Ultrasensitive Strain Sensor Utilizing a AgF-AgNW Hybrid Nanocomposite for Breath Monitoring and Pulmonary Function Analysis

Erasable and Multifunctional On-Skin Bioelectronics Prepared by Direct Writing

The field of bioelectronics has witnessed significant advancements, offering practical solutions for personalized healthcare through the acquisition and analysis of skin-based physical, chemical, and electrophysiological signals. Despite these advancements, current bioelectronics face several challenges, including complex preparation procedures, poor skin adherence, susceptibility to motion artifacts, and limited personalization and reconfigurability capabilities. In this study, we introduce an innovative method for fabricating erasable bioelectronics on a flexible substrate coating adhered to the skin using a ballpoint pen without any postprocessing. Our approach yields devices that are thin, erasable, reconfigurable, dry-friction resistant, self-healing, and highly customizable. We demonstrate the multifunctionality of these on-skin bioelectronics through their application as strain sensors for motion monitoring, temperature and humidity sensors for breath monitoring, and heating elements for target point hyperthermia. The potential of our bioelectronics in personalized medicine is substantial, particularly in health monitoring. We provide a novel solution for achieving efficient and convenient personalized medical services, addressing the limitations of existing technologies and paving the way for next-generation wearable health devices.

Piezoelectric nanogenerators from sustainable biowaste source: Power harvesting and respiratory monitoring with electrospun crab shell powder-poly(vinylidene fluoride) composite nanofibers

Wearable piezoelectric nanogenerators (PENGs) are increasingly significant in healthcare and energy harvesting applications due to their ability to convert mechanical energy into electrical signals. In this study, we developed PENGs by incorporating crab shell powder (CS-NFs) into electrospun polyvinylidene fluoride (PVDF) nanofibers to enhance their piezoelectric properties. The PVDF-CS-NFs (PC-NFs) composites were evaluated for structural, thermal, and piezoelectric performance. The 1.5 wt% CS-NFs composite exhibited a notable improvement, with a maximum output voltage of 19 V under mechanical deformation, significantly higher than pristine PVDF NFs. Furthermore, the device demonstrated excellent sensitivity in real-time respiratory monitoring when applied to various body locations, including the chest, throat, and mask. Additionally, the PC-NFs-based PENGs were capable of charging a 2.2 µF capacitor to 2 V within 180 s and powering 56 LEDs. These results underscore the potential of using sustainable crab shell waste in biocompatible, eco-friendly piezoelectric devices for wearable sensors and energy harvesting applications.

An intelligent humidity sensing system for human behavior recognition

An intelligent humidity sensing system has been developed for real-time monitoring of human behaviors through respiration detection. The key component of this system is a humidity sensor that integrates a thermistor and a micro-heater. This sensor employs porous nanoforests as its sensing material, achieving a sensitivity of 0.56 pF/%RH within a range of 60-90% RH, along with excellent long-term stability and superior gas selectivity. The micro-heater in the device provides a high operating temperature, enhancing sensitivity by 5.8 times. This significant improvement enables the capture of weak humidity variations in exhaled gases, while the thermistor continuously monitors the sensor's temperature during use and provides crucial temperature information related to respiration. With the assistance of a machine learning algorithm, a behavior recognition system based on the humidity sensor has been constructed, enabling behavior states to be classified and identified with an accuracy of up to 96.2%. This simple yet intelligent method holds great potential for widespread applications in medical assistance analysis and daily health monitoring.

Recent Advances of Stretchable Nanomaterial-Based Hydrogels for Wearable Sensors and Electrophysiological Signals Monitoring

Electrophysiological monitoring is a commonly used medical procedure designed to capture the electrical signals generated by the body and promptly identify any abnormal health conditions. Wearable sensors are of great significance in signal acquisition for electrophysiological monitoring. Traditional electrophysiological monitoring devices are often bulky and have many complex accessories and thus, are only suitable for limited application scenarios. Hydrogels optimized based on nanomaterials are lightweight with excellent stretchable and electrical properties, solving the problem of high-quality signal acquisition for wearable sensors. Therefore, the development of hydrogels based on nanomaterials brings tremendous potential for wearable physiological signal monitoring sensors. This review first introduces the latest advancement of hydrogels made from different nanomaterials, such as nanocarbon materials, nanometal materials, and two-dimensional transition metal compounds, in physiological signal monitoring sensors. Second, the versatile properties of these stretchable composite hydrogel sensors are reviewed. Then, their applications in various electrophysiological signal monitoring, such as electrocardiogram monitoring, electromyographic signal analysis, and electroencephalogram monitoring, are discussed. Finally, the current application status and future development prospects of nanomaterial-optimized hydrogels in wearable physiological signal monitoring sensors are summarized. We hope this review will inspire future development of wearable electrophysiological signal monitoring sensors using nanomaterial-based hydrogels.

Materials, Structure, and Interface of Stretchable Interconnects for Wearable Bioelectronics

Since wearable technologies for telemedicine have emerged to tackle global health concerns, the demand for well-attested wearable healthcare devices with high user comfort also arises. Skin-wearables for health monitoring require mechanical flexibility and stretchability for not only high compatibility with the skin's dynamic nature but also a robust collection of fine health signals from within. Stretchable electrical interconnects, which determine the device's overall integrity, are one of the fundamental units being understated in wearable bioelectronics. In this review, a broad class of materials and engineering methodologies recently researched and developed are presented, and their respective attributes, limitations, and opportunities in designing stretchable interconnects for wearable bioelectronics are offered. Specifically, the electrical and mechanical characteristics of various materials (metals, polymers, carbons, and their composites) are highlighted, along with their compatibility with diverse geometric configurations. Detailed insights into fabrication techniques that are compatible with soft substrates are also provided. Importantly, successful examples of establishing reliable interfacial connections between soft and rigid elements using novel interconnects are reviewed. Lastly, some perspectives and prospects of remaining research challenges and potential pathways for practical utilization of interconnects in wearables are laid out.

Review of Recent Progress on Silicone Rubber Composites for Multifunctional Sensor Systems

The latest progress (the year 2021-2024) on multifunctional sensors based on silicone rubber is reported. These multifunctional sensors are useful for real-time monitoring through relative resistance, relative current change, and relative capacitance types. The present review contains a brief overview and literature survey on the sensors and their multifunctionalities. This contains an introduction to the different functionalities of these sensors. Following the introduction, the survey on the types of filler or rubber and their fabrication are briefly described. The coming section deals with the fabrication methodology of these composites where the sensors are integrated. The special focus on mechanical and electro-mechanical properties is discussed. Electro-mechanical properties with a special focus on response time, linearity, and gauge factor are reported. The next section of this review reports the filler dispersion and its role in influencing the properties and applications of these sensors. Finally, various types of sensors are briefly reported. These sensors are useful for monitoring human body motions, breathing activity, environment or breathing humidity, organic gas sensing, and, finally, smart textiles. Ultimately, the study summarizes the key takeaway from this review article. These conclusions are focused on the merits and demerits of the sensors and are followed by their future prospects.

Biocompatible, Antibacterial, and Stable Deep Eutectic Solvent-Based Ionic Gel Multimodal Sensors for Healthcare Applications

In this study, we investigated a novel approach to fabricate multifunctional ionic gel sensors by using deep eutectic solvents (DESs) as replacements for water. When two distinct DESs were combined, customizable mechanical and conductive properties were created, resulting in improved performance compared with traditional hydrogel-based strain sensors. DES ionic gels possess superior mechanical properties, transparency, biocompatibility, and antimicrobial properties, making them suitable for a wide range of applications such as flexible electronics, soft robotics, and healthcare. We conducted a comprehensive evaluation of the DES ionic gels, evaluating their performance under extreme temperature conditions (-70 to 80 °C), impressive optical transparency (94%), and biocompatibility. Furthermore, a series of tests were conducted to evaluate the antibacterial performance (Escherichia coli) of the DES ionic gels. Their wide strain (1-400%) and temperature (15-50 °C)-sensing ranges demonstrate the versatility and adaptability of DES ionic gels for diverse sensing requirements. The resulting DES ionic gels were successfully applied in human activity and vital sign monitoring, demonstrating their potential for biointegrated sensing devices and healthcare applications. This study offers valuable insights into the development and optimization of hydrogel sensors, particularly for applications that require environmental stability, biocompatibility, and antibacterial performance, thereby paving the way for future advancements in this field.

Convolution Neural Networks for Motion Detection with Electrospun Reversibly-Cross-linkable Polymers and Encapsulated Ag Nanowires

This paper presents the design, fabrication, and implementation of a novel composite film, a polybutadiene-based urethane (PBU)/AgNW/PBU sensor (PAPS), demonstrating remarkable mechanical stability and precision in motion detection. The sensor capitalizes on the integration of Ag nanowire (AgNW) electrodes into a neutral plane, embedded within a reversibly cross-linkable PBU polymer. The meticulous arrangement confers pore-free and interfaceless sensor formation, resulting in an enhanced mechanical robustness, reproducibility, and long-term reliability. The PBU polymer is subjected to an electrospinning process, followed by sequential Diels-Alder (DA) and retro-DA reactions to produce a planarized encapsulation layer. This pioneering technology, based on electrospinning, allows for more flawless engineering of the neutral plane as compared to conventional film lamination or layer-by-layer spin-coating processes. This encapsulation, matching the thickness of the preformed PBU film, effectively houses the AgNW electrodes. The PAPS outperforms conventional AgNW/PBU sensors (APS) in terms of mechanical stability and bending insensitivity. When affixed to various body parts, the PAPS generates distinctive signal curves, reflecting the specific body part and degree of motion involved. The PAPS sensor's utility is further magnified by the application of machine learning and deep learning algorithms for signal interpretation. K-means clustering algorithm authenticated the superior reproducibility and consistency of the signals derived from the PAPS over the APS. Deep learning algorithms, including a singular 1D convolutional neural network (1D CNN), long short-term memory (LSTM) network, and dual-layered combinations of 1D CNN + LSTM and LSTM + 1D CNN, were deployed for signal classification. The singular 1D CNN model displayed a classification accuracy exceeding 98%. The PAPS sensor signifies a pivotal development in the field of intelligent motion sensors.