Fingerpad-Inspired Multimodal Electronic Skin for Material Discrimination and Texture Recognition

Quantitative softness and texture bimodal haptic sensors for robotic clinical feature identification and intelligent picking

Replicating human somatosensory networks in robots is crucial for dexterous manipulation, ensuring the appropriate grasping force for objects of varying softness and textures. Despite advances in artificial haptic sensing for object recognition, accurately quantifying haptic perceptions to discern softness and texture remains challenging. Here, we report a methodology that uses a bimodal haptic sensor to capture multidimensional static and dynamic stimuli, allowing for the simultaneous quantification of softness and texture features. This method demonstrates synergistic measurements of elastic and frictional coefficients, thereby providing a universal strategy for acquiring the adaptive gripping force necessary for scarless, antislippage interaction with delicate objects. Equipped with this sensor, a robotic manipulator identifies porcine mucosal features with 98.44% accuracy and stably grasps visually indistinguishable mature white strawberries, enabling reliable tissue palpation and intelligent picking. The design concept and comprehensive guidelines presented would provide insights into haptic sensor development, promising benefits for robotics.

Biometric-Tuned E-Skin Sensor with Real Fingerprints Provides Insights on Tactile Perception: Rosa Parks Had Better Surface Vibrational Sensation than Richard Nixon

The dense mechanoreceptors in human fingertips enable texture discrimination. Recent advances in flexible electronics have created tactile sensors that effectively replicate slowly adapting (SA) and rapidly adapting (RA) mechanoreceptors. However, the influence of dermatoglyphic structures on tactile signal transmission, such as the effect of fingerprint ridge filtering on friction-induced vibration frequencies, remains unexplored. A novel multi-layer flexible sensor with an artificially synthesized skin surface capable of replicating arbitrary fingerprints is developed. This sensor simultaneously detects pressure (SA response) and vibration (RA response), enabling texture recognition. Fingerprint ridge patterns from notable historical figures - Rosa Parks, Richard Nixon, Martin Luther King Jr., and Ronald Reagan - are fabricated on the sensor surface. Vibration frequency responses to assorted fabric textures are measured and compared between fingerprint replicas. Results demonstrate that fingerprint topography substantially impacts skin-surface vibrational transmission. Specifically, Parks' fingerprint structure conveyed higher frequencies more clearly than those of Nixon, King, or Reagan. This work suggests individual fingerprint ridge morphological variation influences tactile perception and can confer adaptive advantages for fine texture discrimination. The flexible bioinspired sensor provides new insights into human vibrotactile processing by modeling fingerprint-filtered mechanical signals at the finger-object interface.

Electronic Skin: Opportunities and Challenges in Convergence with Machine Learning

Recent advancements in soft electronic skin (e-skin) have led to the development of human-like devices that reproduce the skin's functions and physical attributes. These devices are being explored for applications in robotic prostheses as well as for collecting biopotentials for disease diagnosis and treatment, as exemplified by biomedical e-skins. More recently, machine learning (ML) has been utilized to enhance device control accuracy and data processing efficiency. The convergence of e-skin technologies with ML is promoting their translation into clinical practice, especially in healthcare. This review highlights the latest developments in ML-reinforced e-skin devices for robotic prostheses and biomedical instrumentations. We first describe technological breakthroughs in state-of-the-art e-skin devices, emphasizing technologies that achieve skin-like properties. We then introduce ML methods adopted for control optimization and pattern recognition, followed by practical applications that converge the two technologies. Lastly, we briefly discuss the challenges this interdisciplinary research encounters in its clinical and industrial transition.

Highly-Aligned All-Fiber Actuator with Asymmetric Photothermal-Humidity Response and Autonomous Perceptivity

Soft robots adapt to complex environments for autonomous locomotion, manipulation, and perception are attractive for robot-environment interactions. Strategies to reconcile environment-triggered actuation and self-powered sensing responses to different stimuli remain challenging. By tuning the in situ vapor phase solvent exchange effect in continuous electrospinning, an asymmetric highly-aligned all-fiber membrane (HAFM) with a hierarchical "grape-like" nanosphere-assembled microfiber structure (specific surface area of 13.6 m2 g-1) and excellent mechanical toughness (tensile stress of 5.5 MPa, and fracture toughness of 798 KJ m-3) is developed, which shows efficient asymmetric actuation to both photothermal and humidity stimuli. The HAFM consists of a metal-organic framework (MOF)-enhanced moisture-responsive layer and an MXene-improved photothermal-responsive layer, which achieves substantial actuation with a bending curvature up to ≈7.23 cm-1 and a fast response of 0.60 cm-1 s-1. By tailoring the fiber alignment and bi-layer thickness ratio, different types of micromanipulators, automatic walking robots, and plant robots with programmable structures are demonstrated, which are realized for self-powered information perception of material type, object moisture, and temperature by integrating the autonomous triboelectric effect induced by photothermal-moisture actuation. This work presents fiber materials with programable hierarchical asymmetries and inspires a common strategy for self-powered organism-interface robots to interact with complex environments.

An Ionic Assisted Enhancement Strategy Enabled High Performance Flexible Pressure-Temperature Dual Sensor

Flexible pressure sensors with a broad range and high sensitivity are greatly desired yet challenging to build. Herein, we have successfully fabricated a pressure-temperature dual sensor via an ionic assisted charge enhancement strategy. Benefiting from the immobilization effect for [EMIM+] [TFSI-] ion pairs and charge transfer between ionic liquid (IL) and HFMO (H10Fe3Mo21O51), the formed IL-HFMO-TPU pressure sensor shows a high sensitivity of 25.35 kPa-1 and broad sensing range (∼10 MPa), respectively. Furthermore, the sensor device exhibits high durability and stability (5000 cycles@1 MPa). The IL-HFMO-TPU sensor also shows the merit of good temperature sensing properties. Attributed to these superior properties, the proposed sensor device could detect pressure in an ultrawide sensing range (from Pa to MPa), including breathe and biophysical signal monitoring etc. The proposed ionic assisted enhancement approach is a generic strategy for constructing high performance flexible pressure-temperature dual sensor.

Self-healing and adhesive MXene-polypyrrole/silk fibroin/polyvinyl alcohol conductive hydrogels as wearable sensor

Conductive hydrogels become increasing attractive for flexible electronic devices and biosensors. However, challenges still remain in fabrication of flexible hydrogels with high electrical conductivity, self-healing capability and adhesion property. Herein, a conductive hydrogel (PSDM) was prepared by solution-gel method using MXene and dopamine modified polypyrrole as conductive enhanced materials, polyvinyl alcohol and silk fibroin as gel networks, and borax as cross-linking agent. Notably, the PSDM hydrogels not only showed high permeability (13.82 mg∙cm-2∙h-1), excellent stretch ability (1235 %), high electrical conductivity (11.3 S/m) and long-term stability, but also exhibited high adhesion performance and self-healing properties. PSDM hydrogels displayed outstanding sensing performance and durability for monitoring human activities including writing, finger bending and wrist bending. The PSDM hydrogel was made into wearable flexible electrodes and realized accurate, sensitive and reliable detection of human electromyographic and electrocardiographic signals. The sensor was also applied in human-computer interaction by collecting electromyography signals of different gestures for machine learning and gesture recognition. According to 480 groups of data collected, the recognition accuracy of gestures by the electrodes was close to 100 %, indicating that the PSDM hydrogel electrodes possessed excellent sensing performance for high precision data acquisition and human-computer interaction interface.

Enhanced sensitivity and linear-response in iontronic pressure sensors for non-contact, high-frequency vibration recognition

Monitoring non-contact high-frequency vibrations requires improving the sensitivity and linear response of iontronic pressure sensors (IPSs). In this study, we incorporate composite electrodes comprising silver nanowires (Ag NWs) and MXene into IPSs to enhance electronic conduction and pseudocapacitance. Moreover, we utilize a novel surface-pillar microstructure, along with an internally randomized multi-bubble structure within the dielectric layer, to significantly expand the linear response range of the sensor. The resulting IPS device demonstrates exceptional linear sensitivity, measuring approximately 153.83 kPa-1, across a broad pressure range of up to 260 kPa. Additionally, it exhibits long-term stability, rapid response and recovery characteristics, and remains functional underwater. Notably, these devices exhibit remarkable capabilities in monitoring ultrasonic vibrations and accurately identifying sound wave vibrations. The integration of composite electrodes, microstructure designs, and their compatibility with underwater applications positions these IPSs as highly promising tools for precise measurements and advancements in flexible electronics technology.

Age of Flexible Electronics: Emerging Trends in Soft Multifunctional Sensors

With the commercialization of first-generation flexible mobiles and displays in the late 2010s, humanity has stepped into the age of flexible electronics. Inevitably, soft multifunctional sensors, as essential components of next-generation flexible electronics, have attracted tremendous research interest like never before. This review is dedicated to offering an overview of the latest emerging trends in soft multifunctional sensors and their accordant future research and development (R&D) directions for the coming decade. First, key characteristics and the predominant target stimuli for soft multifunctional sensors are highlighted. Second, important selection criteria for soft multifunctional sensors are introduced. Next, emerging materials/structures and trends for soft multifunctional sensors are identified. Specifically, the future R&D directions of these sensors are envisaged based on their emerging trends, namely i) decoupling of multiple stimuli, ii) data processing, iii) skin conformability, and iv) energy sources. Finally, the challenges and potential opportunities for these sensors in future are discussed, offering new insights into prospects in the fast-emerging technology.

Recent advance of high-quality perovskite nanostructure and its application in flexible photodetectors

Flexible photodetectors (PDs) have garnered increasing attention for their potential applications in diverse fields, including weather monitoring, smart robotics, smart textiles, electronic eyes, wearable biomedical monitoring devices, and so on. Notably, perovskite nanostructures have emerged as a promising material for flexible PDs due to their distinctive features, such as a large optical absorption coefficient, tunable band gap, extended photoluminescence decay time, high carrier mobility, low defect density, long exciton diffusion lengths, strong self-trapped effect, good mechanical flexibility, and facile synthesis methods. In this review, we first introduce various synthesis methods for perovskite nanostructures and elucidate their corresponding optical and electrical properties, encompassing quantum dots, nanocrystals, nanowires, nanobelts, nanosheets, single-crystal thin films, polycrystalline thin films, and nanostructured arrays. Furthermore, the working mechanism and key performance parameters of optoelectronic devices are summarized. The review also systematically compiles recent advancements in flexible PDs based on various nanostructured perovskites. Finally, we present the current challenges and prospects for the development of perovskite nanostructures-based flexible PDs.

An All-Protein Multisensory Highly Bionic Skin

To achieve a highly realistic robot, closely mimicking human skin in terms of materials and functionality is essential. This paper presents an all-protein silk fibroin bionic skin (SFBS) that emulates both fast-adapting (FA) and slow-adapting (SA) receptors. The mechanically different silk film and hydrogel, which exhibited skin-like properties, such as stretchability (>140%), elasticity, low modulus (<10 kPa), biocompatibility, and degradability, were prepared through mesoscopic reconstruction engineering to mimic the epidermis and dermis. Our SFBS, incorporating SA and FA sensors, demonstrated a highly sensitive (1.083 kPa-1) static pressure sensing performance (in vitro and in vivo), showed the ability to sense high-frequency vibrations (50-400 Hz), could discriminate materials and sliding, and could even identify the fine morphological differences between objects. As proof of concept, an SFBS-integrated rehabilitation glove was synthesized, which could help stroke patients regain sensory feedback. In conclusion, this work provides a practical approach for developing skin equivalents, prostheses, and smart robots.

Biomimetic Wearable Sensors: Emerging Combination of Intelligence and Electronics

Owing to the advancement of interdisciplinary concepts, for example, wearable electronics, bioelectronics, and intelligent sensing, during the microelectronics industrial revolution, nowadays, extensively mature wearable sensing devices have become new favorites in the noninvasive human healthcare industry. The combination of wearable sensing devices with bionics is driving frontier developments in various fields, such as personalized medical monitoring and flexible electronics, due to the superior biocompatibilities and diverse sensing mechanisms. It is noticed that the integration of desired functions into wearable device materials can be realized by grafting biomimetic intelligence. Therefore, herein, the mechanism by which biomimetic materials satisfy and further enhance system functionality is reviewed. Next, wearable artificial sensory systems that integrate biomimetic sensing into portable sensing devices are introduced, which have received significant attention from the industry owing to their novel sensing approaches and portabilities. To address the limitations encountered by important signal and data units in biomimetic wearable sensing systems, two paths forward are identified and current challenges and opportunities are presented in this field. In summary, this review provides a further comprehensive understanding of the development of biomimetic wearable sensing devices from both breadth and depth perspectives, offering valuable guidance for future research and application expansion of these devices.

Mechanically Robust and Linearly Sensitive Soft Piezoresistive Pressure Sensor for a Wearable Human-Robot Interaction System

Soft piezoresistive pressure sensors play an underpinning role in enabling a plethora of future Internet of Things (IoT) applications such as human-robot interaction (HRI) technologies, wearable devices, and metaverse ecosystems. Despite significant attempts to enhance the performance of these sensors, existing sensors still fall short of achieving high strain tolerance and linearity simultaneously. Herein, we present a low-cost, facile, and scalable approach to fabricating a highly strain-tolerant and linearly sensitive soft piezoresistive pressure sensor. Our design utilizes thin nanocracked gold films (NC-GFs) deposited on poly(dimethylsiloxane) (PDMS) as electrodes of the sensor. The large mismatch stress between gold (Au) and PDMS induces the formation of secondary wrinkles along the pyramidal-structured electrode under pressure; these wrinkles function as protuberances on the electrode and enable exceptional linear sensitivity of 4.2 kPa-1 over a wide pressure range. Additionally, our pressure sensor can maintain its performance even after severe mechanical deformations, including repeated stretching up to 30% strain, due to the outstanding strain tolerance of NC-GF. Our sensor's impressive sensing performance and mechanical robustness make it suitable for diverse IoT applications, as demonstrated by its use in wearable pulse monitoring devices and human-robot interaction systems.

Rippling Colloidal Polyelectrolyte Complex for Customized Fingerprints with High Tactile Perception

Fingerprints possess wide applications in personal identification, tactile perception, access control, and anti-counterfeiting. However, latent fingerprints are usually left on touched surfaces, leading to the leakage of personal information. Furthermore, tactile perception greatly decreases when fingerprints are covered by gloves. Customized fingerprints are developed to solve these issues, but it is a challenge to develop fingerprints with various customized patterns using traditional techniques due to their requiring special templates, materials, or instruments. Inspired by ripples on the lake, blowing air is used to generate surface waves on a colloidal polyelectrolyte complex, leading to vertical stratification and the accumulation of particles near the top of the film layer. As water rapidly evaporates, the viscosity of these particles significantly increases and the wave is solidified, forming fingerprint patterns. These customized fingerprints integrate functions of grasping objects, personal identification without leaving latent fingerprints and tactile perception enhancement, which can be applied in information security, anti-counterfeiting, tactile sensors, and biological engineering.

Deep-Learning Enabled Active Biomimetic Multifunctional Hydrogel Electronic Skin

There is huge demand for recreating human skin with the functions of epidermis and dermis for interactions with the physical world. Herein, a biomimetic, ultrasensitive, and multifunctional hydrogel-based electronic skin (BHES) was proposed. Its epidermis function was mimicked using poly(ethylene terephthalate) with nanoscale wrinkles, enabling accurate identification of materials through the capabilities to gain/lose electrons during contact electrification. Internal mechanoreceptor was mimicked by interdigital silver electrodes with stick-slip sensing capabilities to identify textures/roughness. The dermis function was mimicked by patterned microcone hydrogel, achieving pressure sensors with high sensitivity (17.32 mV/Pa), large pressure range (20-5000 Pa), low detection limit, and fast response (10 ms)/recovery time (17 ms). Assisted by deep learning, this BHES achieved high accuracy and minimized interference in identifying materials (95.00% for 10 materials) and textures (97.20% for four roughness cases). By integrating signal acquisition/processing circuits, a wearable drone control system was demonstrated with three-degree-of-freedom movement and enormous potentials for soft robots, self-powered human-machine interaction interfaces of digital twins.

Emerging Trends in Soft Electronics: Integrating Machine Intelligence with Soft Acoustic/Vibration Sensors

In the last decade, soft acoustic/vibration sensors have gained tremendous research interest due to their unique ability to detect broadband acoustic/vibration stimuli, potentializing futuristic applications including voice biometrics, voice-controlled human-machine-interfaces, electronic skin, and skin-mountable healthcare devices. Importantly, to benefit most from these sensors, it is inevitable to use machine learning (ML) to process their output signals; with ML, a more accurate and efficient interpretation of original data is possible. This paper is dedicated to offering an overview of recent advances empowering the development of soft acoustic/vibration sensors and their signal processing using ML. First, the key performance parameters of the sensors are discussed. Second, popular transduction mechanisms for the sensors are addressed, followed by an in-depth overview of each type, covering materials used, structural designs, and sensing performances. Third, potential applications of the sensors are elaborated and fourth, a thorough discussion on ML is conducted, exploring different types of ML, specific ML algorithms suitable for processing acoustic/vibration signals, and current trends in ML-assisted applications. Finally, the challenges and potential opportunities in soft acoustic/vibration sensor and ML research are revealed to offer new insights into future prospects in these fields.

Integrated Multifunctional Electronic Skins with Low-Coupling for Complicated and Accurate Human-Robot Collaboration

Multifunctional capability and low coupling electronic skin (e-skin) is of great significance in advanced robot systems interacting with the human body or the external environment directly. Herein, a multifunctional e-skin system via vertical integrated different sensing materials and structures is presented. The multifunctional e-skin has capacity sensing the proximity, pressure, temperature, and relative humidity simultaneously, with scope of 100-0 mm, 0-30 N, 20-120 °C and 20-70%, respectively. The sensitivity of the four kinds of sensors can be achieved to 0.72 mm^-1 , 16.34 N^-1 , 0.0032 °C^-1 , and 15.2 pF/%RH, respectively. The cross-coupling errors are less than 1.96%, 1.08%, 2.65%, and 1.64%, respectively, after temperature compensation. To be state-of-the-art, a commercial robot is accurately controlled via the multifunctional e-skin system in the complicated environment. The following and safety controlling exhibit both accuracy and high dynamic features. To improve the sensing performance to the insulating objects, machine learning is employed to classify the conductivity during the object approaching, leading to set the threshold in dynamic. The accuracy for isolating the insulator increases from 18% to 88%. Looking forward, the multifunctional e-skin system has broader applications in human-machine collaboration and industrial safety production technology.

A Dual-Responsive Artificial Skin for Tactile and Touchless Interfaces

The progress from intelligent interactions and supplemented/augmented reality requires artificial skins to shift from the single-functional tactile paradigm. Dual-responsive sensors that can both detect pre-contact proximal events and tactile pressure levels enrich the perception dimensions and deliver additional cognitive information. Previous dual-responsive sensors show very limited utilizations only in proximity perception or approaching switches. Whereas, the approaching inputs from the environment should be able to convey more valuable messages. Herein, a flexible iontronic dual-responsive artificial skin is present. The artificial skin is sensitive to external object's applied pressure as well as its approaching, and can elicit information of target material categories encoded in the proximal inputs. Versatile applications are then demonstrated. Dual-mode human-machine interfaces are developed based on the devices, including a manipulation of virtual game characters, navigation and zooming in of electronic maps, and scrolling through electronic documents. More importantly, the proof-of-concept application of an entirely touchless material classification system is demonstrated. Three types of materials (metals, polymers, and human skins) are classified and predicted accurately. These features of the artificial skin make it highly promising for next-generation smart engineered electronics.

Non-equilibrium-Growing Aesthetic Ionic Skin for Fingertip-Like Strain-Undisturbed Tactile Sensation and Texture Recognition

Humans use periodically ridged fingertips to precisely perceive the characteristics of objects via ion-based fast- and slow-adaptive mechanotransduction. However, designing artificial ionic skins with fingertip-like tactile capabilities remains challenging because of the contradiction between structural compliance and pressure sensing accuracy (e.g., anti-interference from stretch and texture recognition). Inspired by the formation and modulus-contrast hierarchical structure of fingertips, an aesthetic ionic skin grown from a non-equilibrium Liesegang patterning process is introduced. This ionic skin with periodic stiff ridges embedded in a soft hydrogel matrix enables strain-undisturbed triboelectric dynamic pressure sensing as well as vibrotactile texture recognition. By coupling with another piezoresistive ionogel, an artificial tactile sensory system is further fabricated as a soft robotic skin to mimic the simultaneous fast- and slow-adaptive multimodal sensations of fingers in grasping actions. This approach may inspire the future design of high-performance ionic tactile sensors for intelligent applications in soft robotics and prosthetics.

Revealing the Meissner Corpuscles in Human Glabrous Skin Using In Vivo Non-Invasive Imaging Techniques

The presence of mechanoreceptors in glabrous skin allows humans to discriminate textures by touch. The amount and distribution of these receptors defines our tactile sensitivity and can be affected by diseases such as diabetes, HIV-related pathologies, and hereditary neuropathies. The quantification of mechanoreceptors as clinical markers by biopsy is an invasive method of diagnosis. We report the localization and quantification of Meissner corpuscles in glabrous skin using in vivo, non-invasive optical microscopy techniques. Our approach is supported by the discovery of epidermal protrusions which are co-localized with Meissner corpuscles. Index fingers, small fingers, and tenar palm regions of ten participants were imaged by optical coherence tomography (OCT) and laser scan microscopy (LSM) to determine the thickness of the stratum corneum and epidermis and to count the Meissner corpuscles. We discovered that regions containing Meissner corpuscles could be easily identified by LSM with an enhanced optical reflectance above the corpuscles, caused by a protrusion of the strongly reflecting epidermis into the stratum corneum with its weak reflectance. We suggest that this local morphology above Meissner corpuscles has a function in tactile perception.

Advances in Biodegradable Electronic Skin: Material Progress and Recent Applications in Sensing, Robotics, and Human-Machine Interfaces

The rapid growth of the electronics industry and proliferation of electronic materials and telecommunications technologies has led to the release of a massive amount of untreated electronic waste (e-waste) into the environment. Consequently, catastrophic environmental damage at the microbiome level and serious human health diseases threaten the natural fate of the planet. Currently, the demand for wearable electronics for applications in personalized medicine, electronic skins (e-skins), and health monitoring is substantial and growing. Therefore, "green" characteristics such as biodegradability, self-healing, and biocompatibility ensure the future application of wearable electronics and e-skins in biomedical engineering and bioanalytical sciences. Leveraging the biodegradability, sustainability, and biocompatibility of natural materials will dramatically influence the fabrication of environmentally friendly e-skins and wearable electronics. Here, the molecular and structural characteristics of biological skins and artificial e-skins are discussed. The focus then turns to the biodegradable materials, including natural and synthetic-polymer-based materials, and their recent applications in the development of biodegradable e-skin in wearable sensors, robotics, and human-machine interfaces (HMIs). Finally, the main challenges and outlook regarding the preparation and application of biodegradable e-skins are critically discussed in a near-future scenario, which is expected to lead to the next generation of biodegradable e-skins.

Crocodile-Skin-Inspired Omnidirectionally Stretchable Pressure Sensor

Stretchable pressure sensors are important components of multimodal electronic skin needed for potentializing numerous Internet of Things applications. In particular, to use pressure sensors in various wearable/skin-attachable electronics, both high deformability and strain-independent sensitivity must be realized. However, previously reported stretchable pressure sensors cannot meet these standards because they exhibit limited stretchability and nonuniform sensitivity under deformation. Herein, inspired by the unique sensory organ of a crocodile, an omnidirectionally stretchable piezoresistive pressure sensor made of polydimethylsiloxane (PDMS)/silver nanowires (AgNWs) composites with microdomes and wrinkled surfaces is developed. The stretchable pressure sensor exhibits high sensitivity that changes negligibly even under uniaxial and biaxial tensile strains of 100% and 50%, respectively. This behavior is attributed to the microdomes responsible for detecting applied pressures being weakly affected by tensile strains, while the isotropic wrinkles between the microdomes deform to effectively reduce the external stress. In addition, because the device comprises all PDMS-based structures, it exhibits outstanding robustness under repeated mechanical stimuli. The device shows strong potential as a wearable pressure sensor and an artificial crocodile sensing organ, successfully detecting applied pressures in various scenarios. Therefore, the pressure sensor is expected to find applications in electronic skin for prosthetics and human-machine interface systems.

Surface Wrinkling for Flexible and Stretchable Sensors

Recent advances in nanolithography, miniaturization, and material science, along with developments in wearable electronics, are pushing the frontiers of sensor technology into the large-scale fabrication of highly sensitive, flexible, stretchable, and multimodal detection systems. Various strategies, including surface engineering, have been developed to control the electrical and mechanical characteristics of sensors. In particular, surface wrinkling provides an effective alternative for improving both the sensing performance and mechanical deformability of flexible and stretchable sensors by releasing interfacial stress, preventing electrical failure, and enlarging surface areas. In this study, recent developments in the fabrication strategies of wrinkling structures for sensor applications are discussed. The fundamental mechanics, geometry control strategies, and various fabricating methods for wrinkling patterns are summarized. Furthermore, the current state of wrinkling approaches and their impacts on the development of various types of sensors, including strain, pressure, temperature, chemical, photodetectors, and multimodal sensors, are reviewed. Finally, existing wrinkling approaches, designs, and sensing strategies are extrapolated into future applications.

A flexible wearable e-skin sensing system for robotic teleoperation

Electronic skin (e-skin) is playing an increasingly important role in health detection, robotic teleoperation, and human-machine interaction, but most e-skins currently lack the integration of on-site signal acquisition and transmission modules. In this paper, we develop a novel flexible wearable e-skin sensing system with 11 sensing channels for robotic teleoperation. The designed sensing system is mainly composed of three components: e-skin sensor, customized flexible printed circuit (FPC), and human-machine interface. The e-skin sensor has 10 stretchable resistors distributed at the proximal and metacarpal joints of each finger respectively and 1 stretchable resistor distributed at the purlicue. The e-skin sensor can be attached to the opisthenar, and thanks to its stretchability, the sensor can detect the bent angle of the finger. The customized FPC, with WiFi module, wirelessly transmits the signal to the terminal device with human-machine interface, and we design a graphical user interface based on the Qt framework for real-time signal acquisition, storage, and display. Based on this developed e-skin system and self-developed robotic multi-fingered hand, we conduct gesture recognition and robotic multi-fingered teleoperation experiments using deep learning techniques and obtain a recognition accuracy of 91.22%. The results demonstrate that the developed e-skin sensing system has great potential in human-machine interaction.

Artificial tactile perception smart finger for material identification based on triboelectric sensing

Tactile perception includes the direct response of tactile corpuscles to environmental stimuli and psychological parameters associated with brain recognition. To date, several artificial haptic-based sensing techniques can accurately measure physical stimuli. However, quantifying the psychological parameters of tactile perception to achieve texture and roughness identification remains challenging. Here, we developed a smart finger with surpassed human tactile perception, which enabled accurate identification of material type and roughness through the integration of triboelectric sensing and machine learning. In principle, as each material has different capabilities to gain or lose electrons, a unique triboelectric fingerprint output will be generated when the triboelectric sensor is in contact with the measured object. The construction of a triboelectric sensor array could further eliminate interference from the environment, and the accuracy rate of material identification was as high as 96.8%. The proposed smart finger provides the possibility to impart artificial tactile perception to manipulators or prosthetics.

All-Nanofibrous Ionic Capacitive Pressure Sensor for Wearable Applications

Currently, with the development of electronic skins (e-skins), wearable pressure sensors with low energy consumption and excellent wearability for long-term physiological signal monitoring are urgently desired but remain a challenge. Capacitive-type devices are desirable candidates for wearable applications, but traditional capacitive pressure sensors are limited by low capacitance and sensitivity. In this study, an all-nanofibrous ionic pressure sensor (IPS) is developed, and the formation of an electrical double layer at the electrode/electrolyte contact interface significantly enhances the capacitance and sensing properties. The IPS is fabricated by sandwiching a nanofibrous ionic gel sensing layer between two thermoplastic polyurethane nanofibrous membranes with graphene electrodes. The IPS has a high sensitivity of 217.5 kPa^-1 in the pressure range of 0-5 kPa, which is much higher than that of conventional capacitive pressure sensors. Combined with the rapid response and recovery speed (30 and 60 ms), the IPS is suitable for real-time monitoring of multiple physiological signals. Moreover, the nanofiber network endows the IPS with excellent air permeability and heat dissipation, which guarantees comfort during long-term wearing. This work provides a viable strategy to improve the wearability of wearable sensors, which can promote healthcare and human-machine interaction applications.

An Open-Environment Tactile Sensing System: Toward Simple and Efficient Material Identification

Robotic perception can have simple and effective sensing functions that are unreachable for humans using only the isolated tactile perception method, with the assistance of a triboelectric nanogenerator (TENG). However, the reliability of triboelectric sensors remains a major challenge due to the inherent environmental limitations. Here, an intelligent tactile sensing system that combines a TENG and deep-learning technology is proposed. Using a triboelectric triple tactile sensor array, typical characteristics of each testing material can be maintained stably even under different contact conditions (touch conditions and external environmental conditions) by extracting features from three independent electrical signals as well as the normalized output signals. Furthermore, a convolutional neural network model is integrated, and a high accuracy of 96.62% is achieved in a material identification task. The tactile sensing system is exhibited to an open environment for material identification and the real-time demonstration. Compared to the complex process that humans must integrate multiple sensing (touching and viewing) to accomplish tactile perception, the proposed sensing system shows a huge advantage in cognitive learning for the visually impaired, biomimetic prosthetics, and virtual spaces construction.

Contact Pattern Recognition of a Flexible Tactile Sensor Based on the CNN-LSTM Fusion Algorithm

Recognizing different contact patterns imposed on tactile sensors plays a very important role in human–machine interaction. In this paper, a flexible tactile sensor with great dynamic response characteristics is designed and manufactured based on polyvinylidene fluoride (PVDF) material. Four contact patterns (stroking, patting, kneading, and scratching) are applied to the tactile sensor, and time sequence data of the four contact patterns are collected. After that, a fusion model based on the convolutional neural network (CNN) and the long-short term memory (LSTM) neural network named CNN-LSTM is constructed. It is used to classify and recognize the four contact patterns loaded on the tactile sensor, and the recognition accuracies of the four patterns are 99.60%, 99.67%, 99.07%, and 99.40%, respectively. At last, a CNN model and a random forest (RF) algorithm model are constructed to recognize the four contact patterns based on the same dataset as those for the CNN-LSTM model. The average accuracies of the four contact patterns based on the CNN-LSTM, the CNN, and the RF algorithm are 99.43%, 96.67%, and 91.39%, respectively. All of the experimental results indicate that the CNN-LSTM constructed in this paper has very efficient performance in recognizing and classifying the contact patterns for the flexible tactile sensor.

Integrating charge mobility, stability and stretchability within conjugated polymer films for stretchable multifunctional sensors

Conjugated polymers (CPs) are promising semiconductors for intrinsically stretchable electronic devices. Ideally, such CPs should exhibit high charge mobility, excellent stability, and high stretchability. However, converging all these desirable properties in CPs has not been achieved via molecular design and/or device engineering. This work details the design, synthesis and characterization of a random polythiophene (RP-T50) containing ~50 mol% of thiophene units with a thermocleavable tertiary ester side chain and ~50 mol% of unsubstituted thiophene units, which, upon thermocleavage of alkyl chains, shows significant improvement of charge mobility and stability. Thermal annealing a RP-T50 film coated on a stretchable polydimethylsiloxane substrate spontaneously generates wrinkling in the polymer film, which effectively enhances the stretchability of the polymer film. The wrinkled RP-T50-based stretchable sensors can effectively detect humidity, ethanol, temperature and light even under 50% uniaxial and 30% biaxial strains. Our discoveries offer new design rationale of strategically applying CPs to intrinsically stretchable electronic systems.

Conjugated polymers are promising semiconductors for stretchable electronic devices but combining important properties such as high charge mobility, stability and stretchability remains challenging. Here, the authors demonstrate the synthesis of a thiophene based semiconducting polymer with cleavable side chains which shows significant improvement of charge mobility, stability and stretchability.

All-Nanofiber Network Structure for Ultrasensitive Piezoresistive Pressure Sensors

Sensing materials with fiber structures are excellent candidates for the fabrication of flexible pressure sensors due to their large specific surface area and abundant contact points. Here, an ultrathin, flexible piezoresistive pressure sensor that consists of a multilayer nanofiber network structure prepared via a simple electrospinning technique is reported. The ultrathin sensitive layer is composite nanofiber films composed of poly (3,4-ethylenedioxythiophene):poly (styrenesulfonate) and polyamide 6 (PEDOT:PSS/PA6) prepared by simultaneous electrospinning. PEDOT:PSS conductive fibers and PA6 elastic fibers are interwoven to form a multilayer network structure that can achieve ultrahigh sensitivity by forming a wealth of contact points during loading. In particular, gold-deposited PA6 fibers as upper and lower flexible electrodes can effectively increase the initial resistance. Due to this special fiber electrode structure, the sensor is able to generate a large electrical signal variability when subjected to a weak external force. The devices with different sensing properties can be obtained by controlling the electrospinning time. The sensor based on the PEDOT:PSS/PA6 nanofiber network has high sensitivity (6554.6 kPa^-1 at 0-1.4 kPa), fast response time (53 ms), and wide detection range (0-60 kPa). Significantly, the device maintains ultrahigh sensitivity when cyclically loaded over 10,000 cycles at 5 kPa, which makes it have great prospects for applications in human health monitoring and motion monitoring.

Laser-Sculptured Hierarchical Spinous Structures for Ultra-High-Sensitivity Iontronic Sensors with a Broad Operation Range

Tactile pressure sensing over a wide operation range (>1 MPa) is challenging for a variety of applications in fields such as aviation, oceanography, and biomedicine. Recently, innovative strategies have been utilized to improve the performances of tactile sensors using specially designed structures, dielectric layers, and electrodes. Here, a hierarchical structural design based on ionic gel films has been utilized to build iontronic pressure sensors with ultrahigh sensitivities and broad operation ranges. Sculptured patterns made by a controlled CO₂ laser scanning process have been produced on polyimide films to achieve two kinds of protrusion structures for high specific surface areas and strength to withstand high pressure. The iontronic sensor has been constructed by adding two screen-printed electrodes of high surface areas to achieve an ultrahigh sensitivity of 2593 kPa^-1 and a wide pressure range from 0 Pa to 3.36 MPa. The prototype device also has a fast response and recovery time of 26 and 13 ms, respectively, and an excellent mechanical durability in the endurance test of over 2700 repeated loading and unloading cycles under a pressure of 1 MPa. Several application examples have been demonstrated, including the detection of physiological signals on human volunteers, the feedback control of intelligent robots, the grasping operation of underwater soft grippers, and the environmental wind-speed monitoring. As such, this work demonstrates a versatile and economical methodology to produce high-performance flexible sensors for various potential applications.

Textile-Based Flexible Capacitive Pressure Sensors: A Review

Flexible capacitive pressure sensors have been widely used in electronic skin, human movement and health monitoring, and human–machine interactions. Recently, electronic textiles afford a valuable alternative to traditional capacitive pressure sensors due to their merits of flexibility, light weight, air permeability, low cost, and feasibility to fit various surfaces. The textile-based functional layers can serve as electrodes, dielectrics, and substrates, and various devices with semi-textile or all-textile structures have been well developed. This paper provides a comprehensive review of recent developments in textile-based flexible capacitive pressure sensors. The latest research progresses on textile devices with sandwich structures, yarn structures, and in-plane structures are introduced, and the influences of different device structures on performance are discussed. The applications of textile-based sensors in human wearable devices, robotic sensing, and human–machine interaction are then summarized. Finally, evolutionary trends, future directions, and challenges are highlighted.

Ultrathin Superhydrophobic Flexible Tactile Sensors for Normal and Shear Force Discrimination

Flexible tactile sensors, with the ability to sense and even discriminate between different mechanical stimuli, can enable real-time and precise monitoring of dexterous and complex robotic motions. However, making them ultrathin and superhydrophobic for practical applications is still a great challenge. Here, superhydrophobic flexible tactile sensors with hierarchical micro- and nanostructures, that is, warped graphene nanosheets adhered to micron-height wrinkled surfaces, were constructed using ultrathin medical tape (40 μm) and graphene. The tactile sensor enables the discrimination of normal and shear forces and senses sliding friction and airflow. Moreover, the tactile sensor exhibits high sensitivity to normal and shear forces, extremely low detection limits (15 Pa for normal forces and 6.4 mN for shear forces), and cyclic robustness. Based on the abovementioned characteristics, the tactile sensor enables real-time and accurate monitoring of the robotic arm's motions, such as moving, gripping, and lifting, during the process of picking up objects. The superhydrophobicity even allows the sensor to monitor the motions of the robotic arm underwater in real time. Our tactile sensors have potential applications in the fields of intelligent robotics and smart prosthetics.

Laser-Induced Corrugated Graphene Films for Integrated Multimodal Sensors

Microstructures play a dominant role in flexible electronics to improve the performance of the devices, including sensitivity, durability, stretchability, and so on. However, the complicated and expensive fabrication process of these microstructures extremely hampers the large-scale application of high-performance devices. Herein, we propose a novel method to fabricate flexible graphene-based sensors with a 3D microstructure by generating laser-induced graphene (LIG) on the 3D printed polyether ether ketone corrugated substrate, which is referred to as CLIG. Based on that, two integrated piezoresistive sensors are developed to monitor the precise strain and pressure signals. Contributed to the 3D corrugated graphene structure, the sensitivities of strain and pressure sensors can be up to 2203.5 and 678.2 kPa^-1, respectively. In particular, the CLIG-based strain sensor exhibits a high resolution to the microdeformation (small as 1 μm or 0.01% strain) and remarkable durability (15,000 cycles); meanwhile, the pressure sensor presents a remarkable working range (1-500 kPa) and fast response time (24 ms). Furthermore, the CLIG-based sensors provide a stable data source in the applications of human-motion monitoring, pressure array, and self-sensing soft robotic systems. High accuracy allows CLIG sensors to recognize more subtle signals, such as pulse, swallowing, gesture distinction of human, and movement status of soft robotics. Overall, this technology shows a promising strategy to fabricate high-performance sensors with high efficiency and low cost.