (IoT) and the Energy Sector. Energies 2020, 13, 494. [Google Scholar] [CrossRef]
  100. Kim, B.; Song, J.Y.; Kim, D.Y.; Kim, M.-C.; Lin, Z.-H.; Choi, D.; Park, S.M. All-Aerosol-Sprayed High-Performance Transparent Triboelectric Nanogenerator with Embedded Charge-Storage Layer for Self-Powered Invisible Security IoT System and Raindrop-Solar Hybrid Energy Harvester. Nano Energy 2022, 104, 107878. [Google Scholar] [CrossRef]
  101. Zhou, K.; Zhao, Y.; Sun, X.; Yuan, Z.; Zheng, G.; Dai, K.; Mi, L.; Pan, C.; Liu, C.; Shen, C. Ultra-Stretchable Triboelectric Nanogenerator as High-Sensitive and Self-Powered Electronic Skins for Energy Harvesting and Tactile Sensing. Nano Energy 2020, 70, 104546. [Google Scholar] [CrossRef]
  102. Zhang, X.; Li, Z.; Du, W.; Zhao, Y.; Wang, W.; Pang, L.; Chen, L.; Yu, A.; Zhai, J. Self-Powered Triboelectric-Mechanoluminescent Electronic Skin for Detecting and Differentiating Multiple Mechanical Stimuli. Nano Energy 2022, 96, 107115. [Google Scholar] [CrossRef]
  103. Bhalla, M.R.; Bhalla, A.V. Comparative Study of Various Touchscreen Technologies. Int. J. Comput. Appl. 2010, 6, 12–18. [Google Scholar] [CrossRef]
Sensors 24 00449 g001
Figure 1. Diagram of representative design principles, functions, and applications of TCLE.
Figure 1. Diagram of representative design principles, functions, and applications of TCLE.
Sensors 24 00449 g001
Sensors 24 00449 g002
Figure 2. Diagrams of (a) the four basic working modes of TENG (reproduced with permission from ref. [46], Copyright 2018, John Wiley and Sons) and (b) a working cycle of single-electrode mode TENG for tap detection.
Figure 2. Diagrams of (a) the four basic working modes of TENG (reproduced with permission from ref. [46], Copyright 2018, John Wiley and Sons) and (b) a working cycle of single-electrode mode TENG for tap detection.
Sensors 24 00449 g002
Sensors 24 00449 g003
Figure 3. TCLEs utilize different design strategies: (a) sensor matrix (reproduced with permission from ref. [55], Copyright 2023, Elsevier); (b) parallel line array (reproduced with permission from ref. [58], Copyright 2021, John Wiley and Sons); (c) (reproduced with permission from ref. [37], Copyright 2016, American Chemical Society); and (d) (reproduced with permission from ref. [59], Copyright 2019, Elsevier) analog localization.
Figure 3. TCLEs utilize different design strategies: (a) sensor matrix (reproduced with permission from ref. [55], Copyright 2023, Elsevier); (b) parallel line array (reproduced with permission from ref. [58], Copyright 2021, John Wiley and Sons); (c) (reproduced with permission from ref. [37], Copyright 2016, American Chemical Society); and (d) (reproduced with permission from ref. [59], Copyright 2019, Elsevier) analog localization.
Sensors 24 00449 g003
Sensors 24 00449 g004
Figure 4. (a) All-printed sensor matrix for triboelectric multi-point contact localization (reproduced with permission from ref. [67], Copyright 2020, Elsevier). (b) 3D-printed TENG sensor matrix (reproduced with permission from ref. [68], Copyright 2023, KeAi Communications Co. Ltd.). (c) Rough surface-enhanced triboelectric tactile sensor array using parallel line array design strategy (reproduced with permission from ref. [69], Copyright 2023, American Chemical Society). (d) Schematic sketch of the ‘ghost point’ problem.
Figure 4. (a) All-printed sensor matrix for triboelectric multi-point contact localization (reproduced with permission from ref. [67], Copyright 2020, Elsevier). (b) 3D-printed TENG sensor matrix (reproduced with permission from ref. [68], Copyright 2023, KeAi Communications Co. Ltd.). (c) Rough surface-enhanced triboelectric tactile sensor array using parallel line array design strategy (reproduced with permission from ref. [69], Copyright 2023, American Chemical Society). (d) Schematic sketch of the ‘ghost point’ problem.
Sensors 24 00449 g004
Sensors 24 00449 g005
Figure 5. (a) Triboelectric sliding detection bar with the 1D electrode array (reproduced with permission from ref. [73], Copyright 2020, John Wiley and Sons). (b) Gary code-based sliding direction detection panel (reproduced with permission from ref. [39], Copyright 2020, Elsevier). (c) Sliding trajectory detection panel based on sensor matrix design strategy (reproduced with permission from ref. [74], Copyright 2020, Elsevier). (d) Self-powered e-skin for sliding trajectory detection based on parallel line array design principle (reproduced with permission from ref. [75], Copyright 2020, American Chemical Society).
Figure 5. (a) Triboelectric sliding detection bar with the 1D electrode array (reproduced with permission from ref. [73], Copyright 2020, John Wiley and Sons). (b) Gary code-based sliding direction detection panel (reproduced with permission from ref. [39], Copyright 2020, Elsevier). (c) Sliding trajectory detection panel based on sensor matrix design strategy (reproduced with permission from ref. [74], Copyright 2020, Elsevier). (d) Self-powered e-skin for sliding trajectory detection based on parallel line array design principle (reproduced with permission from ref. [75], Copyright 2020, American Chemical Society).
Sensors 24 00449 g005
Sensors 24 00449 g006
Figure 6. Applications scenes of TCLE: (a) flexible touchpad for handwriting digit recognition (reproduced with permission from ref. [89], Copyright 2020, Elsevier), (b) self-powered smart mat system for crew identification (reproduced with permission from ref. [90], Copyright 2022, American Chemical Society), (c) stretchable smart mesh for contact localization (reproduced with permission from ref. [91], Copyright 2021, Elsevier), (d) touch operation panel with sliding trajectory detection function (reproduced with permission from ref. [92], Copyright 2022, the American Association for the Advancement of Science), (e) smart mat for sitting posture recognition (reproduced with permission from ref. [93], Copyright 2022, Elsevier), and (f) wood-based self-powered IoT devices for smart home application (reproduced with permission from ref. [94], Copyright 2022, American Chemical Society).
Figure 6. Applications scenes of TCLE: (a) flexible touchpad for handwriting digit recognition (reproduced with permission from ref. [89], Copyright 2020, Elsevier), (b) self-powered smart mat system for crew identification (reproduced with permission from ref. [90], Copyright 2022, American Chemical Society), (c) stretchable smart mesh for contact localization (reproduced with permission from ref. [91], Copyright 2021, Elsevier), (d) touch operation panel with sliding trajectory detection function (reproduced with permission from ref. [92], Copyright 2022, the American Association for the Advancement of Science), (e) smart mat for sitting posture recognition (reproduced with permission from ref. [93], Copyright 2022, Elsevier), and (f) wood-based self-powered IoT devices for smart home application (reproduced with permission from ref. [94], Copyright 2022, American Chemical Society).
Sensors 24 00449 g006
Table 1. Overview of the working principles of TCLE.
Table 1. Overview of the working principles of TCLE.
Working PrincipleSignal ChannelsWiring Main Error Source
Sensor MatrixLargeComplexMal-triggering of sensor units
Parallel Line ArrayModerateSimple-
Analog LocalizationSmallSimpleNoise, device deformation
Table 2. Summary of characteristics of TCLEs for different applications.
Table 2. Summary of characteristics of TCLEs for different applications.
Application SceneSizeStretchabilityResolutionRef.
Touchpad10 cm × 10 cm-1.25 cm × 1.25 cm[35]
Touchpad8 cm × 8 cm-1.6 cm × 1.6 cm[75]
e-skin1 cm × 1 cm (a unit cell)200%-[101]
e-skin6 cm × 6 cm130% (for a sensor unit)1.4 cm × 1.4 cm[102]
Footstep detection mat1.0 m × 1.15 m-11 cm × 11cm[100]
Smart floor40 cm × 60 cm-8 cm × 12 cm[13]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Xu, W.; Ren, Q.; Li, J.; Xu, J.; Bai, G.; Zhu, C.; Li, W. Triboelectric Contact Localization Electronics: A Systematic Review. Sensors 2024, 24, 449. https://doi.org/10.3390/s24020449

AMA Style

Xu W, Ren Q, Li J, Xu J, Bai G, Zhu C, Li W. Triboelectric Contact Localization Electronics: A Systematic Review. Sensors. 2024; 24(2):449. https://doi.org/10.3390/s24020449

Chicago/Turabian Style

Xu, Wei, Qingying Ren, Jinze Li, Jie Xu, Gang Bai, Chen Zhu, and Wei Li. 2024. "Triboelectric Contact Localization Electronics: A Systematic Review" Sensors 24, no. 2: 449. https://doi.org/10.3390/s24020449

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop