Flexible pressure sensors with a highly pressure- and strain-sensitive layer based on nitroxyl radical-grafted hollow carbon spheres

Tactile corpuscle-inspired piezoresistive sensors based on (3-aminopropyl) triethoxysilane-enhanced CNPs/carboxylated MWCNTs/cellulosic fiber composites for textile electronics

Recently, wearable electronic products and gadgets have developed quickly with the aim of catching up to or perhaps surpassing the ability of human skin to perceive information from the external world, such as pressure and strain. In this study, by first treating the cellulosic fiber (modal textile) substrate with (3-aminopropyl) triethoxysilane (APTES) and then covering it with conductive nanocomposites, a bionic corpuscle layer is produced. The sandwich structure of tactile corpuscle-inspired bionic (TCB) piezoresistive sensors created with the layer-by-layer (LBL) technology consists of a pressure-sensitive module (a bionic corpuscle), interdigital electrodes (a bionic sensory nerve), and a PU membrane (a bionic epidermis). The synergistic mechanism of hydrogen bond and coupling agent helps to improve the adhesive properties of conductive materials, and thus improve the pressure sensitive properties. The TCB sensor possesses favorable sensitivity (1.0005 kPa-1), a wide linear sensing range (1700 kPa), and a rapid response time (40 ms). The sensor is expected to be applied in a wide range of possible applications including human movement tracking, wearable detection system, and textile electronics.

Force-Sensitive Interface Engineering in Flexible Pressure Sensors: A Review

Flexible pressure sensors have received extensive attention in recent years due to their great importance in intelligent electronic devices. In order to improve the sensing performance of flexible pressure sensors, researchers are committed to making improvements in device materials, force-sensitive interfaces, and device structures. This paper focuses on the force-sensitive interface engineering of the device, which listing the main preparation methods of various force-sensitive interface microstructures and describing their respective advantages and disadvantages from the working mechanisms and practical applications of the flexible pressure sensor. What is more, the device structures of the flexible pressure sensor are investigated with the regular and irregular force-sensitive interface and accordingly the influences of different device structures on the performance are discussed. Finally, we not only summarize diverse practical applications of the existing flexible pressure sensors controlled by the force-sensitive interface but also briefly discuss some existing problems and future prospects of how to improve the device performance through the adjustment of the force-sensitive interface.

High-sensitivity tactile sensor based on Ti2C-PDMS sponge for wireless human-computer interaction

Tremendous attention has been paid to high-performance flexible tactile sensors owing to their potential applications in bionic skin, wearable electronics, the Internet of Things, etc.However, the majority of pressure sensors require an intricately designed nanostructure requiring a high-cost complex manufacturing process. Therefore, the high-throughput and low-cost technology to produce high-sensitivity, flexible, pressure-sensitive materials with a large responding range is urgently needed. Herein, a novel flexible piezoresistive tactile sensor is fabricated based on the Ti₂C-PDMS sponge as the conductive elastomer. The sensor exhibits a high sensitivity of 279 kPa^-1in a wide pressure range (0-34.4 kPa). The response time is as fast as 0.45 s with excellent durability over 4,000 cycles. Moreover, a 16-pixel wireless sensor system is fabricated and a series of applications have been demonstrated, including real-time force perception and pressure morphology feedback, which promote the potential applications in the visualizing of pressure distribution, human-machine communication and wearable devices.

A Novel Bilinear Feature and Multi-Layer Fused Convolutional Neural Network for Tactile Shape Recognition

Convolutional neural networks (CNNs) can automatically learn features from pressure information, and some studies have applied CNNs for tactile shape recognition. However, the limited density of the sensor and its flexibility requirement lead the obtained tactile images to have a low-resolution and blurred. To address this issue, we propose a bilinear feature and multi-layer fused convolutional neural network (BMF-CNN). The bilinear calculation of the feature improves the feature extraction capability of the network. Meanwhile, the multi-layer fusion strategy exploits the complementarity of different layers to enhance the feature utilization efficiency. To validate the proposed method, a 26 class letter-shape tactile image dataset with complex edges was constructed. The BMF-CNN model achieved a 98.64% average accuracy of tactile shape. The results show that BMF-CNN can deal with tactile shapes more effectively than traditional CNN and artificial feature methods.