Giant Piezoresistance in B-Doped SiC Nanobelts with a Gauge Factor of -1800

Electrically Controlled High Sensitivity Strain Modulation in MoS2 Field-Effect Transistors via a Piezoelectric Thin Film on Silicon Substrates

Strain can modulate bandgap and carrier mobilities in two-dimensional (2D) materials. Conventional strain-application methodologies relying on flexible/patterned/nanoindented substrates are limited by low thermal tolerance, poor tunability, and/or scalability. Here, we leverage the converse piezoelectric effect to electrically generate and control strain transfer from a piezoelectric thin film to electromechanically coupled 2D MoS2. Electrical bias polarity change across the piezo film tunes the nature of strain transferred to MoS2 from compressive (∼0.23%) to tensile (∼0.14%) as verified through Raman and photoluminescence spectroscopies and substantiated by density functional theory calculations. The device architecture, on silicon substrate, integrates an MoS2 field-effect transistor on a metal-piezoelectric-metal stack enabling strain modulation of transistor drain current (130×), on/off ratio (150×), and mobility (1.19×) with high precision, reversibility, and resolution. Large, tunable tensile (1056) and compressive (-1498) strain gauge factors, electrical strain modulation, and high thermal tolerance promise facile integration with silicon-based CMOS and micro-electromechanical systems.

Combining 10 Matrix Pressure Sensor to Read Human Body’s Pressure in Sleeping Position in Relation with Decubitus Patients

This work uses piezoresistive matrix pressure sensors to map the human body’s pressure profile in a sleeping position. This study aims to detect the area with the highest pressure, to visualize the pressure profile into a heatmap, and to reduce decubitus by alerting the subject to changes in position. This research combines ten matrix pressure sensors to read a larger area. This work uses a Raspberry Pi 4 Model B with 8 GB memory as the data processor, and every sensor sheet uses ATMEGA 2560 as the sensor controller for data acquisition. Sensor calibration is necessary because each output must have the same value for the same weight value; the accuracy between different sensors is around 95%. After the calibration process, the output data must be smoothed to make visual representations more distinguishable. The areas with the highest pressure are the heel, tailbone, back, and head. When the subject’s weight increases, pressure on the tailbone and back increases, but that on the heel and head does not. The results of this research can be used to monitor people’s sleeping positions so that they can reduce the risk of decubitus.