Chengwei Zhang

Force and acceleration sensors based on graphene-induced nonradiative transitions and silicon proof mass structures, supported by various two-dimensional (2D) material cantilevers, are investigated. The results show that hexagonal boron nitride, due to its low Young’s modulus and ultrathin thickness, offers superior deformability, thereby enhancing the performance of the microelectromechanical systems (MEMS) sensor. Additionally, the extreme sensitivity of graphene-induced nonradiative transitions to distance allows the sensor to maintain high performance while minimizing its overall dimensions. In force sensing applications, the device achieves a measurement range of 0–400 pN with a sensitivity of 0.50% pN⁻¹. For acceleration sensing, it exhibits a measurement range of 0–6 g, with an accelerometer sensitivity of 17.24% g⁻¹. This work not only demonstrates the feasibility of integrating 2D materials with MEMS, but also establishes a technical foundation for the development of multifunctional MEMS sensors designed for the Internet of Things and implantable medical devices.

Diesel-natural gas dual fuel engine has gained increasing interesting in recent years because of its excellent power and economy. However, the reliability of the dual fuel engine does not meet the requirements of practical application. The piston maximun temperature (PMT) of the dual fuel engine easily exceeds the security border. In view of this, this paper proposes a method based on the lasso regression to estimate the PMT of the dual fuel engine, so as to real-timely monitor the health state of the dual fuel engine. Specifically, PMTs under some working conditions were offline acquired by the finite element analysis with ANSYS. A model is presented to describe the relationship between the PMT and some indirect engine variables, including NOx emission, excess air coefficient, engine speed, and inlet pressure, and the model parameters are optimized using the lasso regression algorithm, which can be easily implemented by the electronic control unit (ECU). Finally, the model is employed to real-timely estimate the PMT of the dual fuel engine. Experiments reveal that the proposed model produces satisfying predictions with deviations less than 10 °C.