Recent progress in textile-based triboelectric force sensors for wearable electronics

ABSTRACT Coastlines are important basic geographic elements and mapping their spatial and attribute changes can help monitor, model and manage coastal zones. Traditional studies focused on the accuracy of extraction methods and the evolution characteristics of coastlines. Thanks to the advances in remote sensing for earth observations, recent coastline extraction studies can reveal detailed ocean-land interaction changes. In this review, we aim to identify key milestones in coastline extraction using remote sensing by associating the emergence of major research topics with the occurrence of multiple application fields, multiple data sources, and multiple algorithms. Specifically, we define coastlines that can be applied to different application fields, summarize the characteristics of coastline products, and analyze the principles, advantages and disadvantages of methods. On this basis, we discussed the development direction and the challenges involved. This study provides practical insights that can be incorporated into the future development of coastline extraction approaches using remote sensing technologies.

Coastline extraction using remote sensing: a review

With the development of 5G, artificial intelligence, and the Internet of Things, diversified sensors (such as the signal acquisition module) have become more and more important in people’s daily life. According to the extensive use of various distributed wireless sensors, powering them has become a big problem. Among all the powering methods, the self-powered sensor system based on triboelectric nanogenerators (TENGs) has shown its superiority. This review focuses on four major application areas of wireless sensors based on TENG, including environmental monitoring, human monitoring, industrial production, and daily life. The perspectives and outlook of the future development of self-powered wireless sensors are discussed.

https://www.mdpi.com/1424-8220/23/3/1329/pdf?version=1675237119

Recent Progress in Self-Powered Wireless Sensors and Systems Based on TENG

As human–machine interaction (HMI) is inseparable from our daily life, it drives people to pursue a more convenient and intelligent way of life. Most of the traditional HMI devices are manufactured with rugged materials, accompanied by disadvantages such as being fragile and not being bent at random. Hydrogels with good mechanical properties, excellent biocompatibility, and ultra-high sensitivity have been used in HMI systems and received wide attention from researchers. However, due to the high water content of hydrogels, freezing inactivation happens in low-temperature environments. Therefore, it is necessary to develop a new generation of antifreeze gel materials that can withstand harsh cold environments and be used to expand the practical applications of wearable HMI devices. Here, we systematically classify antifreeze hydrogels based on the antifreeze mechanism of inhibiting ice crystallization and inhibiting ice nucleation, and summarize the representative antifreeze agents in antifreeze hydrogels as well as the corresponding preparation methods. Meanwhile, the main properties of antifreeze hydrogels are introduced in detail. Furthermore, we highlight representative applications of antifreeze hydrogels in wearable HMI, including healthcare, touch monitoring, and intelligent control. Finally, we outline the critical challenges currently faced in the field and a view of future trends. 

Anti-freeze hydrogel-based sensors for intelligent wearable human-machine interaction

Deep-learning-assisted low-cost flexible cotton yarn-based triboelectric nanogenerator for ultra-sensitive human-computer merging interfaces

Trehalose-enhanced ionic conductive hydrogels with extreme stretchability, self-adhesive and anti-freezing abilities for both flexible strain sensor and all-solid-state supercapacitor

Flexible strain sensors based on self-adhesive, high-tensile, super-sensitive conductive hydrogels have promising application in human-computer interaction and motion monitoring. Traditional strain sensors have difficulty in balancing mechanical strength, detection function, and sensitivity, which brings challenges to their practical applications. In this work, the double network hydrogel composed of polyacrylamide (PAM) and sodium alginate (SA) was prepared, and MXene and sucrose were used as conductive materials and network reinforcing materials, respectively. Sucrose can effectively enhance the mechanical performance of the hydrogels and improve the ability to withstand harsh conditions. The hydrogel strain sensor has excellent tensile properties (strain >2500%), high sensitivity with a gauge factor of 3.76 at 1400% strain, reliable repeatability, self-adhesion, and anti-freezing ability. Highly sensitive hydrogels can be assembled into motion detection sensors that can distinguish between various strong or subtle movements of the human body, such as joint flexion and throat vibration. In addition, the sensor can be applied in handwriting recognition of English letters by using the fully convolutional network (FCN) algorithm and achieved the high accuracy of 98.1% for handwriting recognition. The as-prepared hydrogel strain sensor has broad prospect in motion detection and human-machine interaction, which provides great potential application of flexible wearable devices.

Self-Adhesive, Anti-Freezing MXene-Based Hydrogel Strain Sensor for Motion Monitoring and Handwriting Recognition with Deep Learning.

Performance Enhancement of Triboelectric Nanogenerators Using Contact-Separation Mode in Conjunction with the Sliding Mode and Multifunctional Application for Motion Monitoring

Nowadays, wearable electronic devices are developing rapidly with the internet of things and human-computer interactions. However, there are problems such as low power, short power supply time, and difficulty in charging, leading to a limited range of practical applications. In this paper, a composite hydrogel composed of polyacrylamide, hydroxypropyl methylcellulose, and MXene (Ti3C2Tx) nanosheets was developed, which formed a stable double-chain structure by hydrogen bonding. The configuration endows the hydrogel with excellent properties, such as high strength, strong stretchability, excellent electrical conductivity, and high strain sensitivity. Based on these characteristics, a flexible multifunctional triboelectric nanogenerator (PHM-TENG) was prepared using the hydrogel as a functional electrode. The nanogenerator can collect biomechanical energy and convert it to 183 V with a maximum power density of 78.3 mW/m2. It is worth noting that PHM-TENG can be applied as a green power source for driving miniature electronics. Also, it can be used as an auto-powered strain sensor that distinguishes letters, enabling monitoring under small strain conditions. This work is anticipated to provide an avenue for the development of new intelligent systems for handwriting recognition.

Triboelectric Nanogenerators Based on Super-Stretchable Conductive Hydrogels with the Assistance of Deep-Learning for Handwriting Recognition.

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