Skin-inspired quadruple tactile sensors integrated on a robot hand enable object recognition

A Skin-Inspired High-Performance Tactile Sensor for Accurate Recognition of Object Softness

High-performance tactile sensors with skin-sensing properties are crucial for intelligent perception in next-generation smart devices. However, previous studies have mainly focused on the sensitivity and response range of tactile sensation while neglecting the ability to recognize object softness. Therefore, achieving a precise perception of the softness remains a challenge. Here, we report an integrated tactile sensor consisting of a central hole gradient structure pressure sensor and a planar structure strain sensor. The recognition of softness and tactile perception is achieved through the synergistic effect of pressure sensors that sense the applied pressure and strain sensors that recognize the strain of the target object. The results indicate that the softness evaluation parameter (SC) of the integrated structural tactile sensor increases from 0.14 to 0.47 along with Young's modulus of the object decreasing from 2.74 to 0.45 MPa, demonstrating accurate softness recognition. It also exhibits a high sensitivity of 10.55 kPa-1 and an ultrawide linear range of 0-1000 kPa, showing an excellent tactile sensing capability. Further, an intelligent robotic hand system based on integrated structural tactile sensors was developed, which can identify the softness of soft foam and glass and grasp them accurately, indicating human skin-like sensing and grasping capabilities.

Self-Powered and Self-Recoverable Multimodal Force Sensors Based on Trap State and Interfacial Electron Transfer

Multi-dimensional force sensing that combines intensity, location, area and the like could gather a wealth of information from mechanical stimuli. Developing materials with force-induced optical and electrical dual responses would provide unique opportunities to multi-dimensional force sensing, with electrical signals quantifying the force amplitude and the luminescence output providing spatial distribution of force. However, the reliance on external power supply and high-energy excitation source brings significant challenges to the applicability of multi-dimensional force sensors. Here we reported the mechanical energy-driven and sunlight-activated materials with force-induced dual responses, and investigated the underlying mechanisms of self-sustainable force sensing. Theoretical analysis and experimental data unraveled that trap-controlled luminescence and interfacial electron transfer play a major role in force-induced optical and electrical output. These materials were manufactured into pressure sensor with renewable dual-mode output for quantifying and visualization of pressures by electrical and optical output, respectively, without power supply and high-energy irradiation. The quantification of tactile sensation and stimuli localization of mice highlighted the multi-dimensional sensing ability of the sensor. Overall, this self-powered pressure sensor with multimodal output provides more modalities of force sensing, poised to change the way that intelligent devices sense with the world.

Machine intelligence accelerated design of conductive MXene aerogels with programmable properties

Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels' structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels' physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management.

Machine intelligence-accelerated discovery of all-natural plastic substitutes

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model's prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.

A three-dimensionally architected electronic skin mimicking human mechanosensation

Human skin sensing of mechanical stimuli originates from transduction of mechanoreceptors that converts external forces into electrical signals. Although imitating the spatial distribution of those mechanoreceptors can enable developments of electronic skins capable of decoupled sensing of normal/shear forces and strains, it remains elusive. We report a three-dimensionally (3D) architected electronic skin (denoted as 3DAE-Skin) with force and strain sensing components arranged in a 3D layout that mimics that of Merkel cells and Ruffini endings in human skin. This 3DAE-Skin shows excellent decoupled sensing performances of normal force, shear force, and strain and enables development of a tactile system for simultaneous modulus/curvature measurements of an object through touch. Demonstrations include rapid modulus measurements of fruits, bread, and cake with various shapes and degrees of freshness.

Flexible wide-range multidimensional force sensors inspired by bones embedded in muscle

Flexible sensors have been widely studied for use in motion monitoring, human‒machine interactions (HMIs), personalized medicine, and soft intelligent robots. However, their practical application is limited by their low output performance, narrow measuring range, and unidirectional force detection. Here, to achieve flexibility and high performance simultaneously, we developed a flexible wide-range multidimensional force sensor (FWMFS) similar to bones embedded in muscle structures. The adjustable magnetic field endows the FWMFS with multidimensional perception for detecting forces in different directions. The multilayer stacked coils significantly improved the output from the μV to the mV level while ensuring FWMFS miniaturization. The optimized FWMFS exhibited a high voltage sensitivity of 0.227 mV/N (0.5-8.4 N) and 0.047 mV/N (8.4-60 N) in response to normal forces ranging from 0.5 N to 60 N and could detect lateral forces ranging from 0.2-1.1 N and voltage sensitivities of 1.039 mV/N (0.2-0.5 N) and 0.194 mV/N (0.5-1.1 N). In terms of normal force measurements, the FWMFS can monitor finger pressure and sliding trajectories in response to finger taps, as well as measure plantar pressure for assessing human movement. The plantar pressure signals of five human movements collected by the FWMFS were analyzed using the k-nearest neighbors classification algorithm, which achieved a recognition accuracy of 92%. Additionally, an artificial intelligence biometric authentication system is being developed that classifies and recognizes user passwords. Based on the lateral force measurement ability of the FWMFS, the direction of ball movement can be distinguished, and communication systems such as Morse Code can be expanded. This research has significant potential in intelligent sensing and personalized spatial recognition.

Non-equilibrium compression achieving high sensitivity and linearity for iontronic pressure sensors

Flexible pressure sensors with high sensitivity and linearity are highly desirable for robot sensing and human physiological signal detection. However, the current strategies for stabilizing axial microstructures (e.g., micro-pyramids) are mainly susceptible to structural stiffening during compression, thereby limiting the realization of high sensitivity and linearity. Here, we report a bending-induced non-equilibrium compression process that effectively enhances the compressibility of microstructures, thereby crucially improving the efficiency of interfacial area growth of electric double layer (EDL). Based on this principle, we fabricate an iontronic flexible pressure sensor with vertical graphene (VG) array electrodes. Ultra-high sensitivity (185.09 kPa-1) and linearity (R2 = 0.9999) are realized over a wide pressure range (0.49 Pa-66.67 kPa). It also exhibits remarkable mechanical stability during compression and bending. The sensor is successfully employed in a robotic gripping task to recognize the targets of different materials and shapes based on a multilayer perception (MLP) neural network. It opens the door to realizing haptic sensing capabilities for robotic hands and prosthetic limbs.

Skin-Inspired Multi-Modal Mechanoreceptors for Dynamic Haptic Exploration

Active sensing is a fundamental aspect of human and animal interactions with the environment, providing essential information about the hardness, texture, and tackiness of objects. This ability stems from the presence of diverse mechanoreceptors in the skin, capable of detecting a wide range of stimuli and from the sensorimotor control of biological mechanisms. In contrast, existing tactile sensors for robotic applications typically excel in identifying only limited types of information, lacking the versatility of biological mechanoreceptors and the requisite sensing strategies to extract tactile information proactively. Here, inspired by human haptic perception, a skin-inspired artificial 3D mechanoreceptor (SENS) capable of detecting multiple mechanical stimuli is developed to bridge sensing and action in a closed-loop sensorimotor system for dynamic haptic exploration. A tensor-based non-linear theoretical model is established to characterize the 3D deformation (e.g., tensile, compressive, and shear deformation) of SENS, providing guidance for the design and optimization of multimode sensing properties with high fidelity. Based on SENS, a closed-loop robotic system capable of recognizing objects with improved accuracy (≈96%) is further demonstrated. This dynamic haptic exploration approach shows promise for a wide range of applications such as autonomous learning, healthcare, and space and deep-sea exploration.

Highly efficient recognition of similar objects based on ionic robotic tactile sensors

Tactile sensing provides robots the ability of object recognition, fine operation, natural interaction, etc. However, in the actual scenario, robotic tactile recognition of similar objects still faces difficulties such as low efficiency and accuracy, resulting from a lack of high-performance sensors and intelligent recognition algorithms. In this paper, a flexible sensor combining a pyramidal microstructure with a gradient conformal ionic gel coating was demonstrated, exhibiting excellent signal-to-noise ratio (48 dB), low detection limit (1 Pa), high sensitivity (92.96 kPa-1), fast response time (55 ms), and outstanding stability over 15,000 compression-release cycles. Furthermore, a Pressure-Slip Dual-Branch Convolutional Neural Network (PSNet) architecture was proposed to separately extract hardness and texture features and perform feature fusion. In tactile experiments on different kinds of leaves, a recognition rate of 97.16 % was achieved, and surpassed that of human hands recognition (72.5 %). These researches showed the great potential in a broad application in bionic robots, intelligent prostheses, and precise human-computer interaction.

Multimodal Sensors Enabled Autonomous Soft Robotic System with Self-Adaptive Manipulation

Human hands are amazingly skilled at recognizing and handling objects of different sizes and shapes. To date, soft robots rarely demonstrate autonomy equivalent to that of humans for fine perception and dexterous operation. Here, an intelligent soft robotic system with autonomous operation and multimodal perception ability is developed by integrating capacitive sensors with triboelectric sensor. With distributed multiple sensors, our robot system can not only sense and memorize multimodal information but also enable an adaptive grasping method for robotic positioning and grasp control, during which the multimodal sensory information can be captured sensitively and fused at feature level for crossmodally recognizing objects, leading to a highly enhanced recognition capability. The proposed system, combining the performance and physical intelligence of biological systems (i.e., self-adaptive behavior and multimodal perception), will greatly advance the integration of soft actuators and robotics in many fields.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

Microphase-Separated Elastic and Ultrastretchable Ionogel for Reliable Ionic Skin with Multimodal Sensation

Bioinspired artificial skins integrated with reliable human-machine interfaces and stretchable electronic systems have attracted considerable attention. However, the current design faces difficulties in simultaneously achieving satisfactory skin-like mechanical compliance and self-powered multimodal sensing. Here, this work reports a microphase-separated bicontinuous ionogel which possesses skin-like mechanical properties and mimics the multimodal sensing ability of biological skin by ion-driven stimuli-electricity conversion. The ionogel exhibits excellent elasticity and ionic conductivity, high toughness, and ultrastretchability, as well as a Young's modulus similar to that of human skin. Leveraging the ion-polymer interactions enabled selective ion transport, the ionogel can output pulsing or continuous electrical signals in response to diverse stimuli such as strain, touch pressure, and temperature sensitively, demonstrating a unique self-powered multimodal sensing. Furthermore, the ionogel-based I-skin can concurrently sense different stimuli and decouple the variations of the stimuli from the voltage signals with the assistance of a machine-learning model. The ease of fabrication, wide tunability, self-powered multimodal sensing, and the excellent environmental tolerance of the ionogels demonstrate a new strategy in the development of next-generation soft smart mechano-transduction devices.

Intuition-and-Tactile Bimodal Sensing Based on Artificial-Intelligence-Motivated All-Fabric Bionic Electronic Skin for Intelligent Material Perception

Developing electronic skins (e-skins) with extraordinary perception through bionic strategies has far-reaching significance for the intellectualization of robot skins. Here, an artificial intelligence (AI)-motivated all-fabric bionic (AFB) e-skin is proposed, where the overall structure is inspired by the interlocked bionics of the epidermis-dermis interface inside the skin, while the structural design inspiration of the dielectric layer derives from the branch-needle structure of conifers. More importantly, AFB e-skin achieves intuition sensing in proximity mode and tactile sensing in pressure mode based on the fringing and iontronic effects, respectively, and is simulated and verified through COMSOL finite element analysis. The proposed AFB e-skin in pressure mode exhibits maximum sensitivity of 15.06 kPa-1 (<50 kPa), linear sensitivity of 6.06 kPa-1 (50-200 kPa), and fast response/recovery time of 5.6 ms (40 kPa). By integrating AFB e-skin with AI algorithm, and with the support of material inference mechanisms based on dielectric constant and softness/hardness, an intelligent material perception system capable of recognizing nine materials with indistinguishable surfaces within one proximity-pressure cycle is established, demonstrating abilities that surpass human perception.

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.

Human-Inspired Tactile Perception System for Real-Time and Multimodal Detection of Tactile Stimuli

A human can intuitively perceive and comprehend complicated tactile information because the cutaneous receptors distributed in the fingertip skin receive different tactile stimuli simultaneously and the tactile signals are immediately transmitted to the brain. Although many research groups have attempted to mimic the structure and function of human skin, it remains a challenge to implement human-like tactile perception process inside one system. In this study, we developed a real-time and multimodal tactile system that mimics the function of cutaneous receptors and the transduction of tactile stimuli from receptors to the brain, by using multiple sensors, a signal processing and transmission circuit module, and a signal analysis module. The proposed system is capable of simultaneously acquiring four types of decoupled tactile information with a compact system, thereby enabling differentiation between various tactile stimuli, texture characteristics, and consecutive complex motions. This skin-like three-dimensional integrated design provides further opportunities in multimodal tactile sensing systems.

Haptic Sensing and Feedback Techniques toward Virtual Reality

Haptic interactions between human and machines are essential for information acquisition and object manipulation. In virtual reality (VR) system, the haptic sensing device can gather information to construct virtual elements, while the haptic feedback part can transfer feedbacks to human with virtual tactile sensation. Therefore, exploring high-performance haptic sensing and feedback interface imparts closed-loop haptic interaction to VR system. This review summarizes state-of-the-art VR-related haptic sensing and feedback techniques based on the hardware parts. For the haptic sensor, we focus on mechanism scope (piezoresistive, capacitive, piezoelectric, and triboelectric) and introduce force sensor, gesture translation, and touch identification in the functional view. In terms of the haptic feedbacks, methodologies including mechanical, electrical, and elastic actuators are surveyed. In addition, the interactive application of virtual control, immersive entertainment, and medical rehabilitation is also summarized. The challenges of virtual haptic interactions are given including the accuracy, durability, and technical conflicts of the sensing devices, bottlenecks of various feedbacks, as well as the closed-loop interaction system. Besides, the prospects are outlined in artificial intelligence of things, wise information technology of medicine, and multimedia VR areas.

Ultrasensitive Flexible Temperature Sensors Based on Thermal-Mediated Ions Migration Dynamics in Asymmetrical Polymer Bilayers

Accurately acquiring crucial data on the ambient surroundings and physiological processes delivered via subtle temperature fluctuation is vital for advancing artificial intelligence and personal healthcare techniques but is still challenging. Here, we introduce an electrically induced cation injection mechanism based on thermal-mediated ion migration dynamics in an asymmetrical polymer bilayer (APB) composed of nonionic polymer and polyelectrolyte layers, enabling the development of ultrasensitive flexible temperature sensors. The resulting optimized sensor achieves ultrahigh sensitivity, with a thermal index surpassing 10,000 K-1, which allows identifying temperature differences as small as 10 mK with a sensitivity that exceeds 1.5 mK. The mechanism also enables APB sensors to possess good insensitivity to various mechanical deformations─features essential for practical applications. As a proof of concept, we demonstrate the potential impact of APB sensors in various conceptual applications, such as mental tension evaluation, biomimetic thermal tactile, and thermal radiation detection.

Recent Advances in Tactile Sensory Systems: Mechanisms, Fabrication, and Applications

Flexible electronics is a cutting-edge field that has paved the way for artificial tactile systems that mimic biological functions of sensing mechanical stimuli. These systems have an immense potential to enhance human-machine interactions (HMIs). However, tactile sensing still faces formidable challenges in delivering precise and nuanced feedback, such as achieving a high sensitivity to emulate human touch, coping with environmental variability, and devising algorithms that can effectively interpret tactile data for meaningful interactions in diverse contexts. In this review, we summarize the recent advances of tactile sensory systems, such as piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. We also review the state-of-the-art fabrication techniques for artificial tactile sensors. Next, we focus on the potential applications of HMIs, such as intelligent robotics, wearable devices, prosthetics, and medical healthcare. Finally, we conclude with the challenges and future development trends of tactile sensors.

Self-Adaptive Perception of Object's Deformability with Multiple Deformation Attributes Utilizing Biomimetic Mechanoreceptors

The perception of object's deformability in unstructured interactions relies on both kinesthetic and cutaneous cues to adapt the uncertainties of an object. However, the existing tactile sensors cannot provide adequate cutaneous cues to self-adaptively estimate the material softness, especially in non-standard contact scenarios where the interacting object deviates from the assumption of an elastic half-infinite body. This paper proposes an innovative design of a tactile sensor that integrates the capabilities of two slow-adapting mechanoreceptors within a soft medium, allowing self-decoupled sensing of local pressure and strain at specific locations within the contact interface. By leveraging these localized cutaneous cues, the sensor can accurately and self-adaptively measure the material softness of an object, accommodating variations in thicknesses and applied forces. Furthermore, when combined with a kinesthetic cue from the robot, the sensor can enhance tactile expression by the synergy of two relevant deformation attributes, including material softness and compliance. It is demonstrated that the biomimetic fusion of tactile information can fully comprehend the deformability of an object, hence facilitating robotic decision-making and dexterous manipulation.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Journey from human hands to robot hands: biological inspiration of anthropomorphic robotic manipulators

The development of robotic hands that can replicate the complex movements and dexterity of the human hand has been a longstanding challenge for scientists and engineers. A human hand is capable of not only delicate operation but also crushing with power. For performing tasks alongside and in place of humans, an anthropomorphic manipulator design is considered the most advanced implementation, because it is able to follow humans' examples and use tools designed for people. In this article, we explore the journey from human hands to robot hands, tracing the historical advancements and current state-of-the-art in hand manipulator development. We begin by investigating the anatomy and function of the human hand, highlighting the bone-tendon-muscle structure, skin properties, and motion mechanisms. We then delve into the field of robotic hand development, focusing on highly anthropomorphic designs. Finally, we identify the requirements and directions for achieving the next level of robotic hand technology.

Flexible Ag2Se Thermoelectric Films Enable the Multifunctional Thermal Perception in Electronic Skins

Skin is critical for shaping our interactions with the environment. The electronic skin (E-skin) has emerged as a promising interface for medical devices to replicate the functions of damaged skin. However, exploration of thermal perception, which is crucial for physiological sensing, has been limited. In this work, a multifunctional E-skin based on flexible thermoelectric Ag2Se films is proposed, which utilizes the Seebeck effect to replicate the sensory functions of natural skin. The E-skin can enable capabilities including temperature perception, tactile perception, contactless perception, and material recognition by analyzing the thermal conduction behaviors of various materials. To further validate the capabilities of constructed E-skins, a wearable device with multiple sensory channels was fabricated and tested for gesture recognition. This work highlights the potential for using flexible thermoelectric materials in advanced biomedical applications including health monitoring and smart prosthetics.

Large-area, untethered, metamorphic, and omnidirectionally stretchable multiplexing self-powered triboelectric skins

Large-area metamorphic stretchable sensor networks are desirable in haptic sensing and next-generation electronics. Triboelectric nanogenerator-based self-powered tactile sensors in single-electrode mode constitute one of the best solutions with ideal attributes. However, their large-area multiplexing utilizations are restricted by severe misrecognition between sensing nodes and high-density internal circuits. Here, we provide an electrical signal shielding strategy delivering a large-area multiplexing self-powered untethered triboelectric electronic skin (UTE-skin) with an ultralow misrecognition rate (0.20%). An omnidirectionally stretchable carbon black-Ecoflex composite-based shielding layer is developed to effectively attenuate electrostatic interference from wirings, guaranteeing low-level noise in sensing matrices. UTE-skin operates reliably under 100% uniaxial, 100% biaxial, and 400% isotropic strains, achieving high-quality pressure imaging and multi-touch real-time visualization. Smart gloves for tactile recognition, intelligent insoles for gait analysis, and deformable human-machine interfaces are demonstrated. This work signifies a substantial breakthrough in haptic sensing, offering solutions for the previously challenging issue of large-area multiplexing sensing arrays.

A Multifunctional Flexible Tactile Sensor Based on Resistive Effect for Simultaneous Sensing of Pressure and Temperature

Flexible tactile sensors with multifunctional sensing functions have attracted much attention due to their wide applications in artificial limbs, intelligent robots, human-machine interfaces, and health monitoring devices. Here, a multifunctional flexible tactile sensor based on resistive effect for simultaneous sensing of pressure and temperature is reported. The sensor features a simple design with patterned metal film on a soft substrate with cavities and protrusions. The decoupling of pressure and temperature sensing is achieved by the reasonable arrangement of metal layers in the patterned metal film. Systematically experimental and numerical studies are carried out to reveal the multifunctional sensing mechanism and show that the proposed sensor exhibits good linearity, fast response, high stability, good mechanical flexibility, and good microfabrication compatibility. Demonstrations of the multifunctional flexible tactile sensor to monitor touch, breathing, pulse and objects grabbing/releasing in various application scenarios involving coupled temperature/pressure stimuli illustrate its excellent capability of measuring pressure and temperature simultaneously. These results offer an effective tool for multifunctional sensing of pressure and temperature and create engineering opportunities for applications of wearable health monitoring and human-machine interfaces.

Intelligent flexible manipulator system based on flexible tactile sensing (IFMSFTS) for kiwifruit ripeness classification

BACKGROUND

Consumers all throughout the world enjoy kiwifruit. After harvest, there are as much as 20-25% of kiwifruit lost along the entire industrial chain. An intelligent flexible manipulator system based on flexible tactile sensing (IFMSFTS) was created to automatically and intelligently classify kiwifruit ripeness in order to minimize loss.

RESULT

The flexible manipulator is coupled with the flexible tactile sensor. When kiwifruits are being gripped by the manipulator, the flexible sensor perceives their firmness, and the mapping relationship between firmness and ripeness allows for the prediction and evaluation of the kiwifruit's ripeness. Principal component analysis (PCA) is employed to minimize the dimension of the sample firmness data set. K-Nearest neighbor (KNN) and support vector machine (SVM) classifiers are utilized to train and test the data. The findings indicate that PCA-KNN's classification accuracy is 97.5% and PCA-SVM's classification accuracy is 96.24%. The first is a better fit.

CONCLUSION

IFMSFTS can precisely classify ripeness, effectively address the issue of fruit loss, and realize the sustainable and clean production of fruit by sensing the firmness of kiwifruit and relying on the mapping link between firmness and ripeness. © 2023 Society of Chemical Industry.

Data-Driven Contact-Based Thermosensation for Enhanced Tactile Recognition

Thermal feedback plays an important role in tactile perception, greatly influencing fields such as autonomous robot systems and virtual reality. The further development of intelligent systems demands enhanced thermosensation, such as the measurement of thermal properties of objects to aid in more accurate system perception. However, this continues to present certain challenges in contact-based scenarios. For this reason, this study innovates by using the concept of semi-infinite equivalence to design a thermosensation system. A discrete transient heat transfer model was established. Subsequently, a data-driven method was introduced, integrating the developed model with a back propagation (BP) neural network containing dual hidden layers, to facilitate accurate calculation for contact materials. The network was trained using the thermophysical data of 67 types of materials generated by the heat transfer model. An experimental setup, employing flexible thin-film devices, was constructed to measure three solid materials under various heating conditions. Results indicated that measurement errors stayed within 10% for thermal conductivity and 20% for thermal diffusion. This approach not only enables quick, quantitative calculation and identification of contact materials but also simplifies the measurement process by eliminating the need for initial temperature adjustments, and minimizing errors due to model complexity.

Biomimetic Ion Channel Regulation for Temperature-Pressure Decoupled Tactile Perception

The perception of temperature and pressure of skin plays a vital role in joint movement, hand grasp, emotional expression, and self-protection of human. Among many biomimetic materials, ionic gels are uniquely suited to simulate the function of skin due to its ionic transport mechanism. However, both the temperature and pressure sensing are heavily dependent on the changes in ionic conductivity, making it impossible to decouple the temperature and pressure signals. Here, a pressure-insensitive and temperature-modulated ion channel is designed by synergistic strategies for gel skeleton's compact packing and ultra-thin structure, mimicking the function of the temperature ion channel in human skin. This ion-confined gel can completely suppress the pressure response of the temperature sensing layer. Furthermore, a temperature-pressure decoupled ionic sensor is fabricated and it is demonstrated that the ionic sensor can sense complex signals of temperature and pressure. This novel and effective approach has great potential to overcome one of the current barriers in developing ionic skin and extending its applications.

Micropyramid Array Bimodal Electronic Skin for Intelligent Material and Surface Shape Perception Based on Capacitive Sensing

Developing electronic skins (e-skins) that are comparable to or even beyond human tactile perception holds significant importance in advancing the process of intellectualization. In this context, a machine-learning-motivated micropyramid array bimodal (MAB) e-skin based on capacitive sensing is reported, which enables spatial mapping applications based on bimodal sensing (proximity and pressure) implemented via fringing and iontronic effects, such as contactless measurement of 3D objects and contact recognition of Braille letters. Benefiting from the iontronic effect and single-micropyramid structure, the MAB e-skin in pressure mode yields impressive features: a maximum sensitivity of 655.3 kPa-1 (below 0.5 kPa), a linear sensitivity of 327.9 kPa-1 (0.5-15 kPa), and an ultralow limit of detection of 0.2 Pa. With the assistance of multilayer perceptron and convolutional neural network, the MAB e-skin can accurately perceive 6 materials and 10 surface shapes based on the training and learning using the collected datasets from proximity and pressure modes, thus allowing it to achieve the precise perception of different objects within one proximity-pressure cycle. The development of this MAB e-skin opens a new avenue for robotic skin and the expansion of advanced applications.

Microstructured Polyelectrolyte Elastomer-Based Ionotronic Sensors with High Sensitivities and Excellent Stability for Artificial Skins

High-performance flexible pressure sensors are highly demanded for artificial tactile sensing. Using ionic conductors as the dielectric layer has enabled ionotronic pressure sensors with high sensitivities owing to giant capacitance of the electric double layer (EDL) formed at the ionic conductor/electronic conductor interface. However, conventional ionotronic sensors suffer from leakage, which greatly hinders long-term stability and practical applications. Herein, a leakage-free polyelectrolyte elastomer as the dielectric layer for ionotronic sensors is synthesized. The mechanical and electrical properties of the polyelectrolyte elastomer are optimized, a micropyramid array is constructed, and it is used as the dielectric layer for an ionotronic pressure sensor with marked performances. The obtained sensor exhibits a sensitivity of 69.6 kPa-1 , a high upper detecting limit on the order of 1 MPa, a fast response/recovery speed of ≈6 ms, and excellent stability under both static and dynamic loads. Notably, the sensor retains a high sensitivity of 4.96 kPa-1 at 500 kPa, and its broad sensing range within high-pressure realm enables a brand-new coding strategy. The applications of the sensor as a wearable keyboard and a quasicontinuous controller for a robotic arm are demonstrated. Durable and highly sensitive ionotronic sensors potentialize high-performance artificial skins for soft robots, human-machine interfaces, and beyond.

Artificial Intelligence-Powered Electronic Skin

Skin-interfaced electronics is gradually changing medical practices by enabling continuous and noninvasive tracking of physiological and biochemical information. With the rise of big data and digital medicine, next-generation electronic skin (e-skin) will be able to use artificial intelligence (AI) to optimize its design as well as uncover user-personalized health profiles. Recent multimodal e-skin platforms have already employed machine learning (ML) algorithms for autonomous data analytics. Unfortunately, there is a lack of appropriate AI protocols and guidelines for e-skin devices, resulting in overly complex models and non-reproducible conclusions for simple applications. This review aims to present AI technologies in e-skin hardware and assess their potential for new inspired integrated platform solutions. We outline recent breakthroughs in AI strategies and their applications in engineering e-skins as well as understanding health information collected by e-skins, highlighting the transformative deployment of AI in robotics, prosthetics, virtual reality, and personalized healthcare. We also discuss the challenges and prospects of AI-powered e-skins as well as predictions for the future trajectory of smart e-skins.

High-Performance Dual-Responsive Sensing Skin Enabled by Bioinspired Transduction of Coplanar Square-Loop Electrodes

Advancements in intelligent robots and human-machine interaction necessitate a shift in artificial skins toward multimodal perception. Dual-responsive skins that can detect proximity and pressure information are significant to establishing continuous sensing of interaction processes and extending interactive application scenarios. To address the current limitations of inadequate dual-mode performance, such as limited proximal response change and low tactile sensitivity, this paper presents a bioinspired complementary gradient architecture-enabled (CGA) transduction design and a high-performance dual-responsive skin based on coplanar square-loop electrodes. Through systematic investigation into the transduction of various electrode configurations, comparative results reveal the remarkable potential of coplanar electrodes to deliver superior dual-mode performance without compromise. Simulations and experiments prove that the proposed CGA response mechanism can capture local interface deformation and overall compression signals, further enhancing response sensitivity. The final developed artificial skin is sensitive to external pressure and the approach of objects simultaneously, exhibiting a long detection distance (∼40 mm), a high proximity response (>0.4), and outstanding touch sensitivity (0.131 kPa-1). Furthermore, we demonstrate proof-of-concept applications for the proposed sensing skin in a dual-mode teleoperation interface and adaptive grasping interactions.

A fully integrated, standalone stretchable device platform with in-sensor adaptive machine learning for rehabilitation

Post-surgical treatments of the human throat often require continuous monitoring of diverse vital and muscle activities. However, wireless, continuous monitoring and analysis of these activities directly from the throat skin have not been developed. Here, we report the design and validation of a fully integrated standalone stretchable device platform that provides wireless measurements and machine learning-based analysis of diverse vibrations and muscle electrical activities from the throat. We demonstrate that the modified composite hydrogel with low contact impedance and reduced adhesion provides high-quality long-term monitoring of local muscle electrical signals. We show that the integrated triaxial broad-band accelerometer also measures large body movements and subtle physiological activities/vibrations. We find that the combined data processed by a 2D-like sequential feature extractor with fully connected neurons facilitates the classification of various motion/speech features at a high accuracy of over 90%, which adapts to the data with noise from motion artifacts or the data from new human subjects. The resulting standalone stretchable device with wireless monitoring and machine learning-based processing capabilities paves the way to design and apply wearable skin-interfaced systems for the remote monitoring and treatment evaluation of various diseases.

Touchless Artificial Perception beyond Fingertip Probing

Touchless perception technology allows us to acquire information beyond the contact interfaces, making it ideal for scenarios where physical engagements are not possible. Unlike tactile devices, which have so far achieved impressive results, touchless strategies are fascinating yet underdeveloped. We envisage that touchless technologies could be powerful supplements to current haptics. In this Perspective, we include emerging touchless electronics, aiming to provide a broader and comprehensive picture toward artificial perceptual realm. We overview popular touchless protocols, sketch what could be detected by touchless probing, and summarize their latest spectacular achievements. In addition, we present the promises and challenges posed by touchless technologies and discuss possible directions for their future deployments.

Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses

The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in the intelligentialize of human society. As an essential component that bridges the physical world and digital signals, flexible sensors are evolving from a single sensing element to a smarter system, which is capable of highly efficient acquisition, analysis, and even perception of vast, multifaceted data. While challenging from a manual perspective, the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm (machine learning) and the framework (artificial synapses) level. This review presents the recent progress of the emerging AI-driven, intelligent flexible sensing systems. The basic concept of machine learning and artificial synapses are introduced. The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed, which significantly advances the applications such as flexible sensory systems, soft/humanoid robotics, and human activity monitoring. As two of the most profound innovations in the twenty-first century, the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

Intelligent Recognition Using Ultralight Multifunctional Nano-Layered Carbon Aerogel Sensors with Human-Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion. Under limited visual conditions, people can sense a variety of tactile signals to identify objects accurately and rapidly. However, replicating this unique capability in robots remains a significant challenge. Here, we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure, temperature, material recognition and 3D location capabilities, which is combined with multimodal supervised learning algorithms for object recognition. The sensor exhibits human-like pressure (0.04-100 kPa) and temperature (21.5-66.2 °C) detection, millisecond response times (11 ms), a pressure sensitivity of 92.22 kPa-1 and triboelectric durability of over 6000 cycles. The devised algorithm has universality and can accommodate a range of application scenarios. The tactile system can identify common foods in a kitchen scene with 94.63% accuracy and explore the topographic and geomorphic features of a Mars scene with 100% accuracy. This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing, recognition and intelligence.

Do robots outperform humans in human-centered domains?

The incessant progress of robotic technology and rationalization of human manpower induces high expectations in society, but also resentment and even fear. In this paper, we present a quantitative normalized comparison of performance, to shine a light onto the pressing question, "How close is the current state of humanoid robotics to outperforming humans in their typical functions (e.g., locomotion, manipulation), and their underlying structures (e.g., actuators/muscles) in human-centered domains?" This is the most comprehensive comparison of the literature so far. Most state-of-the-art robotic structures required for visual, tactile, or vestibular perception outperform human structures at the cost of slightly higher mass and volume. Electromagnetic and fluidic actuation outperform human muscles w.r.t. speed, endurance, force density, and power density, excluding components for energy storage and conversion. Artificial joints and links can compete with the human skeleton. In contrast, the comparison of locomotion functions shows that robots are trailing behind in energy efficiency, operational time, and transportation costs. Robots are capable of obstacle negotiation, object manipulation, swimming, playing soccer, or vehicle operation. Despite the impressive advances of humanoid robots in the last two decades, current robots are not yet reaching the dexterity and versatility to cope with more complex manipulation and locomotion tasks (e.g., in confined spaces). We conclude that state-of-the-art humanoid robotics is far from matching the dexterity and versatility of human beings. Despite the outperforming technical structures, robot functions are inferior to human ones, even with tethered robots that could place heavy auxiliary components off-board. The persistent advances in robotics let us anticipate the diminishing of the gap.

Bioinspired Young's Modulus-Hierarchical E-Skin with Decoupling Multimodality and Neuromorphic Encoding Outputs to Biosystems

As key interfaces for the disabled, optimal prosthetics should elicit natural sensations of skin touch or proprioception, by unambiguously delivering the multimodal signals acquired by the prosthetics to the nervous system, which still remains challenging. Here, a bioinspired temperature-pressure electronic skin with decoupling capability (TPD e-skin), inspired by the high-low modulus hierarchical structure of human skin, is developed to restore such functionality. Due to the bionic dual-state amplifying microstructure and contact resistance modulation, the MXene TPD e-skin exhibits high sensitivity over a wide pressure range and excellent temperature insensitivity (91.2% reduction). Additionally, the high-low modulus structural configuration enables the pressure insensitivity of the thermistor. Furthermore, a neural model is proposed to neutrally code the temperature-pressure signals into three types of nerve-acceptable frequency signals, corresponding to thermoreceptors, slow-adapting receptors, and fast-adapting receptors. Four operational states in the time domain are also distinguished after the neural coding in the frequency domain. Besides, a brain-like machine learning-based fusion process for frequency signals is also constructed to analyze the frequency pattern and achieve object recognition with a high accuracy of 98.7%. The TPD neural system offers promising potential to enable advanced prosthetic devices with the capability of multimodality-decoupling sensing and deep neural integration.

Shear-Pressure Decoupling and Accurate Perception of Shear Directions in Ionic Sensors by Analyzing the Frequency-Dependent Ionic Behavior

In artificial tactile sensing, to emulate the human sense of touch, independent perception of shear force and pressure is important. Decoupling the pressure and shear force is a challenging task for ensuring stable grasping manipulation for both soft and brittle objects. This study introduces a deformable ion gel-based tactile sensor that is capable of distinguishing pressure from shear force when pressurized shear force is applied in any direction. Recognition of the decoupled forces and precise shear directions is enabled by acquiring tactile data at only two frequencies (20 Hz and 10 kHz) based on the frequency-dependent ion dynamics. This study demonstrates monitoring the changes in pressure, shear force, and shear directions while performing practical robotic actions, such as pouring a water bottle, opening a water bottle cap, and picking up a book and placing it on a shelf.

All-Fabric Capacitive Pressure Sensors with Piezoelectric Nanofibers for Wearable Electronics and Robotic Sensing

Flexible pressure sensors are increasingly sought after for applications ranging from physiological signal monitoring to robotic sensing; however, the challenges associated with fabricating highly sensitive, comfortable, and cost-effective sensors remain formidable. This study presents a high-performance, all-fabric capacitive pressure sensor (AFCPS) that incorporates piezoelectric nanofibers. Through the meticulous optimization of conductive fiber electrodes and P(VDF-TrFE) nanofiber dielectric layers, the AFCPS exhibits exceptional attributes such as high sensitivity (4.05 kPa-1), an ultralow detection limit (0.6 Pa), an extensive detection range (∼100 kPa), rapid response time (<26 ms), and robust stability (>14,000 cycles). The sensor's porous structure enhances its compressibility, while its piezoelectric properties expedite charge separation, thereby increasing the interface capacitance and augmenting overall performance. These features are elucidated further through multiphysical field-coupling simulations and experimental testing. Owing to its comprehensive superior performance, the AFCPS has demonstrated its efficacy in monitoring human activity and physiological signals, as well as in discerning soft robotic grasping movements. Additionally, we have successfully implemented multiple AFCPS units as pressure sensor arrays to ascertain spatial pressure distribution and enable intelligent robotic gripping. Our research underscores the promising potential of the AFCPS device in wearable electronics and robotic sensing, thereby contributing significantly to the advancement of high-performance fabric-based sensors.

Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross-Talk of Bimodal Resistive Sensory Inputs

Tactility in biological organisms is a faculty that relies on a variety of specialized receptors. The bimodal sensorized skin, featured in this study, combines soft resistive composites that attribute the skin with mechano- and thermoreceptive capabilities. Mimicking the position of the different natural receptors in different depths of the skin layers, a multi-layer arrangement of the soft resistive composites is achieved. However, the magnitude of the signal response and the localization ability of the stimulus change with lighter presses of the bimodal skin. Hence, a learning-based approach is employed that can help achieve predictions about the stimulus using 4500 probes. Similar to the cognitive functions in the human brain, the cross-talk of sensory information between the two types of sensory information allows the learning architecture to make more accurate predictions of localization, depth, and temperature of the stimulus contiguously. Localization accuracies of 1.8 mm, depth errors of 0.22 mm, and temperature errors of 8.2 °C using 8 mechanoreceptive and 8 thermoreceptive sensing elements are achieved for the smaller inter-element distances. Combining the bimodal sensing multilayer skins with the neural network learning approach brings the artificial tactile interface one step closer to imitating the sensory capabilities of biological skin.

Progress and prospects in flexible tactile sensors

Flexible tactile sensors have the advantages of large deformation detection, high fault tolerance, and excellent conformability, which enable conformal integration onto the complex surface of human skin for long-term bio-signal monitoring. The breakthrough of flexible tactile sensors rather than conventional tactile sensors greatly expanded application scenarios. Flexible tactile sensors are applied in fields including not only intelligent wearable devices for gaming but also electronic skins, disease diagnosis devices, health monitoring devices, intelligent neck pillows, and intelligent massage devices in the medical field; intelligent bracelets and metaverse gloves in the consumer field; as well as even brain-computer interfaces. Therefore, it is necessary to provide an overview of the current technological level and future development of flexible tactile sensors to ease and expedite their deployment and to make the critical transition from the laboratory to the market. This paper discusses the materials and preparation technologies of flexible tactile sensors, summarizing various applications in human signal monitoring, robotic tactile sensing, and human-machine interaction. Finally, the current challenges on flexible tactile sensors are also briefly discussed, providing some prospects for future directions.

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems.

Sensing in Soft Robotics

Soft robotics is an exciting field of science and technology that enables robots to manipulate objects with human-like dexterity. Soft robots can handle delicate objects with care, access remote areas, and offer realistic feedback on their handling performance. However, increased dexterity and mechanical compliance of soft robots come with the need for accurate control of the position and shape of these robots. Therefore, soft robots must be equipped with sensors for better perception of their surroundings, location, force, temperature, shape, and other stimuli for effective usage. This review highlights recent progress in sensing feedback technologies for soft robotic applications. It begins with an introduction to actuation technologies and material selection in soft robotics, followed by an in-depth exploration of various types of sensors, their integration methods, and the benefits of multimodal sensing, signal processing, and control strategies. A short description of current market leaders in soft robotics is also included in the review to illustrate the growing demands of this technology. By examining the latest advancements in sensing feedback technologies for soft robots, this review aims to highlight the potential of soft robotics and inspire innovation in the field.

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.

Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform

The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.

Stretchable, Adhesive, and Bioinspired Visual Electronic Skin with Strain/Temperature/Pressure Multimodal Non-Interference Sensing

It is highly desirable to construct a single-multimodal sensor that could synchronously perceive multiple stimuli without interference. Here, we propose an adhesive multifunctional chromotropic electronic skin (MCES) that can respond to and distinguish three different stimuli of stain, temperature, and pressure within the two-terminal sensing unit. The mutually discriminating "three-in-one" device converts strain into capacitance and pressure into voltage signals for a tactile stimulus response and produces visual color changes against temperature. In this MCES system, the interdigital capacitor sensor shows high linearity (R² = 0.998), and temperature sensing is realized via reversible multicolor switching bioinspired by the chameleon, showing attractive potential in visualization interaction. Notably, the energy-harvesting triboelectric nanogenerator in MCES can not only detect pressure incentive but also identify objective material species. Looking forward, these findings promise for multimodal sensor technology with reduced complexity and production costs that are highly anticipated in soft robotics, prosthetics, and human-machine interaction applications.

Bionic Tactile-Gustatory Receptor for Object Identification Based on All-Polymer Electrochemical Transistor

Human sensory receptors enable the real world to be perceived effortlessly. Hence, massive efforts have been devoted to the development of bionic receptors capable of identifying objects. Unfortunately, most of the existing devices are limited to single sensory emulation and are established on solid-state electronic technologies, which are incompatible with the biological reactions occurring in electrolyte media. Here, an iontronic tactile-gustatory receptor using an all-polymer electrochemical transistor (AECT) is presented. The sensor is biocompatible with the operation voltage of 0.1 V, which is 1 to 2 orders lower than those of reported values. By this study, one receptor is able to accurately recognize various objects perceived by the human tactile and gustatory system without complex circuitry. Additionally, to promote its further application, flexible AECT arrays with channel length of 2 µm and density of 104 167 transistors cm^-2 (yield of 97%) are fabricated, 1 to 5 orders higher than those of related works. Finally, a flexible integrated network for electrocardiogram recording is successfully constructed. This study moves a step forward toward state-of-the-art bionic sensors.

Boron nitride-enabled printing of a highly sensitive and flexible iontronic pressure sensing system for spatial mapping

Recently, flexible iontronic pressure sensors (FIPSs) with higher sensitivities and wider sensing ranges than conventional capacitive sensors have been widely investigated. Due to the difficulty of fabricating the nanostructures that are commonly used on electrodes and ionic layers by screen printing techniques, strategies for fabricating such devices using these techniques to drive their mass production have rarely been reported. Herein, for the first time, we employed a 2-dimensional (2D) hexagonal boron nitride (h-BN) as both an additive and an ionic liquid reservoir in an ionic film, making the sensor printable and significantly improving its sensitivity and sensing range through screen printing. The engineered sensor exhibited high sensitivity (Smin> 261.4 kPa-1) and a broad sensing range (0.05-450 kPa), and it was capable of stable operation at a high pressure (400 kPa) for more than 5000 cycles. In addition, the integrated sensor array system allowed accurate monitoring of wrist pressure and showed great potential for health care systems. We believe that using h-BN as an additive in an ionic material for screen-printed FIPS could greatly inspire research on 2D materials for similar systems and other types of sensors. Hexagonal boron nitride (h-BN) was employed for the first time to make iontronic pressure sensor arrays with high sensitivity and a broad sensing range by screen printing.

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.