Skin-like Sensors Research Articles

Toward Integrated Multifunctional Laser-Induced Graphene-Based Skin-Like Flexible Sensor Systems.

The burgeoning demands for health care and human-machine interfaces call for the next generation of multifunctional integrated sensor systems with facile fabrication processes and reliable performances. Laser-induced graphene (LIG) with highly tunable physical and chemical characteristics plays vital roles in developing versatile skin-like flexible or stretchable sensor systems. This Progress Report presents an in-depth overview of the latest advances in LIG-based techniques in the applications of flexible sensors. First, the merits of the LIG technique are highlighted especially as the building blocks for flexible sensors, followed by the description of various fabrication methods of LIG and its variants. Then, the focus is moved to diverse LIG-based flexible sensors, including physical sensors, chemical sensors, and electrophysiological sensors. Mechanisms and advantages of LIG in these scenarios are described in detail. Furthermore, various representative paradigms of integrated LIG-based sensor systems are presented to show the capabilities of LIG technique for multipurpose applications. The signal cross-talk issues are discussed with possible strategies. The LIG technology with versatile functionalities coupled with other fabrication strategies will enable high-performance integrated sensor systems for next-generation skin electronics.

MXene-Based Skin-Like Hydrogel Sensor and Machine Learning-Assisted Handwriting Recognition.

Conductive hydrogels are widely used in flexible sensors owing to their adjustable structure, good conductivity, and flexibility. The performance of excellent mechanical properties, high sensitivity, and elastic modulus compatible with human tissues is of great interest in the field of flexible sensors. In this paper, the functional groups of trisodium citrate dihydrate (SC) and MXene form multiple hydrogen bonds in the polymer network to prepare a hydrogel with mechanical properties (Young's modulus (23.5-92 kPa) of similar human tissue (0-100 kPa)), sensitivity (stretched GF is 4.41 and compressed S1 is 5.15 MPa-1), and durability (1000 cycles). The hydrogel is able to sensitively detect deformations caused by strain and stress and can be used in flexible sensors to detect human movement in real time such as fingers, wrists, and walking. In addition, the combination of matrix sensing and machine learning was successfully used for handwriting recognition with an accuracy of 0.9744. The combination of machine learning and flexible sensors shows great potential in areas such as healthcare, information security, and smart homes.

Bio-Skin-Inspired Flexible Pressure Sensor Based on Carbonized Cotton Fabric for Human Activity Monitoring.

With the development of technology, people's demand for pressure sensors with high sensitivity and a wide working range is increasing. An effective way to achieve this goal is simulating human skin. Herein, we propose a facile, low-cost, and reproducible method for preparing a skin-like multi-layer flexible pressure sensor (MFPS) device with high sensitivity (5.51 kPa-1 from 0 to 30 kPa) and wide working pressure range (0-200 kPa) by assembling carbonized fabrics and micro-wrinkle-structured Ag@rGO electrodes layer by layer. In addition, the highly imitated skin structure also provides the device with an extremely short response time (60/90 ms) and stable durability (over 3000 cycles). Importantly, we integrated multiple sensor devices into gloves to monitor finger movements and behaviors. In summary, the skin-like MFPS device has significant potential for real-time monitoring of human activities in the field of flexible wearable electronics and human-machine interaction.

Scientometric analysis of textile sensors

Textile sensors have been widely used in many applications such as healthcare, sports, and entertainment. Focusing on the scientific evolution in the research field of textile sensors, this paper presents a comprehensive review on the academic publications, research hotspots, and major research directions in the field based on scientometric analysis. Totally 3,820 papers published between the year 1991 and 2023 were retrieved and investigated from the Web of Science Core Collection. The software CiteSpace and VOSviewer were adopted to perform scientometric analysis on the collaboration between countries, regions, and institutions. In addition, the citations and co-citations of authors, the source journals, the references, and the co-occurrence of keywords were analyzed. Furthermore, five main research directions in the field of textile sensors were also summarized in this paper, and the most promising and potentially profitable research direction could be health monitoring. The analysis result shows that the research hotspots in recent years include the development of triboelectric nanogenerator, the manufacture of transparent skin-like sensors and optical fiber sensors, the process of data collected from wearable textile sensors, and the design of new materials such as nanocomposites for textile sensors.

A triboelectric sensing array integrating material identification and self-healing enabled by a healable polyamide-based device unit

To develop skin-like tactile sensor systems with both self-healing ability of repairing mechanical damage and perception ability of identifying materials and objects is significantly valuable for smart and high-quality interaction with environment. It is noticed that the sensing array comprised of distinct triboelectric nanogenerator (TENG) units shows great advantages in material recognition. However, those attempts of integrating self-healing ability into above triboelectric sensing array are challenged by the lack of self-healing TENG constituent units with differentiated electron affinity. Herein, we exploit a self-healing polyamide (SPA) with high mechanical strength as the base material for constructing a novel self-healing TENG unit with distinctive electron affinity. The healing of the prepared SPA-based TENG (SPA-TENG) can be autonomously conducted at room temperature without external intervention required. Our results reveal that SPA-TENG possesses stronger electron affinity compared to existing self-healing TENGs, which enables it to be an ideal constituent unit to construct self-healing triboelectric sensing array for material identification. After combining SPA-TENG with other previously reported self-healing TENGs, we firstly demonstrate a fully self-healing triboelectric sensing array capable of distinguishing various commercial plastic products based on their fingerprint waveform combinations, which shows promising application in fields of intelligent robots, artificial prosthesis, and automatic production etc.

Pt/Zn-TCPP Nanozyme-Based Flexible Immunoassay for Dual-Mode Pressure-Temperature Monitoring of Low-Abundance Proteins.

Pressure and temperature, as common physical parameters, are important for monitoring human health. In contrast, single-mode monitoring is prone to causing experimental errors. Herein, we innovatively designed a dual-mode flexible sensing platform based on a platinum/zinc-meso-tetrakis(4-carboxyphenyl)porphyrin (Pt/Zn-TCPP) nanozyme for the quantitative monitoring of carcinoembryonic antigen (CEA) in biological fluids with pressure and temperature readouts. The Pt/Zn-TCPP nanozyme with catalytic and photothermal efficiencies was synthesized by means of integrating photosensitizers into porous materials. The flexible sensing system after the antigen-antibody reaction recognized the pressure using a flexible skin-like pressure sensor with a digital multimeter readout, whereas the temperature was acquired via the photoheat conversion system of the Pt/Zn-TCPP nanozyme under 808 nm near-infrared (NIR) irradiation using a portable NIR imaging camera on a smartphone. Meanwhile, the dual-mode flexible sensing system was carried out on a homemade three-dimensional (3D)-printed device. Results revealed that the developed dual-mode immunosensing platform could exhibit good pressure and temperature responses within the dynamic range of 0.5-100 ng mL-1 CEA with the detection limits of 0.24 and 0.13 ng mL-1, respectively. In addition, the pressure and temperature were sensed simultaneously without crosstalk interference. Importantly, the dual-mode flexible immunosensing system can effectively avoid false alarms during the measurement, thus providing great potential for simple and low-cost development for point-of-care testing.

Recent progress of optical tactile sensors: A review

Over the past decade, flexible electronics has become a cutting-edge technology owing to its broad applications in robotics, health monitoring, and human-machine interface. The recent development of flexible electrical tactile sensor triggers the research of optical tactile sensors due to their potentials in high-sensitivity, high-precision, low power consumption, and anti-electromagnetic interference. Here, this review mainly discusses the advances and applications of silica fiber, polymer fiber, hydrogel, and micro/nanofiber based tactile sensors, specifically, focusing on the latest research results and on the necessary technologies to enable the health monitoring, robotics, and human-machine interface applications. Furthermore, future outlooks in developing skin-like optical tactile sensors are also presented.

Laser-Induced Skin-like Flexible Pressure Sensor for Artificial Intelligence Speech Recognition.

Skin-like flexible pressure sensors with good sensing performance have great application potential, but their development is limited owing to the need for multistep, high-cost, and low-efficiency preparation processes. Herein, a simple, low-cost, and efficient laser-induced forming process is proposed for the first time to prepare a skin-like flexible piezoresistive sensor. In the laser-induced forming process, based on the photothermal effect of graphene and the foaming effect of glucose, a skin-like polydimethylsiloxanes (PDMS) film with porous structures and surface protrusions is obtained by using infrared laser irradiation of the glucose/graphene/PDMS prepolymer film. Further, based on the skin-like PDMS film with a graphene conductive layer, a new skin-like flexible piezoresistive sensor is obtained. Due to the stress concentration caused by the surface protrusions and the low stiffness caused by the porous structures, the flexible piezoresistive sensor realizes an ultrahigh sensitivity of 1348 kPa-1 at 0-2 kPa, a wide range of 200 kPa, a fast response/recovery time of 52 ms/35 ms, and good stability over 5000 cycles. The application of the sensor to the detection of human pulses and robot clamping force indicates its potential for health monitoring and soft robots. Furthermore, in combination with the neural network (CNN) algorithm in artificial intelligence technology, the sensor achieves 95% accuracy in speech recognition, which demonstrates its great potential for intelligent wearable electronics. Especially, the laser-induced forming process is expected to facilitate the efficient, large-scale preparation of flexible devices with multilevel structures.

Electrostatically self-assembled three-dimensional conductive network for highly sensitive and reliable skin-like strain sensor

In recent years, flexible strain sensors have garnered significant attention in industrial manufacturing and daily life. Sensitivity and reliability are two crucial characteristics of flexible strain sensors in practical applications, and they depend on the development of the sensor's internal conductive network. However, the aggregation phenomenon of conductive fillers in the elastic matrix has a serious impact on the construction of a developed conductive network. In this work, we have designed electropositive amino-functionalized carbon nanotubes (CNTs-p) based on the electrostatic self-assembly of electronegative MXene in the aqueous phase. Compared to the use of surfactants, the electrical modulation of carbon nanotubes through chemical bonding modification is more robust and the electrostatic self-assembly with MXene is more stable. CNTs-p and MXene were self-assembled by electrostatic attraction in butyl latex and uniformly dispersed in the latex. Following demulsification, the polymer composite film (MXene&CNTs-p/IIR) with a three-dimensional conductive network was obtained. The skin-like strain sensor, which utilizes the conductive composite film, demonstrates high sensitivity (gauge factor (GF) = 35137 that is among the highest values for the reported strain sensor), remarkable reliability (The signal monitoring capability remains after 15000 cycles), and excellent responsiveness (62 ms). Additionally, the skin-like strain sensor boasts a wide detection range (0–431%) and unprecedented stability, enabling strain sensing functionality in a wide temperature range of -10—100 °C, as well as strong acid (pH = 1) and strong alkali (pH = 11) environment. The preparation of MXene&CNTs-p/IIR provides a safe, environmentally friendly and effective method for improving the sensitivity and reliability of flexible sensors in wearable intelligent electronics and health detection.

A Skin-like Self-Powered Flexible Sensor for Wearable Monitoring and Robotic Tactile Application

A Skin-like Self-Powered Flexible Sensor for Wearable Monitoring and Robotic Tactile Application

Vision-and Tactile-Based Continuous Multimodal Intention and Attention Recognition for Safer Physical Human–Robot Interaction

Employing skin-like tactile sensors on robots enhances both the safety and usability of collaborative robots by adding the capability to detect human contact. Unfortunately, simple binary tactile sensors alone cannot determine the context of the human contact—whether it is a deliberate interaction or an unintended collision that requires safety manoeuvres. Many published methods classify discrete interactions using more advanced tactile sensors or by analysing joint torques. Instead, we propose to augment the intention recognition capabilities of simple binary tactile sensors by adding a robot-mounted camera for human posture analysis. Different interaction characteristics, including touch location, human pose, and gaze direction, are used to train a supervised machine learning algorithm to classify whether a touch is intentional or not with an F1-score of 86%. We demonstrate that multimodal intention recognition is significantly more accurate than monomodal analyses with the collaborative robot Baxter. Furthermore, our method can also continuously monitor interactions that fluidly change between intentional or unintentional by gauging the user’s attention through gaze. If a user stops paying attention mid-task, the proposed intention and attention recognition algorithm can activate safety features to prevent unsafe interactions. We also employ a feature reduction technique that reduces the number of inputs to five to achieve a more generalized low-dimensional classifier. This simplification both reduces the amount of training data required and improves real-world classification accuracy. It also renders the method potentially agnostic to the robot and touch sensor architectures while achieving a high degree of task adaptability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Whenever a user interacts physically with a robot, such as in collaborative manufacturing, the robot may respond to unintended touch inputs from the user. This may be through body collisions or that the user is suddenly distracted and is no longer paying attention to what they are doing. We propose an easy-to-implement method to augment safety of physical human-robot collaboration by determining whether the touch from a user is intentional or not through the use of robot-mounted basic touch sensors and computer vision. The algorithm examines the location of the user’s hands relative to the touched sensors in addition to observing where the user is looking. Machine learning is then used to classify in real-time, with an F1-score of 86%, whether a touch is intentional or not such that the robot can react accordingly. The method is particularly applicable in collaborative manufacturing contexts, but can also be applied anywhere where a user physically interacts with a robot. We demonstrate the utility of the method in enhancing safety during human-robot collaboration through a simulated collaborative manufacturing scenario with the robot Baxter, but the method can easily be adapted to other systems.

Thermochromic and conductive hydrogels with tunable temperature sensitivity for dual sensing of temperature and human motion

Skin-like body sensors have important applications in soft-bodied intelligent robots, electronic skins, and medical health monitoring.

Highly sensitive skin-like wearable optical sensor for human physiological signals monitoring

Pusle signal is an important information related to the cardiovascular system. Long-term and accurate pulse monitoring is of great significance. Wearable fiber-optic sensors show great promise in this field. In this work, a highly sensitive wearable skin-like optical pressure sensor for human physiological signals monitoring is demonstrated by embedding bent fiber in a silica gel film. By employing small-core fiber (SCF) and fabric, the skin-like optical pressure sensor achieves high sensitivity (2.2 kPa−1), fast response (5 ms), low detection limit (4 Pa) and remarkable stability (over 40,000 cycles). As a proof of concept, monitoring of pulse from the most parts of human body and finger motion have been demonstrated based on the proposed sensor through the power variation of the output light. Moreover, abnormal pulse signals in unhealthy states are demonstrated. This novel photonics-based sensor provides a new basis for developing high-performance wearable medical devices.

Biomimetic nanofiber-iongel composites for flexible pressure sensors with broad range and ultra-high sensitivity

To achieve high-performance flexible pressure sensors, it is imperative to develop biomimetic devices that mimic the functional structure and sensing mechanism of human skin. Nevertheless, the creation of skin-like sensors with both ultra-high sensitivity and broad response range poses a formidable challenge. Drawing inspiration from the tactile sensing mechanisms and hierarchical structure of human skin, we engineered a nanofiber-iongel (NFIG) composite with internally graded stiffness characteristics and surface semi-embedded microstructures through the application of electrostatic spinning and droplet injection methods. The gel mimics the layered nanofiber structure of human skin, along with its ion-sensing mechanism, and comprises an ion gel infused with highly elastic PVDF-HFP nanofibers. This study explores the impact of Young's modulus and external pressure on unit capacitance, and it establishes a fiber-gel composite model to assess how the fibers influence sensor performance, encompassing ion fluxes, displacements, and alterations in electric potential. These findings reveal that the utilization of high-modulus materials enhances ion mobility, decreases the double electrical layer thickness, and augments pressure resistance. Based on these discoveries, we engineered the NFIG sensor, which exhibits ultra-high sensitivity (>10,000 kPa−1), a wide pressure range (∼1000 kPa), and exceptional stability (over 5000 cycles). Furthermore, this sensor is versatile, finding utility in a range of human monitoring contexts, array configurations, and even skateboard monitoring, thereby substantiating its promise in the fields of human-computer interaction and sports health.

Hybrid assembly based on nanomaterial reinforcement for multifunctionalized skin-like flexible sensors

Currently, the inferior mechanical strength, weak environmental adaptability, and limited functionality of conductive hydrogel significantly impede its potential application in wearable sensors. Here, ''hard'' acrylic bentonite (AABT) intercalated nanostructures and silver-modified polydopamine (PDA@Ag) particles are encapsulated in a ''soft'' polyacrylic acid matrix in a water/glycerol binary solvent system. This strategy successfully realized the high matched skin modulus (58 kPa), high stress (371 kPa) and strain (1025 %). The PDA@Ag particles retains rich phenolic hydroxyl groups imitating mussel to provide strong adhesion (29.12 kPa). These particles also give the hydrogel long time antibacterial properties (3 days), while demonstrating excellent biosafety. The introduction of a binary system of water/glycerol effectively suppresses the evaporation and crystallization of water, thereby maintaining sensing performance above 82 % even under extreme conditions. This hydrogel achieves integrated applications of multifunctionality and multi-environmental adaptability, providing a new idea for the development of next-generation skin-like hydrogel sensors.

Machine Learning-Enabled Tactile Sensor Design for Dynamic Touch Decoding.

Skin-like flexible sensors play vital roles in healthcare and human-machine interactions. However, general goals focus on pursuing intrinsic static and dynamic performance of skin-like sensors themselves accompanied with diverse trial-and-error attempts. Such a forward strategy almost isolates the design of sensors from resulting applications. Here, a machine learning (ML)-guided design of flexible tactile sensor system is reported, enabling a high classification accuracy (≈99.58%) of tactile perception in six dynamic touch modalities. Different from the intuition-driven sensor design, such ML-guided performance optimization is realized by introducing a support vector machine-based ML algorithm along with specific statistical criteria for fabrication parameters selection to excavate features deeply concealed in raw sensing data. This inverse design merges the statistical learning criteria into the design phase of sensing hardware, bridging the gap between the device structures and algorithms. Using the optimized tactile sensor, the high-quality recognizable signals in handwriting applications are obtained. Besides, with the additional data processing, a robot hand assembled with the sensor is able to complete real-time touch-decoding of an 11-digit braille phone number with high accuracy.

Highly Strain-Stable Intrinsically Stretchable Olfactory Sensors for Imperceptible Health Monitoring.

Intrinsically stretchable gas sensors possess outstanding advantages in seamless conformability and high-comfort wearability for real-time detection toward skin/respiration gases, making them promising candidates for health monitoring and non-invasive disease diagnosis and therapy. However, the strain-induced deformation of the sensitive semiconductor layers possibly causes the sensing signal drift, resulting in failure in achievement of the reliable gas detection. Herein, a surprising result that the stretchable organic polymers present a universal strain-insensitive gas sensing property is shown. All the stretchable polymers with different degrees of crystallinity, including indacenodithiophene-benzothiadiazole (PIDTBT), diketo-pyrrolo-pyrrole bithiophene thienothiophene (DPPT-TT) and poly[4-(4,4-dihexadecyl-4H-cyclopenta[1,2-b:5,4-b']dithiophen-2-yl)-alt-[1,2,5]thiad-iazolo [3,4-c] pyridine] (PCDTPT), show almost unchanged gas response signals in the different stretching states. This outstanding advantage enables the intrinsically stretchable devices to imperceptibly adhere on human skin and well conform to the versatile deformations such as bending, twisting, and stretching, with the highly strain-stable gas sensing property. The intrinsically stretchable PIDTBT sensor also demonstrates the excellent selectivity toward the skin-emitted trimethylamine (TMA) gas, with a theoretical limit of detection as low as 0.3ppb. The work provides new insights into the preparation of the reliable skin-like gas sensors and highlights the potential applications in the real-time detection of skin gas and respiration gas for non-invasive medical treatment and disease diagnosis.

Ti3C2Tx MXene-Based Multifunctional Tactile Sensors for Precisely Detecting and Distinguishing Temperature and Pressure Stimuli.

Although skin-like sensors that can simultaneously detect various physical stimuli are of fair importance in cutting-edge human-machine interaction, robotic, and healthcare applications, they still face challenges in facile, scalable, and cost-effective production using conventional active materials. The emerging two-dimensional transition metal carbide, Ti3C2Tx MXene, integrated with favorable thermoelectric properties, metallic-like conductivity, and a hydrophilic surface, is promising for solving these problems. Herein, skin-like multifunctional sensors are designed to precisely detect and distinguish temperature and pressure stimuli without cross-talk by decorating elastic and porous substrates with MXene sheets. Because the combination of the thermoelectric and conductive MXene with the thermally insulating, elastic, and porous substrate integrates efficient Seebeck and piezoresistive effects, the resultant sensor exhibits not only an ultralow detection limit (0.05 K), high signal-to-noise ratio, and excellent cycling stability for temperature detection but also high sensitivity, fast response time, and outstanding durability for pressure detection. Based on the impressive dual-mode sensing properties and independent temperature and pressure detections, a multimode input terminal and an electronic skin are created, exhibiting great potential in robotic and human-machine interaction applications. This work provides a scalable fabrication of multifunctional tactile sensors for precisely detecting and distinguishing temperature and pressure stimuli.

An EIT-based piezoresistive sensing skin with a lattice structure

Spatially distributed sensing has gained significant value in diverse domains, with wearables as a notable application. In this work, a flexible skin-like sensor was developed for distributed pressure sensing. The sensing skin comprised a carbon black/silicone composite lattice structure embedded in a silicone sheet. The lattice-patterned structure is a distinct departure from conventional uniform sensing skins. Electrical impedance tomography (EIT) was employed to reconstructelectrical resistance over the sensing area, which was then mapped into pressure distribution based on theprinciple of piezoresistivity. EIToffers continuity and design simplicity as it eliminates the need for internal wiring, making it a promising technique in the wearable industry. The lattice sensing skin offered favorable sensing attributes, including quick response and recovery (75 ms and 84 ms at 85 kPa), a linear response with sensitivity as high as 0.119 kPa−1, a full-scale range of at least 100 kPa, and high repeatability (∼0.5% drop in maximum relative resistance over 300 cycles). The sensing skin was responsive over its entire area in both flat and non-planar conditions and was able to detect both single- and multi-point touch. The sensitivity and tactile area detectability varied depending on the position of applied pressure over the sensing area. Future studies will examine other lattice patterns and conductive composite fillers with the intention to develop a framework for optimizing the lattice sensing skin for tailored accuracy and resolution.

Chitosan-based transparent and conductive hydrogel with highly stretchable, adhesive and self-healing as skin-like sensor

Hydrogel sensors attained increasing attention due to their excellent mechanical and sensing properties. However, it is still a big challenge to fabricate hydrogel sensors with multifunctional properties of transparent, high stretchability, self-adhesive and self-healing ability. In this study, chitosan as a natural polymer has been employed to construct a polyacrylamide-chitosan-Al3+ (PAM-CS-Al3+) double network (DN) hydrogel with high transparency (>90 % at 800 nm), good electrical conductivity (up to 5.01 S/m) and excellent mechanical properties (strain and toughness as high as 1040 % and 730 kJ/m3). Moreover, the dynamic ionic and hydrogen bond interaction between PAM and CS endowed the PAM-CS-Al3+ hydrogel good self-healing ability. In addition, the hydrogel possesses good self-adhesive ability on different substrates, including glass, wood, metal, plastic, paper, polytetrafluoroethylene (PTFE) and rubber. Most importantly, the prepared hydrogel could be assembled into transparent, flexible, self-adhesive, self-healing and high sensitive strain/pressure sensor for monitoring human body movement. This work may pave the way for fabricating the multifunctional chitosan-based hydrogels which has potential application in the fields of wearable sensor and soft electronic devices.