Driven by the loss of bone calcium, the elderly are prone to osteoporosis, and regular routine checks on bone status are necessary, which mainly rely on bone testing equipment. Therefore, wearable real-time healthcare devices have become a research hotspot. Herein, we designed a high-performance flexible ultrasonic bone testing system using axial transmission technology based on quantitative ultrasound theory. First, a new rare-earth-element-doped PMN-PZT piezoelectric ceramic was synthesized using a solid-state reaction, and characterized by X-ray diffraction and SEM. Both a high piezoelectric coefficient d₃₃ = 525 pC/N and electromechanical coupling factors of k₃₃ = 0.77, k_t = 0.58 and k_p = 0.63 were achieved in 1%La/Sm-doped 0.17 PMN-0.47 PZ-0.36 PT ceramics. Combining a flexible PDMS substrate with an ultrasonic array, a flexible hardware circuit was designed which includes a pulse excitation module, ultrasound array module, amplification module, filter module, digital-to-analog conversion module and wireless transmission module, showing high power transfer efficiency and power intensity with values of 35% and 55.4 mW/cm², respectively. Finally, the humerus, femur and fibula were examined by the flexible device attached to the skin, and the bone condition was displayed in real time on the mobile client, which indicates the potential clinical application of this device in the field of wearable healthcare.

Piezoelectric materials have become a key component in sensors and actuators in many industrial fields, such as energy harvesting devices, self-powered structures, biomedical devices, nondestructive testing, owing to the novel properties including high piezoelectric coefficient and electromechanical coupling factors. Piezoelectric thin films integrated on silicon substrates are widely investigated for their high performance and low manufacturing costs to meet the requirement of sensor networks in internet of things (IoT). The aim of this work is to clarify the application and design structure of various piezoelectric thin films types, synthesis methods, and device processes. Based on latest literature, the process of fabricating thin film sensors is outlined, followed by a concise overview of techniques used in microelectromechanical systems (MEMS) processing that can integrate more complex functions to obtain relevant information in surrounding environment. Additionally, by addressing piezoelectric thin films sensors as a cutting-edge technology with the ability to produce self-powered electronic devices, this work delivers incisive conclusions on all aspects of piezoelectric sensor related features. A greater understanding of piezoelectricity is necessary regarding the future development and industry challenges.

Aluminum–air batteries are characterized as “green energy for the 21st century” due to their clear advantages in terms of high current discharge, high specific energy, low cost, and easy-to-obtain electrode materials. This study develops the SLM aluminum anode quality prediction model and evaluates its learning and training results using the BP neural network architecture. By altering the network topology of the SLM aluminum anode quality prediction model, we create a process parameter backpropagation model that takes advantage of the extremely adaptable capabilities of artificial neural networks. The quick and exact selection of process parameters meets the goals of density, self-corrosion current, and anode usage, hence improving the forming quality and processing efficiency of SLM aluminum anodes. The experimental results show that the process parameter backpropagation model’s parameter configurations match to the real densities and self-corrosion currents, which are somewhat higher than the specified target values. The maximum error rate for the aluminum anode forming quality prediction model is 8.23%. Furthermore, the actual anode utilization rate is somewhat lower than the projected target value, indicating that the backpropagation model can satisfy actual production needs.