Latest developments and trends in electronic skin devices
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, ... Abstract
The skin, a vital medium for human-environment communication, stands as an indispensable and pivotal element in the realms of both production and daily life. As the landscape of science and technology undergoes gradual evolution and the demand for seamless human-machine interfaces continues to surge, an escalating need emerges for a counterpart to our biological skin - electronic skins (e-skins). Achieving high-performance sensing capabilities comparable to our skin has consistently posed a formidable challenge. In this article, we systematically outline fundamental strategies enabling e-skins with capabilities including strain sensing, pressure sensing, shear sensing, temperature sensing, humidity sensing, and self-healing. Subsequently, complex e-skin systems and current major applications were briefly introduced. We conclude by envisioning the future trajectory, anticipating continued advancements and transformative innovations shaping the dynamic landscape of e-skin technology. This article provides a profound insight into the current state of e-skins, potentially inspiring scholars to explore new possibilities.
Keywords
INTRODUCTION
The skin, the largest organ in the human body, serves not only as a physical protective barrier but also plays a crucial role in perceiving the external environment, regulating physiological functions, and actively participating in immune defense. Throughout the development of human work and life, the skin holds immense significance, forming the fundamental basis for human perception of the surrounding environment[1,2].
Simultaneously, the advancement of human civilization sees a notable trend in integrating intelligent machinery, robots, and manufacturing into our daily lives, ushering in a significant transformation toward the digitization and intelligence of various aspects of human existence. These elements are intricately interconnected, collectively driving a profound change in society[3].
The increasing need for Human-Machine Interfaces (HMI) has led to a notable trend in replicating the sensory capabilities of human skin, facilitated by advancements in electronics and materials science[4-6]. Referred to as electronic skins (e-skins), this innovation holds substantial potential across various domains such as flexible wearable electronics, health monitoring, healthcare, robotics, smart manufacturing, smart homes, and more[7,8].
An electronic skin (e-skin) system needs to possess the following basic characteristics:
First, akin to human skin, e-skins should be flexible, stretchable and tough enough. They need to adapt well to the movement of the torso, machinery, or devices in applications while preserving their sensing function, and thus, some stretchable structures need to be developed to maintain the sensing capability of the active devices[9]. Obviously, flexible stretchable materials are an important foundation; materials such as hydrogel, cellulose, and polydimethylsiloxane (PDMS) are widely used[10-12].
Second, resembling human skin, e-skins need to be carefully designed to provide various sensing capabilities. Examples include the ability to sense stimuli such as strain[13-15], temperature[16,17], and humidity[18-21].
Thirdly, similar to human skin, e-skins should also have good self-healing ability. To better cope with the complex environment, they may face too strong stimuli, which will significantly extend their lifespan[22,23].
Fourth, the e-skins applied in some special scenarios should have certain specific characteristics, such as biocompatibility, self-supply of energy, energy storage, transparency, color-changing capabilities, cold resistance, recyclability, biodegradability, and so on.
Taking a broad perspective, a human skin system involves the coordinated operation of various subsystems, including the skin itself, the brain and nervous system, and the heart and blood circulation system. Similarly, an e-skin system comprises three essential subsystems: a sensor subsystem emulating the functions of human skin, a signal collection/transmission/processing subsystem mimicking the roles of the brain and nervous system, and an energy supply subsystem replicating the functions of the heart and blood circulation.
Within this framework, the sensor system is tasked with detecting pertinent external stimuli or physiological signals and transforming them into measurable electrical signals. The signal collection/transmission/processing system, on the other hand, gathers signals from the sensor system and subsequently transmits and processes them. Lastly, the energy supply system plays a critical role in providing power to these subsystems, with energy collection and supply mechanisms encompassing options such as batteries, triboelectric devices, and piezoelectric devices[24].
Evidently, the sensor system constitutes the foundation of the entire system. The construction of a perceptual capability that is clear and precise across various stimuli stands as a pivotal element in developing an ideal e-skin and, consequently, intelligent systems built upon it. On this basis, multimodal
While several notable scholars have previously provided excellent reviews and commentaries on
This paper aims to integrate and summarize the strategies and research progress of e-skins. Firstly, we take stock of several basic capabilities and the main implementation strategies of e-skins, which include strain sensing, pressure sensing, shear force sensing, temperature sensing, humidity sensing, and self-healing capability. Subsequently, we will briefly introduce complex e-skin systems and current major applications
Figure 1. Overview of electronic skins, including the aspects (basic sensing capabilities, complex e-skins, applications) that are vital to their design. IoT: Internet of things; ML: machine learning.
BASIC SENSING CAPABILITIES OF E-SKINS
The basic sensory capabilities of human skin include five aspects: strain, pressure, shear, temperature, and humidity, of which the combined perception of strain, pressure, and shear constitutes the “tactility”. In addition, human skin can heal itself in response to overpowering stimuli.
The basic implementation strategy of e-skins to perceive these stimuli is to utilize the established physical ground stimulus-response law to design the conductive pathway and structure of the flexible device to achieve the mapping between the external stimulus and the change of the electrical signals in the conductive pathway, and to realize the perception of the corresponding stimulus through the detection of the electrical signals collected by computer. These signals include resistance[29-31], capacitance[32,33], piezoelectricity, semiconductor properties, triboelectricity, and so on.
In this section, we will present the basic implementation strategies and representative works of existing
Pressure sensing capability
There are four main categories of pressure sensors: (1) piezoresistive; (2) capacitive; (3) piezoelectric; (4) triboelectric[34-36] [Figure 2A].
Figure 2. (A) Schematic diagram of common pressure sensing mechanisms: (i) resistive, (ii) capacitive, (iii) piezoelectric, (iv) triboelectric; (B) the microstructure and sensing mechanism of porous MWCNT-PGS sensors[45]; (C) the structure, performance, sensing mechanism of piezoresistive sensors with interlocking microsphere array interlocked microdome arrays structure[46,47]; (D) the preparation process and microstructure of the multi-scale dome/spire + stepped structure pressure sensor[51]. MWCNT-PGS: A biodegradable porous piezoresistive sensor; PDMS: polydimethylsiloxane; PEDOT: poly(3,4-ethylenedioxythiophene).
(1) Piezoresistive conductive composite pressure sensors consist of a stretchable elastomer and conductive fillers, and the resistance of the sensor varies with the compressive strain of the device, sensing the external compressive stress based on its piezoresistivity[37-40]. This basic principle not only works during material compression but also during tensile strain, so some strain sensors also possess a certain degree of pressure sensing capability[40].
Scholars often enhance their performance further through special designs such as multilayers[41,42], patterned conductive paths[43], and bionics[44], which require novel geometrical designs of sensor structures.
Sencadas et al. demonstrated a biodegradable porous piezoresistive sensor (MWCNT-PGS) made of poly(glycerol sebacate) (PGS) blended with multi-walled carbon nanotubes (MWCNTs) for real-time monitoring complex human movements, and the prepared sensor can be degraded by water[45]. The unique porous properties enhance its performance [Figure 2B], and it exhibits high reliability, long lifetime
Inspired by the interlocking microstructure of the epidermal-dermal ridge of human skin, Park et al. developed a piezoresistive interlocked microdome array structure, providing a significant advantage over ordinary planar structure pressure sensors[46,47] [Figure 2C]. This array structure was demonstrated to be highly sensitive to detecting various mechanical stimuli, including normal, shear, tensile, bending and twisting forces. This layered interlocking structure can effectively concentrate the compressive stress near the ridge tips, leading to the deformation of microstructures such as microspheres, and because the contact area between the specially shaped interlocking arrays can be rapidly increased, the pressure sensors of this structure possess higher performance as pressure sensors in low-pressure states[48]. Not only pressure sensing, sensors with this structure also perform well in other sensing fields such as strain, shear force, bending, and torsion[49,50].
Baek et al. demonstrated a maskless method for fabricating flexible pressure sensors through high-precision 3D printing and thin-film coatings of conductive polymers, which can be used to create very precise microstructures[51]. The team demonstrated diverse pressure sensors with complex multi-scale dome/spike + step-like structures, showing extremely high process controllability [Figure 2D]. The prefabricated pressure sensors presented in this work exhibit very sensitive responsiveness (up to 185 kPa-1) and ultra-fast response times (≈ 36 μs), which could be utilized in smart mechanical devices.
(2) Capacitive pressure sensors operate by converting external pressure into a capacitive signal. The capacitance of a medium is known to be defined as:
where ε is the dielectric constant, A is the area of the device, and d is the distance between two conductive plates, and thus the pressure-induced changes in d and A can be used to measure the applied pressure[34,52]. Piezo-capacitive pressure sensors typically have the advantage of high sensitivity and no inherent temperature sensitivity compared to resistive pressure sensors[53-55].
It should be noted that using microstructural design to improve device performance is not exclusive to piezoresistive conductive composite pressure sensors[56-58]. Tee et al. prepared a pyramid-structured capacitive pressure sensor using PDMS material and investigated the compressibility of the PDMS microstructures and the effect of spatial arrangement on the mechanical sensitivity of the microstructured film[59]. It was used as a parallel plate capacitor structure in pressure sensors. The sensors showed potential for applications such as blood pulse monitoring and force-sensing touch panels.
Niu et al. present a facile technique based on a simple melt infiltration process and a commercial cone-shaped nanoporous anodic aluminum oxide (AAO) transfer method for large-scale and low-cost preparation of interlocking asymmetric nanocones on poly(vinylidenefluoride-co-trifluoroethylene) [P(VDF-TrFE)] films[60] [Figure 3A][60-62]. The developed capacitive tactile sensors have excellent performance, including a sensitivity of 6.583 kPa-1 in the low-pressure region (0-100 Pa), a detection limit as low as ≈ 3 Pa, response/recovery times as low as 48/36 ms, and excellent stability and reproducibility (10,000 cycles).
Figure 3. (A) the micro-vertebral structure and basic properties of the P(VDF-TrFE) sensor[60]; (B) the morphology of a conical array of interlaced microstructures that mimic the surface of a plant and the analysis of the sensing array on the stress distribution during a handshake[61]; (C) the piezoelectric fiber structure and sensor device characteristics[62]. PI: Polyimide; P(VDF-TrFE): poly(vinylidenefluoride-co-trifluoroethylene); PDMS: polydimethylsiloxane; PTFE: polytetrafluoroethylene; AgNWs: silver nanowires; PVDF: polyvinylidene fluoride; NFMs: nanostructured functional materials.
(3) Triboelectric pressure sensors utilize the triboelectric nanogenerator (TENG) triboelectric effect and the principle of electrostatic induction to detect pressure changes. In normal operation, this sensor usually contains two or more material layers inside; when subjected to pressure, these layers contact each other and move in the mechanical stress of spontaneous charge separation to generate electrostatic charge. The external pressure stimulus can produce different characteristics of the electrical signal, the contact area, the degree of contact tightness and relative sliding distance, and other factors will affect the characteristics of the generated charge; therefore, by detecting the electrical signal changes, triboelectric pressure sensors can indirectly measure the size of the applied pressure on the sensor. These sensors are self-powered and are suitable for the development of self-powered e-skins[63-66].
It has been reported that designing microstructures on the dielectric surface to provide a larger contact area and more active sites to transfer electrostatic charges during contact electrification can enhance the triboelectric effect. Yao et al. developed a self-powered TENG e-skin sensor mimicking a conical array of interlocking microstructures on the surface of a plant [Figure 3B], an interlocking microstructure that enhances the triboelectric effect[61]. Small polytetrafluoroethylene (PTFE) spines on the friction surface increased the sensitivity of the pressure measurement by a factor of 14. The sensor exhibits very regular and stable sensing properties, and characterization of the pressure distribution during a handshake was achieved by fabricating an array of sensors.
(4) Piezoelectric pressure sensors work by utilizing the piezoelectric effect of materials. When pressure is applied to the sensor, the piezoelectric element generates a charge signal proportional to the pressure, and this weak electrical signal is then amplified and processed by electronic circuits to reflect the pressure. Piezoelectric pressure sensors can likewise be used to create self-powered e-skins[35,67].
Out of the pursuit of smart textiles, a popular strategy is to make fibers with high piezoelectricity[68-70]. We consider that this is, at the same time, conducive to improving the contact area and obtaining better self-powered capability and signal quality[71].
Zhu et al. fabricated an e-skin based on coaxial piezoelectric fibers [nanofibers (NFs)] using electrostatic spinning technology, and the final piezoelectric properties of the NFs were greatly enhanced by introducing inorganic barium titanate nanoparticles (BTO NPs) with high piezoelectricity and graphene oxide (GO) nanosheets into the fibers[62] [Figure 3C]. Subsequently, a three-layer structured e-skin was made using elastic polyurethane (PU) film as the substrate and conductive fabric as the electrodes. A good sensitivity (10.89 ± 0.5 mV·kPa-1 in the pressure range of 80-230 kPa) and stability were demonstrated.
Huang et al. prepared polyvinylidene fluoride (PVDF)/carbon nanotubes (CNTs) incorporated into polyacrylonitrile/CNTs [PVDF/CNTX@PAN/CNTX (DPCPCX)] single-layer binary fiber nanocomposite membranes (SBFNMs) using the co-electrospinning method and fabricated the interpenetrating structures of PVDF@CNT and PAN@CNT nanofiber interpenetrating structures by adding CNTs[72]. The prepared monolayer fabrics possess excellent self-powered capability and exhibit excellent synergistic effects of piezoelectricity and triboelectricity, while the triboelectric conversion is realized by the friction between the binary fibers within the membrane [Figure 4A-D]. The drawbacks of traditional piezoelectric materials are avoided, and significantly higher piezoelectric capabilities are realized.
Figure 4. (A) The device structure, synergistic effect and pressure sensing mechanism; (B) structures of PVDF@CNT and PAN@CNT nanofibers; (C) excellent self-powered capability; (D) hypothetical applications of SBFNMs in self-powered wearable electronic devices[72]. PVDF@CNT: Combination of polyvinylidene fluoride (PVDF) with carbon nanotubes (CNTs); PAN@CNT: combination of polyacrylonitrile (PAN) with carbon nanotubes (CNTs).
Strain sensing capability
Due to the limitations of the underlying principles, capacitive and triboelectric principles are unsuitable for strain sensors, and strain sensors of the relevant principles tend to exhibit unfavorable performance with low sensitivity[73], with resistive strain sensors (RSSs) being the mainstream.
The sensing mechanism of RSSs utilizes the deformation of the device under the action of stress to change the structure of the conductive network inside the sensor device, thus causing corresponding changes to convey the strain information. The characteristics of the conductive network as a function of strain are the core of this type of sensor.
RSSs measure strain by analyzing the relationship between strain and resistance based on the principle that deformation of the material under stress leads to regular changes in the microscopic conductive mechanisms such as crack expansion-repair, fracture, and tunneling effects of the conductive network within the material, resulting in a corresponding change in the macroscopic resistance of the material[74,75].
Based on the substrate used, RSSs can be further classified into two categories: (1) flexible conductive polymer composites (FCPCs)-based; (2) conductive gel-based.
For FCPCs-based strain sensors, there are two main implementation strategies[76]:
(1) Filled-type FCPCs-based RSSs are made by randomly filling a stretchable polymer matrix with conductive fillers to make a conductive elastomer, which is combined with electrodes at both ends to form a tensile strain sensor[77]. Changes in the conductive network cause changes in the resistance signal during stretching, and the strain can be detected based on the correspondence therein[78]. It is favorable for sensing in a large dynamic range and also has the advantages of low cost and simple process[79]. In addition, scholars have begun to consider using sophisticated structural design to further improve performance, such as micro-cracks and micro-contacts[80,81].
Na et al. designed highly durable and recyclable PDMS/vertical graphene (VG)-structured RSSs with ultra-sensitive sensing properties [Figure 5A][82-84], which present a gauge factor (GF) of over 5,000 with only a very small hysteresis and maintained a stable performance after more than 10,000 cycles[82]. This study fully demonstrates that prefabricated cracks in the cluster network structure possess highly reversible resistance changes, especially after the current path is broken and the state can still be restored.
Figure 5. (A) Schematic diagram of the preparation process, structure, performance and application scenario of the PDMS/VG sensor[82]; (B) the preparation process, structure, sensing mechanism and performance of TGASSs[83]; (C) the structure and performance of the PDMS/graphene/PDMS sensor[84]. BFG: buffer flat graphene; VG: vertical graphene; PDMS: polydimethylsiloxane; TGASSs: total graphene artwork strain sensors.
Qiao et al. reported total graphene artwork strain sensors (TGASSs) based on a laser engraving technique that allows the sensor to be conveniently transferred to any other surface[83]. The sensor exhibits excellent performance, including ultra-high sensitivity of up to 673 and a strain range of up to 10% with better stability, and its performance can also be adjusted by tweaking the pattern. It can be used to detect weak physiological signals such as pulse, breathing and sound [Figure 5B].
(2) Sandwich-type FCPCs-based RSSs. Sandwich-type FCPCs-based RSSs are assembled by making a separate conductive layer of conductive material and then sandwiching it between two layers of stretchable film to form a sandwich structure to form a strain sensor[85-87].
The preparation methods of the conductive layer include deposition, spraying, transfer, coating, printing, etc. Due to the rapid evaporation of organic solvents during the preparation of the conductive layer, an extremely large number of microcracks are usually present on the conductive layer. When the sensor is stretched, the microcracks expand, resulting in a degradation of the conductivity of the entire device and an increase in sensor resistance. When the tension is released, the microcracks recover, allowing the conductive channel to recover and the sensor resistance to decrease. Since the crack gap increases significantly with strain, crack-type strain sensors are extremely sensitive to weak strain stimuli and also facilitate a lower detection limit[88]. Therefore, many works tend to pre-stretch the substrate when preparing the conductive layer in order to produce more crack structures[81]. Chen et al. fabricated an innovative PDMS/graphene/PDMS sensor with a more stable sensing network prepared by a one-step solvent evaporation method and an extended temperature monitoring range[84]. The prepared sensor exhibited resistivity dependence on temperature and strain variations [Figure 5C], with temperature and strain monitoring resolutions of 0.5 °C and 0.0625%, respectively, and excellent reproducibility.
Gel-type materials comprise a polymer network and a solution containing a large number of ions and have remarkable flexibility, excellent adhesion to the skin, and strong biocompatibility, making them ideal candidates for the manufacture of sensors and devices that can be attached to the human body or even implanted[89-92]. Based on the material system of polymer networks + ionic solutions, they can be classified into three main categories: hydrogels, organogels, and organohydrogels[93] [Figure 6A].
Figure 6. (A) Various gel materials and their characteristics[93]; (B) Schematic structure and main properties of CS/P(AA-co-AAm)/MXene@PEI/Fe3++Cu2+ nanocomposite dual network hydrogel[94]; (C) the opaque/transparent transition of SN-PAAM dual-responsive ion-conducting hydrogel[95]. KPS: Potassium persulfate; TMEDA: tetramethylethylenediamine; CS/P(AA-co-AAm)/MXene@PEI: Chitosan/poly(acrylic acid-co-acrylamide)/MXene@polyethyleneimine; SN-PAAM: sodium dodecyl sulfate-modified poly(acrylamide) hydrogel.
Xie et al. prepared Chitosan/poly(acrylic acid-co-acrylamide)/MXene@polyethyleneimine [CS/P(AA-co-AAm)/MXene@PEI]/Fe3++Cu2+ nanocomposite bi-network hydrogels by thermal cross-linking, which exhibited excellent properties (including 2.64 MPa tensile strength, 689% elongation at break, 10.25 MJ·m-3 toughness and 1.89 S·m-1 conductivity)[94]. Sensors made with this hydrogel can achieve linear sensing over a strain amplitude of more than 300%, showing excellent potential for diverse applications [Figure 6B]. Lei
Although ionic hydrogel-based strain sensors have the advantages of good flexibility, biocompatibility, and excellent strain sensing performance, their environmental stability has been an intractable problem due to the volatilization, evaporation, and coagulation of the aqueous solvents in the hydrogel, limiting their application areas[96]. Some application scenarios require hydrophobicity[97]. Organo-hydrogel- and ionogel-based strain sensors using organic solvents can alleviate these problems and typically exhibit dominance to freezing, desiccation, and long-term stability[98-100]. Mao et al. developed a poly[oligo(ethylene glycol) methylacrylate-co-acrylic acid]/hydrated ionic liquid [P(OEGMA-co-AA)/HIL] hydrogel with good electrical conductivity, self-healing ability, and frost resistance by a simple in situ photopolymerization strategy[101]. The incorporation of ionic liquid (IL) greatly reduces the freezing point of water, which gives the sensor excellent anti-freezing properties. The temperature dependence of the abundant hydrogen bonding it contains endows this ionic hydrogel with switchable transparency, which allows for the visualization of ambient temperature, demonstrating the potential of the gel material for temperature sensing.
Shear sensing capability
The detection of shear forces by human skin does not only refer to the perception of lateral forces applied to the skin surface but also to the perception of wind and proximity[102]. The bionic micro-spine or hemispherical structures, commonly used in other sensors, can only be used to sense the magnitude of the force, not the direction of the force, and are inapplicable to the needs of shear force sensors. Sensor structures commonly used in the field of shear force sensing are fluff structures[102-104].
Yu et al. developed a capacitive shear force sensor with tilted micro-hair arrays (TMHAs) by mimicking the hair structure of human skin[105] [Figure 7A][105,106]. The asymmetric microhair structure dielectric layer was obtained by the two-photon polymerization (TPP) method, which would allow different deformations due to shear forces in various directions to discriminate the direction of the force. The sensor can determine the direction of static and dynamic shear forces, exhibit a large response scale from 30 Pa to 300 kPa with a relative capacitance change ΔC/C0 < 2.5%, and maintain high stability even after 5,000 cycles.
Figure 7. (A) The structure and sensing mechanism of shear force sensor with TMHAs[105]; (B) the design concept of multi-contact tactile sensors and mechanism of shear resolution[106]. UV: Ultraviolet; PVA: polyvinyl alcohol; PET: polyethylene terephthalate; PDMS: polydimethylsiloxane; TMHAs: tilted micro-hair arrays.
In addition, the recognition of the direction of shear force can be realized using the strategy of multi-sensor array + array structure design + signal processing[107]. Zhu et al. proposed a haptic sensor based on a multi-touch mechanism using a structure of PDMS/MWCNTs with conductive and curved surfaces, which sequentially contact the resistive columns on the micro-honeycomb electrodes (MHEs) according to the pressure to maintain proper piezoresistive characteristics [Figure 7B], and the whole sensor is characterized by high sensitivity, high linearity, good robustness, and wide dynamic range[106]. By adjusting the contact distribution density and the curvature of the PDMS/MWCNTs contact, the sensitivity and detection range can be tuned (500 kPa + 64.68 kPa-1 for high sensitivity mode, 1,400 kPa + 25.88 kPa-1 for wide range mode). The possibility of realizing normal and force measurements was demonstrated by fabricating a triaxial tactile sensor capable of sensing normal force (0-3.5 N) and shear force (0-1.5 N) with sensitivities of 58.097 and 36.137 N-1.
Temperature sensing capability
Temperature sensing capability provides early warning of burn/frostbite risk and is important for the skin system to maintain intact function. There are four main types of sensing mechanisms for temperature sensors: resistive[108,109], capacitive[110], piezoelectric, and triboelectric[111,112]. Resistive temperature sensors are the most common type[113,114]; their basic principle is that the resistance of the device changes with temperature due to the thermal transport/scattering mechanism of the thermosensitive material and the change in geometry with temperature[115,116]. One of the difficulties of temperature sensors is achieving a high level of resolution.
Li et al. prepared a PEDOT-TPU composite fiber (PTCF) temperature sensor by growing poly(3,4-ethylenedioxythiophene) (PEDOT) in situ on the surface of thermoplastic PU (TPU) fibers using in situ polymerization[117]. The sensor exhibited a sensitivity as high as 0.95% °C-1 and a high linearity between 20 and 40 °C. Additionally, the best part is the temperature resolution of up to 0.2 °C. The temperature-sensing fibers can be easily embedded in textiles. By sewing the fibers in an S-shape into normal textiles, strain disturbances can be virtually avoided, even when the textile is stretched to 140%.
Temperature changes can lead to variations in ion mobility, so gel materials that contain a large number of ions and are predominantly ion-conductive can also be designed as temperature sensors[118-120]. Generally speaking, as the temperature rises, the enhanced ion dissociation increases the concentration of charge carriers, further boosting ion conductivity and enhancing conductivity. The relationship between ion conductivity and surrounding temperature can be characterized using the Vogel-Tamman-Fulcher (VTF) equation[121]:
where σ(T) represents the ion conductivity; σ∞, B, and TV are material constants determined by the material.
Ge et al. proposed a strain-temperature dual-sensing hydrogel sensor inspired by the fiber structure of human muscles[122] [Figure 8A]. Polyaniline (PANI) NFs interwoven in the hydrogel enhance the self-healing ability and form strong hydrogen bonds and a “dynamic zipper” effect. The addition of glycerol inhibits water crystallization, improves freeze protection, and further promotes the sensor’s sub-zero temperature self-healing, water retention, and adhesion properties. Thanks to the high thermal sensitivity of PANI NFs, the sensors exhibit excellent temperature coefficient of resistance (TCR) and temperature resolution (2.7 °C).
Figure 8. (A) The structure and temperature sensing properties of the muscle fiber-inspired strain-temperature dual sensing hydrogel sensor[122]; (B) the preparation method, structure and temperature sensing properties of the IL/TPU ionogel fibers[123]. PAA: Polyacrylic acid; PANI: polyaniline; ILs: ionic liquids; TPU: thermoplastic polyurethane.
Wang et al. prepared an IL/TPU ionogel fibrous sensor consisting of TPU and IL using wet spinning techniques[123] [Figure 8B]. In addition to the excellent strain sensing properties, it can be made into a strain-sensitive temperature sensor with an S-shape. The temperature sensing error is within 0.15 °C when the sensor is simultaneously subjected to 30% tensile strain. Thanks to the fast and stable thermal response of IL, the temperature sensors show a monotonic temperature response over a wide temperature range (-15 to 100 °C) with a detection accuracy of 0.1 °C.
Humidity sensing capability
Information such as ambient humidity and sweat content on the skin surface is obviously important. Humidity sensing capabilities have significant application potential in biomedical, environmental monitoring and smart home scenarios[124-127]. A humidity sensor is a device that can detect the moisture content of the environment or the surface of an object and generate a characteristic electrical signal. According to the sensing mechanism, humidity sensors can be categorized as: resistive, piezoelectric, capacitive, optical, and semiconductive[128-131].
Humidity sensors using semiconductor heterojunctions can exhibit sensitive humidity sensing capabilities. Hou et al. prepared one using borophene-MoS2 heterostructures, which has ultra-high sensitivity [up to 15,500% at a relative humidity (RH) of 97%][132], fast response, long lifespan, and good flexibility. In their recent work, the high-performance humidity sensor using a Borophene-BC2N quantum dot heterostructures exhibits stronger performance[133], with an ultra-high sensitivity (up to 22,001% at 97% RH), wide detection range (11%-97%), low hysteresis, and extremely excellent stability. The advantage is best highlighted by the fact that the boronene-BC2N heterostructure is 100 or 20 times more sensitive at 97% RH at room temperature compared to boronene (α′-4H-borophene) or BC2N quantum dots alone. This sensor has strong potential for applications in flexible wearable and smart homes. It can be applied to diaper monitoring for infants and critically ill patients, wireless monitoring of respiratory behavior and speech recognition by detecting humidity in exhaled breath, and contactless switching by detecting humidity values on the surface of fingertips [Figure 9A].
Figure 9. (A) heterostructures, flexible wearable application, and humidity sensing performance of Borophene-BC2N quantum dot humidity sensor[133]; (B) the microstructure, strain sensing and humidity sensing properties of XSBR/CA/AgNPs conductive films[134]; (C) the microstructure, humidity sensing performance, and application scenarios of the SAMP sensor[135]. RH: Relative humidity; NPs: nanoparticles; CA: citric acid; XSBR: carboxylated styrene-butadiene rubber; AMP: alkalized MXenes/polydopamine; SBS: poly(styrene-block-butadienstyrene; NF: nanofiber.
Resistive humidity sensors are the most common of these and typically consist of a conductive element and a hydrophilic element [e.g., WS2, polyimide (PI), polyvinyl alcohol (PVA), citric acid (CA), and hydroxyl ethyl cellulose (HEC)][134-137]. When moisture is absorbed by the sensor, it causes a change in the conductive path of the conductive element, resulting in an alteration in resistance or current[138]. Xu et al. prepared a carboxylated styrene-butadiene rubber (XSBR)/CA/silver nanoparticles (AgNPs) conductive film, where the Ag NPs formed conductive pathways by in situ diffusion in the XSBR matrix[134]. Due to the hygroscopicity of CA, this conductive film is sensitive to various humidity levels [Figure 9B].
A clear trend is the preparation of more skin-conformable, breathable humidity sensors, which is manifested in nascent research by applying special strategies such as nanomesh[139,140], porous[141], fiber/textile[142], and paper matrices[143]. On the one hand, these strategies improve the comfort of wearing. On the other hand, they also increase the chance of contact between the sensor and the moisture to improve the sensor performance (high specific surface area)[144].
Li et al. proposed a humidity sensor (SAMP) based on poly(styrene-block-butadienstyrene) (SBS) NFs and alkalized MXenes/polydopamine (AMP) composites[135] [Figure 9C]. The SBS NFs provide an ultrathin, highly flexible and breathable substrate, and the skin-conformable, breathable, and sensing properties of this sensor have achieved significant progress due to its large specific surface area and abundant water-absorbing hydroxyl groups. The sensor has a Young’s modulus (~0.10 MPa) similar to human skin
Self-healing capability
Self-healing capability is an indispensable natural repair mechanism in the human life system, which is of vital significance to daily life, and enables the human body to recover from injuries, thus effectively safeguarding the health of the body. Similarly, it can greatly expand the lifespan and application scenarios of e-skins[145-148]. The basic strategy for this capability of e-skins is to fabricate e-skins using intrinsic self-healing polymers (SHPs)[149-151]. Such polymers are generally rich in reversible intermolecular interactions or dynamic covalent bonds within them and consist of two main types of materials: repairable plastics[152-155] and gels[156-158].
Liu et al. reinforced poly(vinylidene fluoride-co-hexafluoro propylene) (PVDF-HFP)/fluorosurfactant (FS3000) film with electro-spun PVDF-NFs to make PVDF-NFs/PVDF-HFP/FS3000 self-healing substrates[159]. This nanofiber-reinforced self-healing substrate has 836% and 1,000% higher tensile strength and toughness, respectively, than the pure PVDF-HFP/FS3000 substrate, and also shows greatly improved healing efficiency, allowing self-healing within 24 h at room temperature. Subsequently, e-skin devices were fabricated with the self-healing substrate and two functional layers (detection and heating layers). What is more attractive is that they also designed a related program that collects signals from the detection layer and automatically sends commands to the heating layer when the device is damaged by external forces, accelerating the healing process through Joule heat, demonstrating a destruction-healing process that is closer to that of the human body [Figure 10A][159-161].
Figure 10. (A) the self-healing process and resistance change of the PVDF-NFs/PVDF-HFP/FS3000 e-skin[159]; (B) the structure and healing mechanism of the Clay/P(MEO2MA-co-OEGMA) sensor[160]; (C) the structure of PDMS/MWCNT+LM capacitive strain sensing e-skin[161]. Laponite XLG Clay: 92.32 wt.% [Mg5.34Li0.66Si8O20(OH)4]Na0.66 and 7.68 wt.% Na4P2O7; KPS: potassium persulfate; TEMED: N,N,N′,N′-tetramethylethylenediamine; GW: glycerol-water cosolvent; LM: liquid metals; RT: room temperature; PVDF: polyvinylidene fluoride; NFs: nanofibers; HFP: hexafluoro propylene; P(MEO2MA-co-OEGMA): poly(methoxyethyl acrylate-co-oligo(ethylene glycol) methacrylate; PDMS: polydimethylsiloxane.
Gel materials, with a large number of hydrogen-bonded dynamic cross-links and supramolecular interactions within, possess a good self-healing ability, which, coupled with excellent biocompatibility, adhesion, and ductility, makes it an ideal candidate for self-repairing sensors[162-165].
Wei et al. prepared a Clay poly[2-(2-Methoxyethoxy) ethyl methacrylate-co-oligo(ethylene glycol) methacrylate] [Clay/P(MEO2MA-co-OEGMA)] conductive gel with glycerol-water as a binary solvent by in situ gelation on the clay surface[160] [Figure 10B]. The prepared gels show high flexibility, extensibility (elongation ~600%), and conductivity (~3.32 × 10-4 S·cm-1), and the abundant hydrogen bonding interactions between the clays and the P(MEO2MA-co-OEGMA) chains contribute to a good self-healing ability (healing efficiency of 84.8%).
On the other hand, the self-healing function of e-skins needs to repair not only mechanical damage but also damage to the conductive pathways inside the device[166-168]. A fashionable approach is to use liquid metals (LM) with high conductivity and fluidity, such as eutectic gallium indium (EGaIn) alloys, Fe-particle doped EGaIn (Fe-EGaIn), to make e-skins[169-174]. The LM will form a shell with the surrounding resin matrix and air, encasing it inside. When the device is damaged, this shell ruptures and the LM inside will flow out,
Chen et al. fabricated a self-healing resin matrix + LM capacitive strain sensing e-skin by combining nanoimprinting and printing technologies [Figure 10C] MWCNT-reinforced reversible imine-bonded self-healing poly (dimethylsiloxane) elastomers (PDMS/MWCNT) were used as the substrate, and their good imprintability was utilized to prepare embedded LM patterned circuits, which were assembled to form a capacitive strain sensor that is fully stretchable and self-healing[161]. The related e-skin possesses extremely strong self-healing properties, with a healing efficiency of 94.3% in 1 h for the PDMS/MWCNT matrix alone.
Shi et al. report a heterogeneously integrated e-skin system of dynamically covalently bonded polyimide (encapsulation and matrix) + liquid eutectic LM (electrical and sensing) + chip (information processing), which simultaneously provides full recyclability, excellent mechanical stretchability, self-healing and reconfigurability[181]. The polyimide matrix with an active bond exchange reaction network and the fluidity of LM enable this e-skin system to self-heal from damage and re-adapt to the current application scenario. All materials and components involved are recyclable.
Brief summary
This section summarizes the six most common basic capabilities of e-skins: pressure, strain, shear, temperature, humidity, and self-healing.
Overall, resistive sensing mechanisms are the most used, thanks to their easy and reliable detection and the rich possibilities in device architecture design. In addition, e-skins employing triboelectric and piezoelectricity can be developed into self-powered e-skins, which can be used in the future as power supply units in integration with other e-skins.
From the material point of view, e-skins can be classified into two main categories: FCPCs and conductive gels. FCPCs are classified into polymer matrix and conductive materials. For the former, flexible polymer materials, such as PDMS, TPU, SBS, cellulose, paper, etc., are widely used[182], in which PDMS is popular for its excellent flexibility, transparency, and process adaptability. Commonly used conductive materials include one-dimensional materials, such as silver nanowires (AgNWs), copper nanowires (CuNWs), and CNTs, two-dimensional materials, such as reduced GO (rGO) and MXene[183], and highly conductive materials such as Ag NPs, LM, and carbon black (CB).
E-skins constructed using conductive gel materials have a higher sensing range but tend to exhibit larger response time, and lower durability, which comes from their traditional characteristics such as low modulus and low toughness. However, they tend to be irreplaceable in terms of biocompatibility and adhesion, and scientists have also been trying to improve their performance and shortcomings. Their performance has been greatly improved in recent years. A typical example is the “Salting Out-Alignment-Locking” design recently adopted by Sun et al. to greatly improve the tensile strength, modulus and toughness of gelatin hydrogels. The tensile strength, modulus, and toughness of gelatin hydrogels can be increased up to 940, 2,830, and 1,785 times, respectively[184].
The structure, material, mechanism and main characteristic parameters of some classic e-skins have been listed in Table 1.
Structure, material, mechanism and main characteristic parameters of some classic e-skins
| Sensing type | Material | Structure | Sensing mechanism | Sensitivity | Sensing range | Respond time [trigger/release] | Durability | Ref. |
| Pressure* | PANI coated rGO on textile substrate | Filled-type FCPCs | Resistive | Sp = 97.28 kPa-1 | 0.0005~40 kPa | 30/25 ms | 11,000 | [40] |
| Pressure | CB/TPU | Porous filled-type FCPCs | Resistive | Sp = 1.55 MPa-1 | Max = 584.4 kPa | 150/150 ms | 10,000 | [42] |
| Pressure | CNT-PGS foam | Porous filled-type FCPCs | Resistive | Sp = 8.00 ± 0.20 kPa-1 | 0.1~8 kPa | ≤ 20 ms | 220,000 | [45] |
| Pressure* | CNTs/PDMS | Interlocked microdome array | Resistive | Sp = 2.21 N-1 | 0.1~20 kPa | - | - | [46] |
| Pressure | CNTs/PDMS | Interlocked microdome array | Resistive | Sp = -15.1 kPa-1 | 0.0002~59 kPa | ~ 40 ms | - | [47] |
| Pressure | PEDOT on a 3D-printed substrate | Interlocked microdome array + nano contacts | Resistive | Sp = 184.82 kPa-1 (< 10 kPa) | 0.06~39.2 kPa | 0.038/0.045 ms | 1,000 | [48] |
| Pressure | ZnO nanowires array on PDMS substrate | Interlocked microdome array + nano contacts | Resistive | Sp = -6.8 kPa-1 | Min = 0.0006 kPa | < 5 ms | 1,000 | [49] |
| Pressure* | Wrinkle CNTs (humidity sensing part)/PDMS on a porous CNT/PDMS substrate (pressure sensing part) | Porous | Resistive | Sp = 0.537 kPa-1 (< 3 kPa), 0.007 kPa-1 (> 3 kPa) | - | 78/62.5 ms | 10,000 | [50] |
| Pressure* | CNTs/PDMS-Ni electrodes/resistor array/PI dielectric/Ni electrodes | Resistor array + Micro-honeycomb electrodes | Resistive | Sp = 25.88 kPa-1 (large range mode), 64.68 kPa-1 (high sensitivity mode) | Max = 1,400 kPa (large range mode), 500 kPa (high sensitivity mode) | 133/73 ms | 2,000 | [106] |
| Pressure | SBS/AgNP coated-kevlar fiber textiles | Capacitive fiber textiles | Capacitive | Sp = 0.21 kPa-1 | Max = 3.9 MPa | < 10 ms | 10,000 | [54] |
| Pressure | Ag pyramid array (top electrode)/ATMP-PVA hydrogel film (dielectric layer)/Ag (bottom electrode) | Pyramid array electrode + dielectric layer | Capacitive | Sp = 3,224.2 kPa-1 | Max = 100 kPa | ~6 ms | 200 | [56] |
| Pressure | PI/interlocked P(VDF-TrFE)/Cu | Interlocked pyramid array | Capacitive | Sp = 6.583 kPa-1 | Min = 3 Pa | 48/36 ms | 10,000 | [60] |
| Pressure* | TPU/MXene/AgNWs-TPU- TPU/MXene/AgNWs | Two filled-type FCPCs sandwiched a dielectric layer | Capacitive | Sp = 0.029 kPa-1 | 0~70 kPa | 260/- ms | 2,000 | [73] |
| Pressure* | Carbon fabric + CIP/PDMS flagella array + carbon fabric | Parallel plate + unilateral flagella array | Capacitive | Sp = 0.0124 kPa-1 | 0.002~100 kPa | ~100/100 ms | ~2,000 | [103] |
| Pressure* | rGO/PDMS | Parallel plate + unilateral tilted microhair array | Capacitive | Sp = 0.0513 kPa-1 (0.03 < 6 kPa), 0.0079 kPa-1 (6~120 kPa), 0.0009 kPa-1 (120~300 kPa) | 0.03~300 kPa | 60/54 ms | 5,000 | [105] |
| Pressure | PDMS/PTFE/AgNWs/PDMS TENG | Interlocked conical array | Triboelectric | Sp = 127.22 mV·kPa-1 | 5~50 kPa | - | 5,000 | [61] |
| Pressure | Nylon/PDMS pyramid array/TPU/AgNWs TENG | Pyramid array | Triboelectric | Sp = 9.973 mV·Pa-1 (0-1.6 kPa), 0.538 mV·Pa-1 (> 1.6 kPa) | Max > 6 kPa | - | 1,000 | [66] |
| Pressure | GO-PVDF/BTO Piezoelectric NFs textiles + PU substrate | Piezoelectric fiber textiles | Piezoelectric | Sp = 10.89 ± 0.5 mV·kPa-1 | 80~230 kPa | - | 8,500 | [62] |
| Strain* | PANI coated rGO on textile substrate | Filled-type FCPCs | Resistive | GF = -78 | Max = 10% | - | 1,000 | [40] |
| Strain* | CNTs/PDMS | Interlocked microdome array | Resistive | GF = 27.8 (0%-40%), 1,084 (40%-90%), 9,617 (90%-120%) | Max = 120% | 18/10 ms | - | [46] |
| Strain | AgNW/DS on strain-engineered stretchable substrate | Patterned circuit | Resistive | GF = ~4.5 (0%~25%), ~33 (25%~68%) | 0%~68% | - | 1,000 | [80] |
| Strain | Pre-stretching CNTs@TPU fiber felt | Micro-cracks + micro-contacts | Resistive | GF = 428.5 (0%~100%), 9,268.8 (100%~220%), 83,982.8 (220%-300%) | 0%~300% | 70/70 ms | 10,000 | [81] |
| Strain | Vertical graphene on PDMS | Micro-cracks | Resistive | GF > 5,000 | 0%~30% | 200/100 ms | 10,000 | [82] |
| Strain | Laser scribed graphene | Micro-cracks | Resistive | GF = 11 (0%~2.5%), 92 (2.5%~4.5%), 673 (4.5%~5%) | 0%~10% | - | 1,000 | [83] |
| Strain | PDMS/GO/PDMS | Sandwich-type FCPCs + micro-cracks | Resistive | GF = 2,175.8 | - | - | 1,000 | [84] |
| Strain | NR-NR/CNT-NR | Sandwich-type FCPCs + micro-cracks | Resistive | GF = 2,280 | Max = 500% | 21 ms | 2,000 | [85] |
| Strain | PDMS/GO/PDMS | Sandwich-type FCPCs + micro-cracks | Resistive | GF = 1,054 | Max = 26% | - | 500 | [87] |
| Strain | Ti3C2Tx MXene/CSPMXPy-x/Fe3++Cu2+ hydrogels | Mxene filled-hydrogels | Resistive | GF = 4.64 | Max > 300% | - | 100 | [90] |
| Strain | SN-PAAM hydrogels | Conductive hydrogels | Resistive | GF = 1.62~2.46 | Max = 1,836% | 120~130 ms | 200 | [91] |
| Strain | PVA/NaCl hydrogels | Conductive hydrogels | Resistive | GF < 2.1 | 0.5%~100% | - | 200 | [93] |
| Strain | PU-IL ionogels | Conductive ionogels | Resistive | GF = 1.54 | 0.1%~300% | - | 1,000 | [100] |
| Strain* | PPBN-hydrogel | Conductive hydrogels | Resistive | GF = 18.28 | 6%~268.9% | - | 1,000 | [122] |
| Strain* | ILs/TPU ionogel fiber | Conductive ionogel fiber | Resistive | GF = 3.35 | 0.05%~700% | 170/120 ms | 2,000 | [123] |
| Strain* | TPU/MXene/AgNWs-TPU- TPU/MXene/AgNWs | Two filled-type FCPCs sandwiched a dielectric layer | Capacitive | GF = 1.21 | 0%~150% | 210/280 ms | 2,000 | [73] |
| Shear* | CNTs/PDMS-Ni electrodes/resistor array/PI dielectric/Ni electrodes | Pressure sensor array | Resistive | Ss = 36.137 N-1 | 0-1.5 N | - | - | [106] |
| Shear* | Carbon fabric + CIP/PDMS flagella array + carbon fabric | Parallel plate + unilateral flagella array | Capacitive | Ss = 0.0752 N-1 (0.015~0.50 N), 0.0177 N-1 (0.50-1.30 N) | 0.015~1.3 N | - | - | [103] |
| Shear* | rGO/PDMS | Parallel plate + unilateral tilted microhair array | Capacitive | Ss = -0.0134 kPa-1 (left), 0.0195 kPa-1 (right) | 1.5~30 kPa (left) 0.3~6 kPa (right) | - | 1,000 | [105] |
| Temperature | PEDOT@TPU fiber | Core-shell fiber | Resistive | TCR = 0.95% °C-1 | 20~40 °C | 30/27 s | - | [117] |
| Temperature* | PPBN-hydrogel | Conductive hydrogels | Resistive | TCR = 1.64% °C-1 | 40~110 °C | - | - | [122] |
| Temperature* | ILs/TPU ionogel fiber | Conductive ionogel fiber | Resistive | TCR = 3.28% °C-1 | -15~100 °C | 645/- ms | - | [123] |
| Humidity* | Wrinkle CNTs (humidity sensing part)/PDMS on a porous CNT/PDMS substrate (pressure sensing part) | Wrinkle surface | Resistive | Sh = 0.0055/RH % (15%-60% RH), 0.0323/RH % (60%-85% RH) | Max = 85% RH | - | - | [50] |
| Humidity | AgNWs/etched Al foil/SiO2 moisture sensitive layer/Cu | Multilayer fabric and porous structure | Resistive | Sh = 0.3007/RH % (10%~70% RH), 13.1667/RH % (70%~92% RH) | 10%~92% RH | 25/55 s | 3 | [126] |
| Humidity | SBS nanofibers/AMP | Moisture sensitive fiber felt | Resistive | R = 704 | 10%~95% RH | 0.9/0.9 s | 5 | [138] |
| Humidity | WCN | Moisture sensitive capacitor bridge | Capacitive | Sh = 4.479 (20%~94% RH) R = 2,152 | 7%-94% RH | 20/6 s | - | [129] |
| Humidity | Borophene-MoS2 | Borophene-MoS2 heterostructures | Semiconductive | Sh (%) = 15,500% | 0%~97% RH | 2.5/3.1 s | [132] | |
| Humidity | Borophene/BC2N QD | Borophene-BC2N QD heterostructure | Semiconductive | Sh (%) = 22,001% | 11%~97% RH | 11.82/1.41 s | - | [133] |
COMPLEX E-SKIN SYSTEMS
In fact, the perception of human skin of external stimuli is seldom a single sense or sensing unit acting alone. Typically, for example, when a drop of water is dropped on our skin, most of the time, it is perceived through a combination of temperature and humidity sensing, plus pressure sensing if the drop is heavy enough and falling fast. On the other hand, all responses and processing of stimulus signals require the intervention of information processing units such as the nervous system so that more advanced human-environment interactions can be achieved (for e-skins, it is HMI). Therefore, single-sensing systems are insufficient, and the scientists have been conducting research on complex e-skin systems such as multimodal, IoT-integrated, and ML-enabled e-skin systems.
Multimodal e-skin systems
Integration of multiple sensing capabilities (multimodal e-skins) is necessary to achieve a high level of sensing capability[37,185,186]. Scholars have fabricated excellent multimodal e-skin systems through exquisite material and structural design[187-190].
Some scholars prepare multimodal e-skins with multifunctional materials, which embody two or more sensing properties[191]. However, it is difficult for such multimodal e-skins to achieve excellent comprehensive performance, and it is difficult to avoid interference between various sensing characteristics. Various sensing characteristics often cannot reflect outstanding performance at the same time.
In contrast, integrating multiple types of sensing devices with superior performance into the same e-skin device can achieve better overall performance. In such multimodal e-skins, dealing with the interference problem between different sensing characteristics remains an important issue[192], which is a major shackle restricting the performance of the multimodal e-skin system[112,117,123,193].
A common solution is to independently integrate multiple sensors into a single e-skin device, with each sensor having its own dedicated independent signal transmission line[194]. As shown in [Figure 11A], Zhao
Figure 11. (A) The multimodal sensing characterization and mechanism of the spider web-like flexible tactile sensor[195]; (B) the structure, multimodal sensing properties (temperature, humidity, plane strain, ultraviolet, magnetism, etc.), and preparation process of a highly flexible and stretchable matrix network e-skin[196]; (C) composition of the skin-inspired piezoelectric tactile sensor array system and comparison with human skin[197]. PDMS: Polydimethylsiloxane; SET: stretchable electrode; PI: polyimide; PVA: polyvinyl alcohol; PVDF: polyvinylidene fluoride; ADC: analogue-to-digital converter.
Hua et al. prepared a highly stretchable conformal matrix network (SCMN) on a polyimide network [Figure 11B], which can be fabricated into a multisensory e-skin able to simultaneously sense multiple stimulus signals through the selection of different sensing components, including temperature (Pt-sensitive layer), humidity (Al/PI-sensitive layer), plane strain (Constantan-sensitive layer), ultraviolet (UV, Al/ZnO-sensitive layer), magnetism (Co/Cu multilayer-sensitive layer), pressure, and proximity, thereby realizing multimodal sensing with a tunable range of sensing and large scalable area[196].
Such structures can avoid interference problems, but they also bring about the problem of complex structure, which is disadvantageous in the development trend of high density and miniaturization. Some scholars avoid this interference by carefully designing the structure of the e-skin systems. Lin et al. reported a piezoelectric tactile sensor array with both pressure sensing and bending sensing capabilities[197] [Figure 11C]. The specially designed insulating layer and structural design of the row + column electrodes can greatly alleviate the crosstalk problem in other sensors. A signal processor and logic algorithms were also integrated to enable real-time sensing and differentiation of the magnitude, location, and pattern of various external stimuli, including gentle sliding, touching, and bending. Pressure sensing and bending sensing tests show that the proposed haptic sensor array has high sensitivity (7.7 mV·kPa-1), long-term durability (80,000 cycles), and faster response time (10 ms) than human skin.
IoT-integrated and ML-enabled e-skin systems
In recent years, the integration of e-skins in the IoT has become a clear trend, as it allows for the rationalization of e-skins with other cutting-edge technologies, such as advanced mechanics and ML methods, to create intelligent systems, which is the key to allowing e-skin to be truly integrated into human-machine systems. Advances in this field are expected to open up entirely new applications in areas such as health monitoring, wearable electronics, and robotics[198].
The basic strategy is to use hardware devices with information processing capabilities, such as Micro-Controller Unit (MCU), Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), etc., as the control center to receive sensing signals and process them through specific program burned before. In this way, e-skins can be combined with other components (such as actuators, power supplies, and other
Zhang et al. developed carboxyethyl chitin/polyacrylamide (CECT/PAM) hydrogels with high transparency (92%), high conductivity (0.62 S·m-1), ultra-flexibility (strain up to 1,586%, toughness up to 1,300 kJ·m-3), good fatigue resistance, and strong adhesion ability and used them to make wearable devices including RSSs, capacitive pressure sensors, and TENGs with good performance[199]. Sign language recognition and spatial perception of pressure were achieved using an STC89C52 MCU to process data from the hydrogel-based strain sensor and pressure sensor array [Figure 12A][199,200].
Figure 12. (A) Schematic diagram of (i) composition of the monolithcic integrated sensing system based on CECT/PAM hydrogels, (ii) the signal processing circuit, (iii) array of pressure sensors under four-finger contact and the corresponding signaling diagram, (iv) wearability of the e-skin system and the signaling diagram[199]; (B) Schematic diagram of (i) process from sensing to signal processing, (ii) gesture recognition system and its recognition results[13]; (C) Schematic diagram of (i) composition of NWF/AgNWs-MXene/PBSE multi-sensor system, (ii) structure of ResNet18 signal classification neural network, and (iii) various grasping actions and their classification results[200]. MCU: Micro-Controller Unit; CECT: carboxyethyl chitin; PAM: polyacrylamide; NWF: nonwoven fabrics; AgNWs: silver nanowires; PBSE: polyborosiloxane elastomer.
Chen et al. successfully simulated the force sensing system of human body by combining an In-doped ZnO memristors artificial synaptic device with Pt/CNFs strain sensors for stimulus detection and information processing using an Arduino Leonardo (ATmega32U4 chip) MCU[13] [Figure 12B]. The Pt/CNFs strain sensors applied the microcracking principle, where microcracks formed during stretching are bridged by the carbon NFs (CNFs), allowing the sensor to finely detect human motion and convert mechanical stimuli into electrical signals. The In-doped ZnO memristors synaptic device can mimic various basic properties of neuronal synapses and further process the information from the Pt/CNFs strain sensors based on the Spike-Rate-Dependent Plasticity (SRDP) behavior (which is actually a form of ML at the hardware level) after the MCU has converted the sensor signals into a sequence of pulses with different frequencies. By installing five strain sensors on the finger joints, it is demonstrated that the system can recognize gestures with high accuracy.
ML methods can greatly enhance the intelligence level of an e-skin system, which can greatly improve the performance of human-machine interfaces (HMI), and show a broad application prospect in medical health, rehabilitation therapy and remote monitoring[201-204]. They can learn feature signals corresponding to a certain stimulus from a large amount of experimental data, which can recognize different types of stimuli (such as gesture, touch strength, texture, and shape)[205-208].
ML models need to be trained on collected data before they can be put into use, and a signal collection system is necessary[209]. Therefore, ML-enabled e-skin systems are often further developed based on IoT-integrated e-skin systems.
Wang et al. constructed a wireless sensing grasping action recognition system using an MCU to collect signals from a prepared nonwoven fabrics (NWF)/AgNWs-MXene/polyborosiloxane elastomer (PBSE) multi-sensor system and combined it with a deep learning method to achieve accurate grasping action recognition[200] [Figure 12C].
Wang et al. built an artificial neural network (ANN) with a fully-connected structure to learn the data from a large number of tiny tactile sensors on the prepared tactile sensing gloves and realized the recognition of objects with different hardnesses and shapes, grasping motions for four kinds of fruits (with the highest recognition accuracy up to 99.26%), and grasping motions for seven kinds of objects ranging from soft to hard (with the highest recognition accuracy up to 99.35%)[210].
Niu et al. built an intelligent material sensing system by building a multilayer perceptron (MLP) neural network and making it learn the collected capacitive response data of the all-fabric bionic (AFB) e-skin to different materials in proximity and pressure modes[211]. The system can accurately discriminate nine materials with fuzzy morphology and smooth surfaces by the differences in dielectric constants and hardness of the materials, with an average accuracy of 96.6%.
Kim et al. presented a novel e-skin system integrated with a deep neural network[212]. It has a fine laser-induced crack structure to detect small deformations, captures dynamic motion without creating a sensor network, and, in combination with a deep neural network, a single skin sensor can discriminate the motion of the corresponding body part.
Tan et al. built a bioinspired spiking multisensory neural network (MSeNN) that integrates vision, touch, hearing, simulated smell and taste with cross-modal learning via ANNs[213]. Through distributed multi-sensors and bionic layered architecture design, it not only senses, processes, and memorizes multimodal data but also fuses multisensory data at both hardware and software levels.
APPLICATIONS OF E-SKINS
Scholars have been working hard to expand the application areas of e-skins. Here, we will briefly introduce the applications of e-skins, which can be simply divided into two major application fields: (1) wearable electronics and healthcare and (2) intelligent machinery. The former is the field of most concern. It is worth mentioning that there are not very clear boundaries between different applications.
Wearable electronics and healthcare
E-skins are invented to mimic human skin, making them naturally suitable for use in the healthcare and wearable fields[36]. The application environments often require the corresponding e-skin devices to possess various qualities such as non-toxicity, harmlessness, high flexibility, strong biocompatibility, chemical stability, comfort, high adhesion, and reconfigurability. Materials such as gels, soft metals (mainly precious metals with high chemical stability in physiological environments, such as Ag and Pt), PDMS, PI, and cellulose are widely used[5]. Typical application scenarios include detection and recognition of human limb movements, continuous monitoring of signals such as electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), etc., and detection of human body fluids (sweat, water vapor, excretory fluids, etc.)[214].
As shown in [Figure 13A], Gao et al. fabricated a composite hydrogel [poly(vinyl alcohol)/microfiber composite hydrogel (PVA/MF-CH) and PVA/MF/Glycerol-CH (PVA/MF/Gly-CH)] composed of PVA hydrogel and PU microfiber network embedded using electrospinning and spin-coating[215]. In addition to the common excellent ionic conductivity, freezing resistance, and dehydration resistance, their thickness
Figure 13. (A) schematic diagram of the microstructure, high mechanical adaptability to organs, and detection of EMG biological signals of PVA/MF/Gly-CH[215]; (B) schematic diagram of the preparation process, gesture detection, and detection of ECG signals of the PDES/CMFs flexible electrodes[216]; (C) schematic diagram of the architecture, composition and working process of the wireless electronic skin clinical detection system[217]. PU: Polyurethane; PVA: polyvinyl alcohol; MF-CH: microfiber composite hydrogel; PET: polyethylene terephthalate; PI: polyimide; PDMS: polydimethylsiloxane; NF: nanofiber; CMFs: cellulose microfibers; AMP: alkalized MXenes/polydopamine; EMG: electromyogram; MF: microfiber; ECG: electrocardiogram.
As shown in Figure 13C, Cho et al. reported an e-skin system consisting of a multimodal sensor system and a wireless detection system (using the RF430FRL152H NFC SoC chip from Texas Instruments) for continuously monitoring pressure, temperature, and hydration at skin interfaces of a patient in a wheelchair[217]. The multimodal sensor system includes a pressure sensor, a temperature sensor, and a galvanic skin response sensor. The wireless detection system communicates data with a plurality of wireless devices mounted on the skin of the wheelchair user, enabling accurate and stable measurements of pressure, temperature and hydration. Clinical trials with wheelchair patients demonstrated the feasibility and stability of the monolithic integrated system in preventing injuries caused by sedentary behavior.
Intelligent machinery
E-skins also possess vast potential in the field of intelligent machinery[218,219]. The application of e-skins in this area mainly includes two major aspects: remote control and mechanical haptics. For the former, the basic implementation method is to use e-skins as convenient signal sources and realize the convenient control of machinery by decoding the characteristics of the signals. Some scholars installed e-skins at the joint positions of hands or gloves and achieved synchronized control of a robotic hand by decoding the characteristic information transmitted by these e-skins in real time[220]. For the latter, the basic strategy is to install e-skins on the surface of the machinery so as to give the machinery tactile ability[221-223][Figure 14A (i) and Figure 14B (i)][224-229].
Figure 14. (A) Schematic diagram of remote control: (i) system composition and demonstration of synchronous control of SPRET
Zhang et al. designed a textile resistive pressure sensor, SPRET, consisting of a CNT network (conductive layer) + polypyrrole-polydopamine-perfluorodecyltrlethoxysilane (PPy-PDA-PFDS) polymer layer (hydrophobic layer) + a textile (substrate), exhibiting sparing to various reagents and high robustness[224]. This wearable e-skin can accurately, continuously, long-term and reliably detect human motion and physiological signals in air/humid conditions or underwater and also enable synchronized remote control of manipulators through a MCU [Figure 14A (i)].
Lu et al. developed hydroxypropyl cellulose (HPC)-based eutectic gels with high conductivity, transparency, and anti-freezing properties with 98.1% resilience suitable for wearable applications by introducing HPC into a metal salt-based deep eutectic solvent (MDES)[225]. Self-powered e-skin composed of this Eutectic Gel-Based Triboelectric Nanogenerator (E-TENG) prepared using this eutectic gel possesses excellent performance. Realized by an Arduino nano MCU (using ATmega328P Chip) for gesture recognition and robotic remote control, this highly resilient self-powered e-skin exhibits extraordinary potential for practical HMI and cooperative operation of intelligent machinery [Figure 14A (ii)].
Liu et al. proposed a capacitive bimodal e-skin, silicone rubber/4-Methylbenzenesulfonhydrazide/Silok-7455 Hyperdispersant/Polyphenylmethylsiloxane (SR/TSH/dispersant/PPMS), with 12 labyrinth-patterned sensing units in a pyramidal porous multistage dielectric structure for proximity and pressure sensing in HMI scenarios, which has a proximity detection range of 0-110 mm and a pressure detection range of
Yang et al. report a self-healing high-performance flexible pressure sensor with MXene/PU (Sensitive Layer) - MXene (interdigital electrode) that can be prepared by a low-cost spraying method, which can be prepared on other arbitrary flexible substrates, with the hydrogen bonding of PU conferring self-healing functionality to the device[228]. The sensor exhibits high sensitivity (up to 509.8 kPa-1) and good stability (10,000 cycles). This pressure sensor-based e-skin can be mounted on a robotic hand to give it haptic capabilities
Luo et al. demonstrated a capacitive-dielectric integrated pressure sensor, micro-conformal electrode-dielectric integration (MEDI), which has a complex structure of graphene nanowalls (pyramid-shaped top electrode)/PDMS (pyramid-shaped dielectric layer)/ZnO (pyramid-shaped dielectric layer)/poly(methyl methacrylate) (PMMA, protective layer/dielectric layer)/AgNWs (bottom electrode)/polyethylene terephthalate (PET) (substrate)[228,229]. The introduction of the dielectric layer can effectively improve the sensitivity (up to 22.3 kPa-1) and pressure response range (0-22 kPa). Subsequently, the research team mounted it on a robotic hand to realize intelligent mechanical applications such as braille recognition, object grasping and roughness detection [Figure 14B (iii)].
PROSPECTS AND OUTLOOKS
In this review, we introduced the background and development of e-skins and outline the general strategies of various types of e-skins for realizing different basic functions, along with the latest advances in integrating these multiple functions into e-skin systems. We are pleased to see that the field of e-skins has been showing rapid development and making great strides every year, and we consider that the following trends will follow in the next period of time:
1. A major challenge remains how to integrate the various sensing characteristics better. This involves two key points: (1) the preparation of multimodal e-skins that integrate diverse powerful sensing capabilities; (2) the sensing characteristics are as non-interfering with each other as possible.
2. ML technologies will be applied in large numbers, accelerating the intelligent development of e-skins.
3. E-skins with special capabilities will increasingly emerge, expanding the applications of e-skins. For example, e-skins with good biocompatibility and degradability[230-232], suitable for medical and bioelectronic applications; e-skins having high transparency[159,233], suitable for photovoltaic and display applications. In fact, there have been e-skins with special capabilities such as light-emitting[234-236], thermal management[200], hydrophobicity[237,238], UV resistance[239], electromagnetic radiation resistance[200,240], and fire alarm[241].
4. Realizing these trends necessitates sophisticated designs in materials, circuits, processes, and signal processing. This calls for deep interdisciplinary collaboration among scientists in various fields such as chemistry, materials, mechatronics, electronics, information, and computers. For example, to achieve high performance, new high-performance sensors need to be designed from the material design, device structure design and other aspects, which requires the cooperation of scientists in the fields of chemistry, materials, mechanics, and so on. The trend of cross-field cooperation is more obvious in developing complex e-skin systems. The Multimodal E-skin Systems listed in the paper require fine design from chemistry, materials, mechanics, electronics, etc. to combine high performance and high reliability, and the IoT-Integrated and ML-Enabled E-skin Systems listed in the article involve a large number of technologies in the fields of electronics, information science, and computers. The applications of e-skins also require the contributions of multiple disciplines, such as medicine, biology, materials, chemistry, mechanics, electronics, and information technology, to be realized. And for the application of e-skin in other lesser-known fields, it is even more important to have cross-disciplinary collaboration to help inspire, promote, and apply. It is clear that these high-level, cutting-edge visions cannot be easily realized by traditional chemical and materials scientists alone and that cross-disciplinary research is the order of the day.
5. The signal communication in e-skin systems needs to be reformed. For example, for the synchronized remote control, which can be foreseen that such scenarios will inevitably become increasingly prevalent in the future, low latency is necessary, which means the requirements for fine material and structural design to improve the response speed, interface impedance, and other characteristics of the new e-skins[242,243].
6. Brain-computer interface (BCI) technology has made breakthroughs one after another in recent years[244,245]. In fact, it is a further in-depth development of the HMI concept, and it is foreseeable that e-skin will gain a lot of applications in this field. Similar to healthcare applications, but furthermore, the requirements for non-toxicity and biocompatibility are more demanding, so we believe that some hydrogel materials with superior biocompatibility will be more suitable for applications in this field. On the other hand, it is clear that this is an extremely complex field that relies on the intimate cooperation of many disciplines such as biology, neurology, medicine, materials science, information science, ethics, mechanics, and so on.
DECLARATIONS
Authors’ contributions
Determined the content and structure of the manuscript: Pang J, He P
Review and revise the manuscript: Li Z, Zhang S
Researching the literature and writing the manuscript: Zhu P
All authors have read the manuscript and approved the final version.
Availability of data and materials
Not applicable.
Financial support and sponsorship
The authors acknowledge the financial support from the National Natural Science Foundation of China (No. 52375327) and the Heilongjiang Provincial Natural Science Foundation of China (No. YQ2022E024).
Conflicts of interest
All authors declared that there are no conflicts of interest.
Ethical approval and consent to participate
Figures 3C, 4D, 5A, 5B, 6C, 12A, 12C, 13A, 13C and 14A are cited from the researches in refs.[62,72,82,83,95,199,200,215,217,224,225], respectively, showing partial body parts of the participants in the experiment. The purpose of these citations is to present the relevant research results and experimental methods more intuitively. The original images have been rigorously reviewed during the production of relevant illustrations to ensure the protection of the privacy of the experimental participants.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2024.
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Zhu P, Li Z, Pang J, He P, Zhang S. Latest developments and trends in electronic skin devices. Soft Sci 2024;4:17. http://dx.doi.org/10.20517/ss.2024.05
AMA Style
Zhu P, Li Z, Pang J, He P, Zhang S. Latest developments and trends in electronic skin devices. Soft Science. 2024; 4(2): 17. http://dx.doi.org/10.20517/ss.2024.05
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Zhu, Pengyu, Zihan Li, Jinbo Pang, Peng He, Shuye Zhang. 2024. "Latest developments and trends in electronic skin devices" Soft Science. 4, no.2: 17. http://dx.doi.org/10.20517/ss.2024.05
ACS Style
Zhu, P.; Li Z.; Pang J.; He P.; Zhang S. Latest developments and trends in electronic skin devices. Soft. Sci. 2024, 4, 17. http://dx.doi.org/10.20517/ss.2024.05
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