Printed, Wireless, Soft Bioelectronics and Deep Learning Algorithm for Smart Human-Machine Interfaces

MOF-Based Bioelectronic Supercapacitors

Metal-organic frameworks (MOFs) represent a highly promising material class for bioelectronic supercapacitors, characterized by their adjustable structures, extensive surface areas, and superior electrochemical properties. This research explores the synthesis and incorporation of MOF-based materials into bioelectronic devices aimed at energy storage and biosensing applications. The focus is on improving the electrochemical performance of MOFs while preserving their structural integrity through functionalization with biocompatible polymers and conductive materials. The resulting MOF-based bioelectronic supercapacitors exhibit significant improvements in specific capacitance, energy density, and cycling stability. Additionally, the inclusion of bioreceptors allows for the simultaneous detection of biochemical signals alongside energy storage, thus enabling innovative applications in wearable electronics and health monitoring systems. These results suggest that MOF-based supercapacitors have the capacity to fulfill energy storage needs while also advancing bioelectronics by merging energy and sensing capabilities.

Beyond Flexible: Unveiling the Next Era of Flexible Electronic Systems

Flexible electronics are integral in numerous domains such as wearables, healthcare, physiological monitoring, human-machine interface, and environmental sensing, owing to their inherent flexibility, stretchability, lightweight construction, and low profile. These systems seamlessly conform to curvilinear surfaces, including skin, organs, plants, robots, and marine species, facilitating optimal contact. This capability enables flexible electronic systems to enhance or even supplant the utilization of cumbersome instrumentation across a broad range of monitoring and actuation tasks. Consequently, significant progress has been realized in the development of flexible electronic systems. This study begins by examining the key components of standalone flexible electronic systems-sensors, front-end circuitry, data management, power management and actuators. The next section explores different integration strategies for flexible electronic systems as well as their recent advancements. Flexible hybrid electronics, which is currently the most widely used strategy, is first reviewed to assess their characteristics and applications. Subsequently, transformational electronics, which achieves compact and high-density system integration by leveraging heterogeneous integration of bare-die components, is highlighted as the next era of flexible electronic systems. Finally, the study concludes by suggesting future research directions and outlining critical considerations and challenges for developing and miniaturizing fully integrated standalone flexible electronic systems.

Wireless breathable face mask sensor for spatiotemporal 2D respiration profiling and respiratory diagnosis

Owing to air pollution and the pandemic outbreak, the need for quantitative pulmonary monitoring has greatly increased. The COVID-19 outbreak has aroused attention for comfortable wireless monitoring of respiratory profiles and more real-time diagnosis of respiratory diseases. Although respiration sensors have been investigated extensively with single-pixel sensors, 2D respiration profiling with a pixelated array sensor has not been demonstrated for both exhaling and inhaling. Since the pixelated array sensor allowed for simultaneous profiling of the nasal breathing and oral breathing, it provides essential respiratory information such as breathing patterns, respiration habit, breathing disorders. In this study, we introduced an air-permeable, stretchable, and a pixelated pressure sensor that can be integrated into a commercial face mask. The mask sensor showed a strain-independent pressure-sensing performance, providing 2D pressure profiles for exhalation and inhalation. Real-time 2D respiration profiles could monitor various respiratory behaviors, such as oral/nasal breathing, clogged nose, out-of-breath, and coughing. Furthermore, they could detect respiratory diseases, such as rhinitis, sleep apnea, and pneumonia. The 2D respiratory profiling mask sensor is expected to be employed for remote respiration monitoring and timely patient treatment.

Soft bioelectronics for diagnostic and therapeutic applications in neurological diseases

Physical and chemical signals in the central nervous system yield crucial information that is clinically relevant under both physiological and pathological conditions. The emerging field of bioelectronics focuses on the monitoring and manipulation of neurophysiological signals with high spatiotemporal resolution and minimal invasiveness. Significant advances have been realized through innovations in materials and structural design, which have markedly enhanced mechanical and electrical properties, biocompatibility, and overall device performance. The diagnostic and therapeutic potential of soft bioelectronics has been corroborated across a diverse array of pre-clinical settings. This review summarizes recent studies that underscore the developments and applications of soft bioelectronics in neurological disorders, including neuromonitoring, neuromodulation, tumor treatment, and biosensing. Limitations and outlooks of soft devices are also discussed in terms of power supply, wireless control, biocompatibility, and the integration of artificial intelligence. This review highlights the potential of soft bioelectronics as a future platform to promote deciphering brain functions and clinical outcomes of neurological diseases.

Soft Bioelectronics for Neuroengineering: New Horizons in the Treatment of Brain Tumor and Epilepsy

Soft bioelectronic technologies for neuroengineering have shown remarkable progress, which include novel soft material technologies and device design strategies. Such technological advances that are initiated from fundamental brain science are applied to clinical neuroscience and provided meaningful promises for significant improvement in the diagnosis efficiency and therapeutic efficacy of various brain diseases recently. System-level integration strategies in consideration of specific disease circumstances can enhance treatment effects further. Here, recent advances in soft implantable bioelectronics for neuroengineering, focusing on materials and device designs optimized for the treatment of intracranial disease environments, are reviewed. Various types of soft bioelectronics for neuroengineering are categorized and exemplified first, and then details for the sensing and stimulating device components are explained. Next, application examples of soft implantable bioelectronics to clinical neuroscience, particularly focusing on the treatment of brain tumor and epilepsy are reviewed. Finally, an ideal system of soft intracranial bioelectronics such as closed-loop-type fully-integrated systems is presented, and the remaining challenges for their clinical translation are discussed.

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing

In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets, thereby unveiling latent knowledge crucial for informed decision-making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM-printed parts. This review delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML-related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation), and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM-related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM-related applications. It is envisioned that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM-related research areas.

Electronic Skin for Health Monitoring Systems: Properties, Functions, and Applications

Electronic skin (e-skin), a skin-like wearable electronic device, holds great promise in the fields of telemedicine and personalized healthcare because of its good flexibility, biocompatibility, skin conformability, and sensing performance. E-skin can monitor various health indicators of the human body in real time and over the long term, including physical indicators (exercise, respiration, blood pressure, etc.) and chemical indicators (saliva, sweat, urine, etc.). In recent years, the development of various materials, analysis, and manufacturing technologies has promoted significant development of e-skin, laying the foundation for the application of next-generation wearable medical technologies and devices. Herein, the properties required for e-skin health monitoring devices to achieve long-term and precise monitoring and summarize several detectable indicators in the health monitoring field are discussed. Subsequently, the applications of integrated e-skin health monitoring systems are reviewed. Finally, current challenges and future development directions in this field are discussed. This review is expected to generate great interest and inspiration for the development and improvement of e-skin and health monitoring systems.

Advances in Energy Harvesting Technologies for Wearable Devices

The development of wearable electronics is revolutionizing human health monitoring, intelligent robotics, and informatics. Yet the reliance on traditional batteries limits their wearability, user comfort, and continuous use. Energy harvesting technologies offer a promising power solution by converting ambient energy from the human body or surrounding environment into electrical power. Despite their potential, current studies often focus on individual modules under specific conditions, which limits practical applicability in diverse real-world environments. Here, this review highlights the recent progress, potential, and technological challenges in energy harvesting technology and accompanying technologies to construct a practical powering module, including power management and energy storage devices for wearable device developments. Also, this paper offers perspectives on designing next-generation wearable soft electronics that enhance quality of life and foster broader adoption in various aspects of daily life.

Wireless Battery-free and Fully Implantable Organ Interfaces

Advances in soft materials, miniaturized electronics, sensors, stimulators, radios, and battery-free power supplies are resulting in a new generation of fully implantable organ interfaces that leverage volumetric reduction and soft mechanics by eliminating electrochemical power storage. This device class offers the ability to provide high-fidelity readouts of physiological processes, enables stimulation, and allows control over organs to realize new therapeutic and diagnostic paradigms. Driven by seamless integration with connected infrastructure, these devices enable personalized digital medicine. Key to advances are carefully designed material, electrophysical, electrochemical, and electromagnetic systems that form implantables with mechanical properties closely matched to the target organ to deliver functionality that supports high-fidelity sensors and stimulators. The elimination of electrochemical power supplies enables control over device operation, anywhere from acute, to lifetimes matching the target subject with physical dimensions that supports imperceptible operation. This review provides a comprehensive overview of the basic building blocks of battery-free organ interfaces and related topics such as implantation, delivery, sterilization, and user acceptance. State of the art examples categorized by organ system and an outlook of interconnection and advanced strategies for computation leveraging the consistent power influx to elevate functionality of this device class over current battery-powered strategies is highlighted.

Biosignal-integrated robotic systems with emerging trends in visual interfaces: A systematic review

Human-machine interfaces (HMI) are currently a trendy and rapidly expanding area of research. Interestingly, the human user does not readily observe the interface between humans and machines. Instead, interactions between the machine and electrical signals from the user's body are obscured by complex control algorithms. The result is effectively a one-way street, wherein data is only transmitted from human to machine. Thus, a gap remains in the literature: how can information be effectively conveyed to the user to enable mutual understanding between humans and machines? Here, this paper reviews recent advancements in biosignal-integrated wearable robotics, with a particular emphasis on "visualization"-the presentation of relevant data, statistics, and visual feedback to the user. This review article covers various signals of interest, such as electroencephalograms and electromyograms, and explores novel sensor architectures and key materials. Recent developments in wearable robotics are examined from control and mechanical design perspectives. Additionally, we discuss current visualization methods and outline the field's future direction. While much of the HMI field focuses on biomedical and healthcare applications, such as rehabilitation of spinal cord injury and stroke patients, this paper also covers less common applications in manufacturing, defense, and other domains.

Large-scale smart bioreactor with fully integrated wireless multivariate sensors and electronics for long-term in situ monitoring of stem cell culture

Achieving large-scale, cost-effective, and reproducible manufacturing of stem cells with the existing devices is challenging. Traditional single-use cell-bag bioreactors, limited by their rigid and single-point sensors, struggle with accuracy and scalability for high-quality cell manufacturing. Here, we introduce a smart bioreactor system that enables multi-spatial sensing for real-time, wireless culture monitoring. This scalable system includes a low-profile, label-free thin-film sensor array and electronics integrated with a flexible cell bag, allowing for simultaneous assessment of culture properties such as pH, dissolved oxygen, glucose, and temperature, to receive real-time feedback for up to 30 days. The experimental results show the accurate monitoring of time-dynamic and spatial variations of stem cells and myoblast cells with adjustable carriers from a plastic dish to a 2-liter cell bag. These advances open up the broad applicability of the smart sensing system for large-scale, lower-cost, reproducible, and high-quality engineered cell manufacturing for broad clinical use.

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

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.

Soft Bioelectronics for Therapeutics

Soft bioelectronics play an increasingly crucial role in high-precision therapeutics due to their softness, biocompatibility, clinical accuracy, long-term stability, and patient-friendliness. In this review, we provide a comprehensive overview of the latest representative therapeutic applications of advanced soft bioelectronics, ranging from wearable therapeutics for skin wounds, diabetes, ophthalmic diseases, muscle disorders, and other diseases to implantable therapeutics against complex diseases, such as cardiac arrhythmias, cancer, neurological diseases, and others. We also highlight key challenges and opportunities for future clinical translation and commercialization of soft therapeutic bioelectronics toward personalized medicine.

Soft Electronics for Health Monitoring Assisted by Machine Learning

Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.

Machine Learning for Bioelectronics on Wearable and Implantable Devices: Challenges and Potential

Bioelectronics presents a promising future in the field of embedded and implantable electronics, providing a range of functional applications, from personal health monitoring to bioactuators. However, due to the intrinsic difficulties present in producing and optimizing bioelectronics, recent research has focused on utilizing machine learning (ML) to reliably mitigate such issues and aid in process development. This review focuses on the recent developments of integrating ML into bioelectronics, aiding in a multitude of areas, such as material development, fabrication process optimization, and system integration. First, discussing how ML has aided in the material development by identifying complex relationships between process input parameters and desired outputs, such as product design. Second, examine the advancements in ML to accurately optimize fabrication precision and stability for various 3D printing technologies. Third, provide an overview of how ML can greatly assist in the analysis of complex, nonlinear relationships in data obtained from bioelectronics. Lastly, a summary of the challenges present with utilizing ML with bioelectronics and any other developments in this field. Such advancements in the field of bioelectronics and ML could hopefully build a strong foundation for this research field, promoting smart optimization together with effective use of ML to further enhance the effectiveness of such applications. Impact statement The article serves to give insight about the use of the machine learning (ML) techniques in the field of bioelectronics, since bioelectronics and ML are two distinct fields. This article allows bioelectronics researcher to get to know the latest advancement in the ML field. On the other hand, the article provides an insight to the ML researchers about how ML techniques can be useful in bioelectronics applications.

Stretchable and Self-Healable Graphene–Polymer Conductive Composite for Wearable EMG Sensor

In bioelectronics, stretchable and self-healable electrodes can reliably measure electrophysiological signals from the human body because they have good modulus matching with tissue and high durability. In particular, the polymer–graphene composite has advantages when it is used as an electrode for bioelectronic sensor devices. However, it has previously been reported that external stimuli such as heat or light are required for the self-healing process of polymer/graphene composites. In this study, we optimized a conducting composite by mixing a self-healing polymer (SHP) and graphene. The composite materials can not only self-heal without external stimulation but also have rapid electrical recovery from repeated mechanical damage such as scratches. In addition, they had stable electrical endurance even when the cyclic test was performed over 200 cycles at 50% strain, so they can be useful for a bioelectronic sensor device with high durability. Finally, we measured the electromyogram signals caused by the movement of arm muscles using our composite, and the measured data were transmitted to a microcontroller to successfully control the movement of the robot’s hand.

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.

Sputtered Electrolyte-Gated Transistor with Temperature-Modulated Synaptic Plasticity Behaviors

Temperature has always been considered as an essential factor for almost all kinds of semiconductor-based electronic components. In this work, temperature-dependent synaptic plasticity behaviors, which are mimicked by the indium-gallium-zinc oxide thin-film transistors gated with sputtered SiO2 electrolytes, have been studied. With the temperature increasing from 303 to 323 K, the electrolyte capacitance decreases from 0.42 to 0.11 μF cm-2. The mobility increases from 1.4 to 3.7 cm2 V-1 s-1, and the threshold voltage negatively shifts from -0.23 to -0.51 V. Synaptic behaviors under both a single pulse and multiple pulses are employed to study the temperature dependence. With the temperature increasing from 303 to 323 K, the post-synaptic current (PSC) at the resting state increases from 1.8 to 7.3 μA. Under a single gate pulse of 1 V and 1 s, the PSC signal altitude and the PSC retention time decrease from 2.0 to 0.7 μA and 5.1 × 102 to 2.5 ms, respectively. A physical model based on the electric field-induced ion drifting, ionic-electronic coupling, and gradient-coordinated ion diffusion is proposed to understand these temperature-dependent synaptic behaviors. Based on the experimental data on individual transistors, temperature-modulated pattern learning and memorizing behaviors are conceptually demonstrated. The in-depth investigation of the temperature dependence helps pave the way for further electrolyte-gated transistor-based neuromorphic applications.

Skin bioelectronics towards long-term, continuous health monitoring

Skin bioelectronics are considered as an ideal platform for personalised healthcare because of their unique characteristics, such as thinness, light weight, good biocompatibility, excellent mechanical robustness, and great skin conformability. Recent advances in skin-interfaced bioelectronics have promoted various applications in healthcare and precision medicine. Particularly, skin bioelectronics for long-term, continuous health monitoring offer powerful analysis of a broad spectrum of health statuses, providing a route to early disease diagnosis and treatment. In this review, we discuss (1) representative healthcare sensing devices, (2) material and structure selection, device properties, and wireless technologies of skin bioelectronics towards long-term, continuous health monitoring, (3) healthcare applications: acquisition and analysis of electrophysiological, biophysical, and biochemical signals, and comprehensive monitoring, and (4) rational guidelines for the design of future skin bioelectronics for long-term, continuous health monitoring. Long-term, continuous health monitoring of advanced skin bioelectronics will open unprecedented opportunities for timely disease prevention, screening, diagnosis, and treatment, demonstrating great promise to revolutionise traditional medical practices.

Graphene-enabled wearable sensors for healthcare monitoring

Wearable sensors in healthcare monitoring have recently found widespread applications in biomedical fields for their non- or minimal-invasive, user-friendly and easy-accessible features. Sensing materials is one of the major challenges to achieve these superiorities of wearable sensors for healthcare monitoring, while graphene-based materials with many favorable properties have shown great efficiency in sensing various biochemical and biophysical signals. In this paper, we review state-of-the-art advances in the development and modification of graphene-based materials (i.e., graphene, graphene oxide and reduced graphene oxide) for fabricating advanced wearable sensors with 1D (fibers), 2D (films) and 3D (foams/aerogels/hydrogels) macroscopic structures. We summarize the structural design guidelines, sensing mechanisms, applications and evolution of the graphene-based materials as wearable sensors for healthcare monitoring of biophysical signals (e.g., mechanical, thermal and electrophysiological signals) and biochemical signals from various body fluids and exhaled gases. Finally, existing challenges and future prospects are presented in this area.

High-Adhesive Flexible Electrodes and Their Manufacture: A Review

All human activity is associated with the generation of electrical signals. These signals are collectively referred to as electrical physiology (EP) signals (e.g., electrocardiogram, electroencephalogram, electromyography, electrooculography, etc.), which can be recorded by electrodes. EP electrodes are not only widely used in the study of primary diseases and clinical practice, but also have potential applications in wearable electronics, human-computer interface, and intelligent robots. Various technologies are required to achieve such goals. Among these technologies, adhesion and stretchable electrode technology is a key component for rapid development of high-performance sensors. In last decade, remarkable efforts have been made in the development of flexible and high-adhesive EP recording systems and preparation technologies. Regarding these advancements, this review outlines the design strategies and related materials for flexible and adhesive EP electrodes, and briefly summarizes their related manufacturing techniques.

Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery-Based Brain-Machine Interfaces

Motor imagery offers an excellent opportunity as a stimulus-free paradigm for brain-machine interfaces. Conventional electroencephalography (EEG) for motor imagery requires a hair cap with multiple wired electrodes and messy gels, causing motion artifacts. Here, a wireless scalp electronic system with virtual reality for real-time, continuous classification of motor imagery brain signals is introduced. This low-profile, portable system integrates imperceptible microneedle electrodes and soft wireless circuits. Virtual reality addresses subject variance in detectable EEG response to motor imagery by providing clear, consistent visuals and instant biofeedback. The wearable soft system offers advantageous contact surface area and reduced electrode impedance density, resulting in significantly enhanced EEG signals and classification accuracy. The combination with convolutional neural network-machine learning provides a real-time, continuous motor imagery-based brain-machine interface. With four human subjects, the scalp electronic system offers a high classification accuracy (93.22 ± 1.33% for four classes), allowing wireless, real-time control of a virtual reality game.

Real-Time Functional Assay of Volumetric Muscle Loss Injured Mouse Masseter Muscles via Nanomembrane Electronics

Skeletal muscle has a remarkable regeneration capacity to recover its structure and function after injury, except for the traumatic loss of critical muscle volume, called volumetric muscle loss (VML). Although many extremity VML models have been conducted, craniofacial VML has not been well-studied due to unavailable in vivo assay tools. Here, this paper reports a wireless, noninvasive nanomembrane system that integrates skin-wearable printed sensors and electronics for real-time, continuous monitoring of VML on craniofacial muscles. The craniofacial VML model, using biopsy punch-induced masseter muscle injury, shows impaired muscle regeneration. To measure the electrophysiology of small and round masseter muscles of active mice during mastication, a wearable nanomembrane system with stretchable graphene sensors that can be laminated to the skin over target muscles is utilized. The noninvasive system provides highly sensitive electromyogram detection on masseter muscles with or without VML injury. Furthermore, it is demonstrated that the wireless sensor can monitor the recovery after transplantation surgery for craniofacial VML. Overall, the presented study shows the enormous potential of the masseter muscle VML injury model and wearable assay tool for the mechanism study and the therapeutic development of craniofacial VML.

Printable Smart Materials and Devices: Strategies and Applications

Smart materials are a kind of functional materials which can sense and response to environmental conditions or stimuli from optical, electrical, magnetic mechanical, thermal, and chemical signals, etc. Patterning of smart materials is the key to achieving large-scale arrays of functional devices. Over the last decades, printing methods including inkjet printing, template-assisted printing, and 3D printing are extensively investigated and utilized in fabricating intelligent micro/nano devices, as printing strategies allow for constructing multidimensional and multimaterial architectures. Great strides in printable smart materials are opening new possibilities for functional devices to better serve human beings, such as wearable sensors, integrated optoelectronics, artificial neurons, and so on. However, there are still many challenges and drawbacks that need to be overcome in order to achieve the controllable modulation between smart materials and device performance. In this review, we give an overview on printable smart materials, printing strategies, and applications of printed functional devices. In addition, the advantages in actual practices of printing smart materials-based devices are discussed, and the current limitations and future opportunities are proposed. This review aims to summarize the recent progress and provide reference for novel smart materials and printing strategies as well as applications of intelligent devices.

Recent advances in nanogenerators-based flexible electronics for electromechanical biomonitoring

Electromechanical biomonitoring is essential in human health evaluation, diseases prevention and life quality improvement. Nanogenerators (NGs) have demonstrated exceptional performances and versatility in self-powered flexible electronics including piezoelectric and electrostatic sensors. Combined with artificial intelligent (AI), five generation (5G) and internet-of-thing (IoT) technologies, the NGs-based flexible electronics are paving a new way for creating intelligent electromechanical biomonitoring systems which are also capable of analyzing, transmitting, and deciding. In this review, we cover the recent remarkable developments in monitoring electromechanical physiological signals using NGs-based flexible electronics. We begin by covering the fundamentals of NGs from the perspective of mechanisms, materials, device structures, and manufacturing methods. We then give an overview of NGs-based flexible electronics in various wearable and implantable sensing applications. Finally, the present limitations and future developing trends of this field are discussed and prospected.

Layer-Scale and Chip-Scale Transfer Techniques for Functional Devices and Systems: A Review

Hetero-integration of functional semiconductor layers and devices has received strong research interest from both academia and industry. While conventional techniques such as pick-and-place and wafer bonding can partially address this challenge, a variety of new layer transfer and chip-scale transfer technologies have been developed. In this review, we summarize such transfer techniques for heterogeneous integration of ultrathin semiconductor layers or chips to a receiving substrate for many applications, such as microdisplays and flexible electronics. We showed that a wide range of materials, devices, and systems with expanded functionalities and improved performance can be demonstrated by using these technologies. Finally, we give a detailed analysis of the advantages and disadvantages of these techniques, and discuss the future research directions of layer transfer and chip transfer techniques.

Development of Flexible Ion-Selective Electrodes for Saliva Sodium Detection

Saliva can be used for health monitoring with non-invasive wearable systems. Such devices, including electrochemical sensors, may provide a safe, fast, and cost-efficient way of detecting target ions. Although salivary ions are known to reflect those in blood, no available clinical device can detect essential ions directly from saliva. Here, we introduce an all-solid-state, flexible film sensor that allows highly accurate detection of sodium levels in saliva, comparable to those in blood. The wireless film sensor system can successfully measure sodium ions from a small volume of infants' saliva (<400 µL), demonstrating its potential as a continuous health monitor. This study includes the structural characterization and error analysis of a carbon/elastomer-based ion-selective electrode and a reference electrode to confirm the signal reliability. The sensor, composed of a pair of the electrodes, shows good sensitivity (58.9 mV/decade) and selectivity (log K = -2.68 for potassium), along with a broad detection range of 5 × 10^-5 ≈ 1 M with a low detection limit of 4.27 × 10^-5 M. The simultaneous comparison between the film sensor and a commercial electrochemical sensor demonstrates the accuracy of the flexible sensor and a positive correlation in saliva-to-blood sodium levels. Collectively, the presented study shows the potential of the wireless ion-selective sensor system for a non-invasive, early disease diagnosis with saliva.