MXene/PPy@PDMS sponge-based flexible pressure sensor for human posture recognition with the assistance of a convolutional neural network in deep learning.

The combination of flexible sensors and deep learning has attracted much attention as an efficient method for the recognition of human postures. In this paper, an in situ polymerized MXene/polypyrrole (PPy) composite is dip-coated on a polydimethylsiloxane (PDMS) sponge to fabricate an MXene/PPy@PDMS (MPP) piezoresistive sensor. The sponge sensor achieves ultrahigh sensitivity (6.8925 kPa-1) at 0-15 kPa, a short response/recovery time (100/110 ms), excellent stability (5000 cycles) and wash resistance. The synergistic effect of PPy and MXene improves the performance of the composite materials and facilitates the transfer of electrons, making the MPP sponge at least five times more sensitive than sponges based on each of the individual single materials. The large-area conductive network allows the MPP sensor to maintain excellent electrical performance over a large-scale pressure range. The MPP sensor can detect a variety of human body activity signals, such as radial artery pulse and different joint movements. The detection and analysis of human motion data, which is assisted by convolutional neural network (CNN) deep learning algorithms, enable the recognition and judgment of 16 types of human postures. The MXene/PPy flexible pressure sensor based on a PDMS sponge has broad application prospects in human motion detection, intelligent sensing and wearable devices.

Multifunctional, Self-Adhesive MXene-Based Hydrogel Flexible Strain Sensors for Hand-Written Digit Recognition with Assistance of Deep Learning.

The conductive hydrogel as a flexible sensor not only has certain mechanical flexibility but also can be used in the field of human health detection and human-computer interaction. Herein, by introduction of tannic acid (TA) with MXene into the polyacrylamide (PAM)/carboxymethyl chitosan (CMC) double-network hydrogel, a hydrogel with high stretchability, self-adhesion, and high sensitivity was prepared. CMC and PAM form a semi-interpenetrating double-network of high toughness and durability through electrostatic interactions and multiple hydrogen bonding networks. The abundant hydrophilic functional groups on TA and MXene form multiple hydrogen bonds simultaneously with the polymer network, ensuring high stretchability and sensitivity of the hydrogel. The hydrogel can display an accurate response to a variety of stimulus signals and can monitor both human joint movements and small physiological signal changes. It can also be combined with deep learning algorithms to classify handwritten digits with an accuracy rate of 98%. This work can promote the application of hydrogel sensors with durability and high sensitivity. The combination of algorithms and flexible sensors provides important ideas for the further development of flexible devices.

Efficient Fabrication of TPU/MXene/Tungsten Disulfide Fibers with Ultra-Fast Response for Human Respiratory Pattern Recognition and Disease Diagnosis via Deep Learning.

Flexible wearable pressure sensors have received increasing attention as the potential application of flexible wearable devices in human health monitoring and artificial intelligence. However, the complex and expensive process of the conductive filler has limited its practical production and application on a large scale to a certain extent. This study presents a kind of piezoresistive sensor by sinking nonwoven fabrics (NWFs) into tungsten disulfide (WS2) and Ti3C2Tx MXene solutions. With the advantages of a simple production process and practicality, it is conducive to the realization of large-scale production. The assembled flexible pressure sensor exhibits high sensitivity (45.81 kPa-1), wide detection range (0-410 kPa), fast response/recovery time (18/36 ms), and excellent stability and long-term durability (up to 5000 test cycles). Because of the high elastic modulus of MXene and the synergistic effect between WS2 and MXene, the detection range and sensitivity of the piezoresistive pressure sensor are greatly improved, realizing the stable detection of human motion status in all directions. Meanwhile, its high sensitivity at low pressure allows the sensor to accurately detect weak signals such as weak airflow and wrist pulses. In addition, combining the sensor with deep-learning makes it easy to recognize human respiratory patterns with high accuracy, demonstrating its potential impact in the fields of ergonomics and low-cost flexible electronics.

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

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.

Ultrastretchable, Self-Healing Conductive Hydrogel-Based Triboelectric Nanogenerators for Human-Computer Interaction.

The rapid development of wearable electronic devices and virtual reality technology has revived interest in flexible sensing and control devices. Here, we report an ionic hydrogel (PTSM) prepared from polypropylene amine (PAM), tannic acid (TA), sodium alginate (SA), and MXene. Based on the multiple weak H-bonds, this hydrogel exhibits excellent stretchability (strain >4600%), adhesion, and self-healing. The introduction of MXene nanosheets endows the hydrogel sensor with a high gauge factor (GF) of 6.6. Meanwhile, it also enables triboelectric nanogenerators (PTSM-TENGs) fabricated from silicone rubber-encapsulated hydrogels to have excellent energy harvesting efficiency, with an instantaneous output power density of 54.24 mW/m2. We build a glove-based human-computer interaction (HMI) system using PTSM-TENGs. The multidimensional signal features of PTSM-TENG are extracted and analyzed by the HMI system, and the functions of gesture visualization and robot hand control are realized. In addition, triboelectric signals can be used for object recognition with the help of machine learning techniques. The glove based on PTSM-TENG achieves the classification and recognition of five objects through contact, with an accuracy rate of 98.7%. Therefore, strain sensors and triboelectric nanogenerators based on hydrogels have broad application prospects in man-machine interface, intelligent recognition systems, auxiliary control systems, and other fields due to their excellent stretchable and high self-healing performance.

Transient response of bridge piers under eccentric impact of near-fault earthquake.

To study the influence of near-fault earthquake on pier failure, establish a double-span continuous girder bridge. The seismic response of the bridge is calculated using the transient wave function expansion method and the indirect mode function superposition method. Solve the dynamic and displacement responses, and the effect of the vertical separation of the bridge on the eccentric compression of the pier is analyzed. The results show that under the near-fault vertical seismic action, the separation can significantly change the horizontal deformation at the top of the pier, and neglecting the separation may underestimate the eccentric compression of the pier. Calculations for different pier heights and girder spans show that separation has a greater effect on the longitudinal deformation of the pier top. Therefore, the reasonable design of longitudinal limit device on pier top in the near-fault area is helpful to reduce the damage of eccentric bridge impact.