FEM-Based Design, Modeling, and Optimization of a High-Performance Hydrogen SAW Sensor Using a GO/Pd Nanoparticles Heterojunction

This work presents the simulation and optimization of a novel surface acoustic wave (SAW) hydrogen sensor based on a graphene oxide (GO) layer decorated with palladium nanoparticles (Pd NPs) on a lithium niobate (LiNbO 3 ) substrate. A finite element method (FEM) was used to model the sensor, and a response surface methodology (RSM) was applied to optimize key parameters, including substrate thickness, interdigital transducer spacing, and operating frequency. The design space was statistically explored using the least squares method (LSM). The optimized configuration yielded an insertion loss of −1.06 dB at 80 MHz with a 74.74 µm substrate thickness and 6.90 mm transducer spacing. The sensor demonstrated excellent performance, with time delay and amplitude sensitivities of 26 ns and 0.72 dB per atomic fraction (af) of hydrogen in Pd, respectively. These results highlight the potential of the proposed SAW device for reliable hydrogen leak detection in safety-critical environments.