A Machine-Learning-Enhanced Simultaneous and Multimodal Sensor Based on Moist-Electric Powered Graphene Oxide

Advances in Machine Learning-Driven Flexible Strain Sensors: Challenges, Innovations, and Applications

Flexible strain sensors have garnered significant attention due to their high sensitivity, rapid response, and flexibility. Recent innovations, particularly those incorporating machine learning, have significantly enhanced their stability, sensitivity, and adaptability, positioning these sensors as promising solutions in health monitoring, human-computer interaction, and smart home applications. However, challenges remain in optimizing sensor materials for enhanced responsiveness, durability, and stability. Moreover, the development of machine learning-based strain sensors faces obstacles, including algorithmic limitations, low noise tolerance in complex environments, and limited model interpretability. This review systematically evaluates the latest advancements in flexible strain sensors, emphasizing the critical role of machine learning in performance enhancement. It further explores the shift from traditional machine learning methods to deep learning approaches, elucidating the potential applications that these algorithms facilitate. Finally, we discuss future research trajectories, highlighting both opportunities and challenges that may guide the next wave of innovations in this dynamic field.

Flexible Optoelectronic Hybrid Microfiber Long-period Grating Multimodal Sensor

Flexible wearable biosensors have emerged as a promising tool for tracking dynamic glycemic profiles of human body in diabetes management. However, it remains a challenge to balance the shrunken device space and multiple redundant sensing arrays for further advancement in miniaturization of multimodal sensors. Herein, this work proposes an entirely new optoelectronic hybrid multimodal optical fiber sensor which is composed of laser patterning of polydimethylsiloxane (PDMS) to form laser-induced graphene (LIG) as the interdigital electrodes, and a long period grating (LPG) prepared from an optical microfiber encapsulated into the PDMS modulated by periodical structure of LIG electrodes. This operation can simultaneously integrate two heterogeneous sensing mechanisms, optical and electrical, into a single sensor in a compact manner. Combining the LIG electrode with conductive hydrogel, a flexible glucose biosensor based on electrical mechanism is constructed by loading glucose oxidase into the hydrogel. Meanwhile, the microfiber LPG can also be served as a spectroscopically available sensor for biomechanical monitoring. Optical and electrical sensors can work simultaneously but independently of each other, particularly in the scene of wound healing for rat model and movement for human exercise. This platform represents a pivotal step toward multifunctional sensors that enable measurements of biomechanical information and glucose.

A self-sustained moist-electric generator with enhanced energy density and longevity through a bilayer approach

Although MEG is being developed as a green renewable energy technology, there remains significant room for improvement in self-sustained power supply, generation duration, and energy density. In this study, we present a self-sustained, high-performance MEG device with a bilayer structure. The lower hydrogel layer incorporates graphene oxide (GO) and carbon nanotubes (CNTs) as the active materials, whereas the upper aerogel layer is comprised of pyrrole-modified graphene oxide (PGO). This design generates a dual-gradient structure (ion density gradient and relative humidity gradient), enabling continuous power generation from the intrinsic moisture in the hydrogel. The device can operate for up to 16 days without external water and extend its operation to 45 days with added moisture. Remarkably, encapsulating this MEG maintains its high performance output even after nearly three months. The short-circuit current of MEG reaches 1695 μA, with an energy density of 809.2 μW h cm-2, which is considerably higher than those reported in previous studies on MEG. This work highlights a promising approach for long-term, self-sustained power generation, with potential applications in environmental sensing and low-power devices.

Bioinspired Sensor and Actuator Hybrid Pixel Array for Moisture/Temperature Mapping, Electrothermal Display and Programmable Deformation

Natural soft organisms with sophisticated perception and deformation abilities provide inspiration for developing flexible electronics. However, the development of flexible sensing and actuating hybrid systems remains a challenge. Herein, we report a bioinspired sensor and actuator hybrid pixel array (SA-HPA) that enables moisture/temperature mapping, electrothermal display, and programmable electrothermal deformation. The SA-HPA is fabricated by femtosecond laser patterning of Cu electrodes/circuits, controllable deposition of graphene, selective encapsulation, and liquid crystal elastomer integration. The versatile SA-HPA can work as a sensor array for temperature and moisture recognition, and the interference between them can be overcome by the selective encapsulation of adjacent pixels. Additionally, SA-HPAs can also serve as electrothermal pixels for programmable infrared display and actuation. As a proof-of-concept, a soft robotic system that enables active temperature and humidity sensing was demonstrated. We deem that the SA-HPA may provide a new paradigm for developing soft electronics.

Deep-Learning-Based Analysis of Electronic Skin Sensing Data

E-skin is an integrated electronic system that can mimic the perceptual ability of human skin. Traditional analysis methods struggle to handle complex e-skin data, which include time series and multiple patterns, especially when dealing with intricate signals and real-time responses. Recently, deep learning techniques, such as the convolutional neural network, recurrent neural network, and transformer methods, provide effective solutions that can automatically extract data features and recognize patterns, significantly improving the analysis of e-skin data. Deep learning is not only capable of handling multimodal data but can also provide real-time response and personalized predictions in dynamic environments. Nevertheless, problems such as insufficient data annotation and high demand for computational resources still limit the application of e-skin. Optimizing deep learning algorithms, improving computational efficiency, and exploring hardware-algorithm co-designing will be the key to future development. This review aims to present the deep learning techniques applied in e-skin and provide inspiration for subsequent researchers. We first summarize the sources and characteristics of e-skin data and review the deep learning models applicable to e-skin data and their applications in data analysis. Additionally, we discuss the use of deep learning in e-skin, particularly in health monitoring and human-machine interactions, and we explore the current challenges and future development directions.

Wearable Biodevices Based on Two-Dimensional Materials: From Flexible Sensors to Smart Integrated Systems

The proliferation of wearable biodevices has boosted the development of soft, innovative, and multifunctional materials for human health monitoring. The integration of wearable sensors with intelligent systems is an overwhelming tendency, providing powerful tools for remote health monitoring and personal health management. Among many candidates, two-dimensional (2D) materials stand out due to several exotic mechanical, electrical, optical, and chemical properties that can be efficiently integrated into atomic-thin films. While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene, the rapid development of new 2D materials with exotic properties has opened up novel applications, particularly in smart interaction and integrated functionalities. This review aims to consolidate recent progress, highlight the unique advantages of 2D materials, and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices. We begin with an in-depth analysis of the advantages, sensing mechanisms, and potential applications of 2D materials in wearable biodevice fabrication. Following this, we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body. Special attention is given to showcasing the integration of multi-functionality in 2D smart devices, mainly including self-power supply, integrated diagnosis/treatment, and human-machine interaction. Finally, the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of 2D materials for advanced biodevices.

Flexible Passive Wireless Sensing Platform with Frequency Mapping and Multimodal Fusion

As one of the core parts of the Internet-of-things (IOTs), multimodal sensors have exhibited great advantages in fields such as human-machine interaction, electronic skin, and environmental monitoring. However, current multimodal sensors substantially introduce a bloated equipment architecture and a complicated decoupling mechanism. In this work we propose a multimodal fusion sensing platform based on a power-dependent piecewise linear decoupling mechanism, allowing four parameters to be perceived and decoded from the passive wireless single component, which greatly broadens the configurable freedom of a sensor in the IOT. A systematic model is employed to analyze the linear sensing properties and ensure the feasibility of the scheme. The excitation power dependence provides an efficient and quantitative linear decoupling strategy of unidentified combinations for multiple stimuli. As a validation for a wearable device such as electronic skin (e-skin), the functionalized sensing film polyaniline/graphene oxide (PANI/GO) is served to synchronously monitor humidity, temperature, ultraviolet, and proximity through the mapping in resonant frequency (fs). Compared with the output errors of ∼18.00%, ∼17.50%, ∼15.00%, and ∼20.00%, the maximum experimental errors of temperature, humidity, ultraviolet, and proximity are 5.70%, 4.00%, 5.00%, and 8.30% after decoupling, respectively. In general, the developed single-component multimodal fusion sensing platform offers a strategic advantage for a miniaturization, passive wireless, and inexpensive (less than $1) signal identification system with a facile circuit layout.

A Multimodal Perception-Enabled Flexible Memristor with Combined Sensing-Storage-Memory Functions for Enhanced Artificial Injury Recognition

With the continuous advancement of wearable technology and advanced medical monitoring, there is an increasing demand for electronic devices that can adapt to complex environments and have high perceptual sensitivity. Here, a novel artificial injury perception device based on an Ag/HfOx/ITO/PET flexible memristor is designed to address the limitations of current technologies in multimodal perception and environmental adaptability. The memristor exhibits excellent resistive switching (RS) performance and mechanical flexibility under different bending angles (BAs), temperatures, humid environment, and repetitive folding conditions. Further, the device demonstrates the multimodal perception and conversion capabilities toward voltage, mechanical, and thermal stimuli through current response tests under different conditions, enabling not only the simulation of artificial injury perception but also holds promise for monitoring and controlling the movement of robotic arms. Moreover, the logical operation capability of the memristor-based reconfigurable logic (MRL) gates is also demonstrated, proving the device has great potential applications with sensing, storage, and memory functions. Overall, this study not only provides a direction for the development of the next-generation flexible multimodal sensors, but also has significant implications for technological advancements in many fields such as robotic arms, electronic skin (e-skin), and medical monitoring.

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

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

Toward an AI Era: Advances in Electronic Skins

Electronic skins (e-skins) have seen intense research and rapid development in the past two decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors that detect physiological and environmental signals have been designed and integrated into functional systems. Recently, researchers have increasingly deployed machine learning and other artificial intelligence (AI) technologies to mimic the human neural system for the processing and analysis of sensory data collected by e-skins. Integrating AI has the potential to enable advanced applications in robotics, healthcare, and human-machine interfaces but also presents challenges such as data diversity and AI model robustness. In this review, we first summarize the functions and features of e-skins, followed by feature extraction of sensory data and different AI models. Next, we discuss the utilization of AI in the design of e-skin sensors and address the key topic of AI implementation in data processing and analysis of e-skins to accomplish a range of different tasks. Subsequently, we explore hardware-layer in-skin intelligence before concluding with an analysis of the challenges and opportunities in the various aspects of AI-enabled e-skins.

Hierarchical Bilayer Polyelectrolyte Ion Paper Conductor for Moisture-Induced Power Generation

Harvesting energy from air water (atmospheric moisture) promises a sustainable self-powered system without any restrictions from specific environmental requirements (e.g., solar cells, hydroelectric, or thermoelectric devices). However, the present moisture-induced power devices traditionally generate intermittent or bursts of energy, especially for much lower current outputs (generally keeping at nA or μA levels) from the ambient environment, typically suffering from inferior ionic conductivity and poor hierarchical structure design for manipulating sustained air water and ion-charge transport. Here, we demonstrate a universal strategy to design a high-performance bilayer polyelectrolyte ion paper conductor for generating continuous electric power from ambient humidity. The generator can produce a continuous voltage of up to 0.74 V and also an exceptional current of 5.63 mA across a single 1.0 mm-thick ion paper conductor. We discover that the sandwiched LiCl-nanocellulose-engineered paper promises an ion-transport junction between the negatively and positively charged bilayer polyelectrolytes for application in MEGs with both high voltage and high current outputs. Moreover, we demonstrated the universality of this bilayer sandwich nanocellulose-salt engineering strategy with other anions and cations, exhibiting similar power generation ability, indicating that it could be the next generation of sustainable MEGs with low cost, easier operation, and high performance.

Moisture-enabled self-charging and voltage stabilizing supercapacitor

Supercapacitor is highly demanded in emerging portable electronics, however, which faces frequent charging and inevitable rapid self-discharging of huge inconvenient. Here, we present a flexible moisture-powered supercapacitor (mp-SC) that capable of spontaneously moisture-enabled self-charging and persistently voltage stabilizing. Based on the synergy effect of moisture-induced ions diffusion of inner polyelectrolyte-based moist-electric generator and charges storage ability of inner graphene electrochemical capacitor, this mp-SC demonstrates the self-charged high areal capacitance of 138.3 mF cm-2 and ~96.6% voltage maintenance for 120 h. In addition, a large-scale flexible device of 72 mp-SC units connected in series achieves a self-charged 60 V voltage in air, efficiently powering various commercial electronics in practical applications. This work will provide insight into the design self-powered and ultra-long term stable supercapacitors and other energy storage devices.

Robustly and Intrinsically Stretchable Ionic Gel-Based Moisture-Enabled Power Generator with High Human Body Conformality

Direct harvesting of energy from moist air will be a promising route to supply electricity for booming wearable and distributed electronics, with the recent rapid development of the moisture-enabled electricity generator (MEG). However, the easy spatial distortion of rigid MEG materials under severe deformation extremely inconveniences the human body with intense physical activity, seriously hindering the desirable applications. Here, an intrinsically stretchable moisture-enabled electricity generator (s-MEG) is developed based on a well-fabricated stretchable functional ionic gel (SIG) with a flexible double-network structure and reversible cross-linking interactions, demonstrating stable electricity output performance even when stretched up to 150% strain and high human body conformality. This SIG exhibits ultrahigh tensile strain (∼600%), and a 1 cm × 1 cm SIG film-based s-MEG can generate a voltage of ∼0.4 V and a current of ∼5.7 μA when absorbing water from humidity air. Based on the strong adhesion and the excellent interface combination of SIG and rough fabric electrodes induced by the fabrication process, s-MEG is able to realize bending or twisting deformation and shows outstanding electricity output stability with ∼90% performance retention after 5000 cycles of bending tests. By connecting s-MEG units in series or parallel, an integrated device of "moisture-powered wristband" is developed to wear on the wrist of humans and drive a flexible sensor for tracking finger motions. Additionally, a comfortable "moisture-powered sheath" based on s-MEGs is created, which can be worn like clothing on human arms to generate energy while walking and flexing the elbow, which is promising in the field of wearable electronics.

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.

Biopolymers for Hygroscopic Material Development

The effective management of atmospheric water will create huge value for mankind. Diversified and sustainable biopolymers that are derived from organisms provide rich building blocks for various hygroscopic materials. Here, a comprehensive review of recent advances in developing biopolymers for hygroscopic materials is provided. It is begun with a brief introduction of species diversity and the processes of obtaining various biopolymer materials from organisms. The fabrication of hygroscopic materials is then illustrated, with a specific focus on the use of biopolymer-derived materials as substrates to produce composites and the use of biopolymers as building blocks to fabricate composite gels. Next, the representative applications of biopolymer-derived hygroscopic materials for dehumidification, atmospheric water harvesting, and power generation are systematically presented. An outlook on future challenges and key issues worthy of attention are finally provided.

Interfacial Assembly of 2D Graphene-Derived Ion Channels for Water-Based Green Energy Conversion

The utilization of sustained and green energy is believed to alleviate increasing menace of global environmental concerns and energy dilemma. Interfacial assembly of 2D graphene-derived ion channels (2D-GDICs) with tunable ion/fluid transport behavior enables efficient harvesting of renewable green energy from ubiquitous water, especially for osmotic energy harvesting. In this review, various interfacial assembly strategies for fabricating diverse 2D-GDICs are summarized and their ion transport properties are discussed. This review analyzes how particular structure and charge density/distribution of 2D-GDIC can be modulated to minimize internal resistance of ion/fluid transport and enhance energy conversion efficiency, and highlights stimuli-responsive functions and stability of 2D-GDIC and further examines the possibility of integrating 2D-GDIC with other energy conversion systems. Notably, the presented preparation and applications of 2D-GDIC also inspire and guide other 2D materials to fabricate sophisticated ion channels for targeted applications. Finally, potential challenges in this field is analyzed and a prospect to future developments toward high-performance or large-scale real-word applications is offered.

Moisture-Enabled Electricity from Hygroscopic Materials: A New Type of Clean Energy

Water utilization is accompanied with the development of human beings, whereas gaseous moisture is usually regarded as an underexploited resource. The advances of highly efficient hygroscopic materials endow atmospheric water harvesting as an intriguing solution to convert moisture into clean water. The discovery of hygroelectricity, which refers to the charge buildup at a material surface dependent on humidity, and the following moisture-enabled electric generation (MEG) realizes energy conversion and directly outputs electricity. Much progress has been made since then to optimize MEG performance, pushing forward the applications of MEG into a practical level. Herein, the evolvement and development of MEG are systematically summarized in a chronological order. The optimization strategies of MEG are discussed and comprehensively evaluated. Then, the latest applications of MEG are presented, including high-performance powering units and self-powered devices. In the end, a perspective on the future development of MEG is given for inspiring more researchers into this promising area.

An in-sensor humidity computing system for contactless human-computer interaction

Being capable of processing large amounts of redundant data and decreasing power consumption, in-sensor computing approaches play significant roles in neuromorphic computing and are attracting increasing interest in perceptual information processing. Herein, we proposed a high performance humidity-sensitive memristor based on a Ti/graphene oxide (GO)/HfOx/Pt structure and verified its potential for application in remote health management and contactless human-machine interfaces. Since GO possesses abundant hydrophilic groups (carbonyl, epoxide, and hydroxyl), the memristor shows a high humidity sensitivity, fast response, and wide response range. By utilizing the proton-modulated redox reaction, humidity exposure to the memristor induces a dynamic change in the switching between high and low resistance states, ensuring essential synaptic learning functions, such as paired-pulse facilitation, spike number-dependent plasticity, and spike amplitude-dependent plasticity. More importantly, based on the humidity-induced salient features originating from the abundant hydrophilic functional groups in GO, we have implemented a noncontact human-machine interface utilizing the respiratory mode in humans, demonstrating the potential of promoting health monitoring applications and effectively blocking virus transmission. In addition, the high recognition accuracy of contactless handwriting in a 5 × 5 array artificial neural network was successfully achieved, which is attributed to the excellent emulated synaptic behaviors. This study provides a feasible method to develop an excellent humidity-sensitive memristor for constructing efficient in-sensor computing for application in health management and contactless human-computer interaction.

Power Generation from the Interaction of a Carbon Foam and Water

Understanding the interaction mechanisms between the surface of carbon-based materials and water is of great significance for the development of water-based energy storage and energy conversion devices. Herein, a self-supporting electric generator is demonstrated based on water adsorption on the surface of the carbon foam (CF) that works with various water resources, including deionized (DI) water, tap water, wastewater, and seawater. It is revealed that the dissociation of oxygen-containing groups on the surface of CF after water molecule adsorption leads to a reduction of the surface potential of the CF. Through surface modulation techniques such as reduction and oxidation, a balance has been uncovered between the oxygen content and conductivity for the high-performance CFs. The generator can generate an open-circuit voltage of approximately 0.6 V in natural seawater with a power density of up to 0.77 mW g-1. A high voltage of more than 2 V can be achieved easily by assembling components connected in series to drive electronic devices, such as a light-emitting diode (LED). This work demonstrates a simple and low-cost method for electricity harvesting, offering an additional option for self-powered devices.

Phyto-inspired sustainable and high-performance fabric generators via moisture absorption-evaporation cycles

Collecting energy from the ubiquitous water cycle has emerged as a promising technology for power generation. Here, we have developed a sustainable moisture absorption-evaporation cycling fabric (Mac-fabric). On the basis of the cycling unidirectional moisture conduction in the fabric and charge separation induced by the negative charge channel, sustainable constant voltage power generation can be achieved. A single Mac-fabric can achieve a high power output of 0.144 W/m2 (5.76 × 102 W/m3) at 40% relative humidity (RH) and 20°C. By assembling 500 series and 300 parallel units of Mac-fabrics, a large-scale demo achieves 350 V of series voltage and 33.76 mA of parallel current at 25% RH and 20°C. Thousands of Mac-fabric units are sewn into a tent to directly power commercial electronic products such as mobile phones in outdoor environments. The lightweight (300 g/m2) and soft characteristics of the Mac-fabric make it ideal for large-area integration and energy collection in real circumstances.

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.

Multistage coupling water-enabled electric generator with customizable energy output

Constant water circulation between land, ocean and atmosphere contains great and sustainable energy, which has been successfully employed to generate electricity by the burgeoning water-enabled electric generator. However, water in various forms (e.g. liquid, moisture) is inevitably discharged after one-time use in current single-stage water-enabled electric generators, resulting in the huge waste of inherent energy within water circulation. Herein, a multistage coupling water-enabled electric generator is proposed, which utilizes the internal liquid flow and subsequently generated moisture to produce electricity synchronously, achieving a maximum output power density of ~92 mW m-2 (~11 W m-3). Furthermore, a distributary design for internal water in different forms enables the integration of water-flow-enabled and moisture-diffusion-enabled electricity generation layers into mc-WEG by a "flexible building blocks" strategy. Through a three-stage adjustment process encompassing size control, space optimization, and large-scale integration, the multistage coupling water-enabled electric generator realizes the customized electricity output for diverse electronics. Twenty-two units connected in series can deliver ~10 V and ~280 μA, which can directly lighten a table lamp for 30 min without aforehand capacitor charging. In addition, multistage coupling water-enabled electric generators exhibit excellent flexibility and environmental adaptability, providing a way for the development of water-enabled electric generators.

Flexible Optoelectronic Multimodal Proximity/Pressure/Temperature Sensors with Low Signal Interference

Multimodal tactile sensors are a crucial part of intelligent human-machine interaction and collaboration. Simultaneous detection of proximity, pressure, and temperature on a single sensor can greatly promote the safety, interactivity, and compactness of interaction systems. However, severe signal interference and complex decoupling algorithms hinder the actual applications. Here, this work reports a flexible optoelectronic multimodal sensor capable of detecting and decoupling proximity/pressure/temperature by integrating a light waveguide and an interdigital electrode (IDE) into a compact fibrous sensor. Negligible signal interference is realized by combining heterogeneous sensing mechanisms of optics and electronics, which encodes proximity into capacitance, pressure into light intensity and temperature into resistance. The sensor exhibits a large sensing distance of 225 mm with fast responses for proximity detection, a pressure sensitivity of 0.42 N-1 , and a temperature sensitivity of 7% °C-1 . As a proof of concept, a doll equipped with the sensor can accurately discriminate and detect various stimuli, thus achieving safe and immersive interactions with the user. This work opens up promising paths for self-decoupled multimodal sensors and related human/machine/environment interaction applications.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.