Flexible piezoelectric nanogenerators (PENGs) used in body movement real-time monitoring are of great interest for their wide application potential such as in the field of smart healthcare. In this work, a self-powered BaTiO₃/Polydimethylsiloxane (PDMS) PENG for body movement sensing was successfully fabricated by extrusion 3D printing. Matrix system composed of different ratios of PDMS was selected based on the rheological property of materials. Experimental investigations were conducted to examine the impact of printing pressure and speed on the linewidth. Subsequently, the extrusion parameters for nanogenerators were determined based on the printed linewidth. The composite showed good ferroelectric property. After polarization, the nanogenerators exhibited an improvement in output performance of up to 55.2%. Additionally, the device demonstrated a good linear relationship between voltage and tapped force test by an electromechanical vibrator. Successful detection of body or muscle movement signals was achieved when the nanogenerator was mounted on the human finger, throat, or foot using a wearable sock, highlighting its potential for applications in self-powered wearable devices for smart healthcare.

The dimensions of material extrusion 3D printing filaments play a pivotal role in determining processing resolution and efficiency and are influenced by processing parameters. This study focuses on four key process parameters, namely, nozzle diameter, nondimensional nozzle height, extrusion pressure, and printing speed. The design of experiment was carried out to determine the impact of various factors and interaction effects on filament width and height through variance analysis. Five machine learning models (support vector regression, backpropagation neural network, decision tree, random forest, and K-nearest neighbor) were built to predict the geometric dimension of filaments. The models exhibited good predictive performance. The coefficients of determination of the backpropagation neural network model for predicting line width and line height were 0.9025 and 0.9604, respectively. The effect of various process parameters on the geometric morphology based on the established prediction model was also studied. The order of influence on line width and height, ranked from highest to lowest, was as follows: nozzle diameter, printing speed, extrusion pressure, and nondimensional nozzle height. Different nondimensional nozzle height settings may cause the extruded material to be stretched or squeezed. The material being in a stretched state leads to a thin filament, and the regularity of processing parameters on the geometric size is not strong. Meanwhile, the nozzle diameter exhibits a significant impact on dimensions when the material is in a squeezing state. Thus, this study can be used to predict the size of printing filament structures, guide the selection of printing parameters, and determine the size of 3D printing layers.

Touch sensors play a pivotal role in the domain of human-machine interfaces. To streamline the complexity of array electrodes, this paper presents a novel functional gradient piezoelectric composite nanogenerator touch sensor by utilizing only a pair of electrodes for touch position detection through voltage measurements. The rheological characteristics of the BaTiO₃/PDMS composites were evaluated, and the functional gradient nanogenerator was successfully fabricated by extrusion 3D printing. Finite element simulations revealed a significant concentration of stress in the vicinity of the touch position, which gradually diminished farther away. The output voltage demonstrated substantial variation across different touch positions, which was validated by experimental results exhibiting a similar trend. Comparing voltage values enabled the distinction of touch positions, as confirmed by both simulation and experimental findings. The nanogenerator can realize the function of human-machine interfaces.