High-sensitivity, ultrawide linear range, antibacterial textile pressure sensor based on chitosan/MXene hierarchical architecture.

It is still a great challenge for the flexible piezoresistive pressure sensors to simultaneously achieve wide linearity and high sensitivity. Herein, we propose a high-performance textile pressure sensor based on chitosan (CTS)/MXene fiber. The hierarchical "point to line" architecture enables the pressure sensor with high sensitivity of 1.16 kPa-1 over an ultrawide linear range of 1.5 MPa. Furthermore, the CTS/MXene pressure sensor possesses a low fatigue over 1000 loading/unloading cycles under 1.5 MPa pressure load, attributed to the strong chemical bonding between CTS fiber and MXene and excellent mechanical stability. Besides, the proposed sensor shows good antibacterial effect benefiting from the strong interaction between polycationic structure of CTS/MXene and the predominantly anionic components of bacteria surface. The sensor is also applied to detect real-time human action, an overall classification accuracy of 98.61% based on deep neural network-convolutional neural network (CNN) for six human actions is realized.

Capping layer enabled controlled fragmentation of two-dimensional materials by cold drawing.

Cold drawing, a well-established processing technique in the polymer industry, was recently revisited and discovered as an efficient material structuring method to create ordered patterns in composites consisting of both cold-drawable polymers and brittle target materials. Such a high-yield and low-cost manufacturing technique enables the large-scale fabrication of micro-ribbon structures for a wide range of functional materials, including two-dimensional (2D) layered materials. Compared to the abundant phenomenological results from experiments, however, the underlying mechanisms of this technique are not fully explored. Here, supported by experimental investigation, finite element calculations, and theoretical modeling, we systematically study the effect of a capping layer on the controlled fragmentation of 2D materials deposited on polymer substrates during the cold drawing. The capping layer is found to prevent the premature fracture of the 2D thin films during elastic deformation of the substrate, when a specific requirement proposed by the theoretical model is satisfied. Controlled fragmentation is enabled in the necking stage due to the protective effect of the capping layer, which also influences the size of the resulting fragments. Flexible and stretchable electrodes based on 2D material ribbons are fabricated to demonstrate the effectiveness of the proposed roadmap. This study gives an accurate understanding of interactions between 2D materials, polymer substrates, and capping layers during cold drawing, and offers guidance for potential applications such as flexible electronics.

Crack-Across-Pore Enabled High-Performance Flexible Pressure Sensors for Deep Neural Network Enhanced Sensing and Human Action Recognition.

Flexible pressure sensors with high sensitivity over a broad pressure range are highly desired, yet challenging to build to meet the requirements of practical applications in daily activities and more significant in some extreme environments. This work demonstrates a thin, lightweight, and high-performance pressure sensor based on flexible porous phenyl-silicone/functionalized carbon nanotube (PS/FCNT) film. The formed crack-across-pore endows the pressure sensor with high sensitivity of 19.77 kPa-1 and 1.6 kPa-1 in the linear range of 0-33 kPa and 0.2-2 MPa, respectively, as well as ultralow detection limit (∼1.3 Pa). Furthermore, the resulting pressure sensor possesses a low fatigue over 4000 loading/unloading cycles even under a high pressure of 2 MPa and excellent durability (>6000 cycles) after heating at high temperature (200 °C), attributed to the strong chemical bonding between PS and FCNT, excellent mechanical stability, and high temperature resistance of PS/FCNT film. These superior properties set a foundation for applying the single sensor device in detecting diverse stimuli from the very low to high pressure range, including weak airflow, sway, vibrations, biophysical signal monitoring, and even car pressure. Besides, a deep neural network based on transformer (TRM) has been engaged for human action recognition with an overall classification rate of 94.96% on six human actions, offering high accuracy in real-time practical scenarios.