Research has shown that SAW (surface acoustic wave) devices with an LGS/Pt (langasite La₃Ga₅SiO₁₄/platinum) structure are useful in high-temperature sensor applications. Extreme high temperature brings great acoustic attenuation because of the thermal radiation loss, which requires that the sensing device offer a sufficiently high quality factor (Q) and a low loss. Therefore, it is necessary to improve the performance of the quality factor as much as possible so as to better meet the application of high-temperature sensors. Based on these reasons, the main work of this paper was to extract accurate simulation parameters to optimize the Pt/LGS device and obtain Q-value device parameters. Optimization of SAW devices with LGS/Pt structure for sensing extreme high temperature was addressed by employing a typical coupling of modes (COM) model in this work. Using the short pulse method, the reflection coefficient of Pt electrodes on LGS substrate was extracted accurately by characterizing the prepared SAW device with strategic design. Other relevant parameters for COM simulation were determined by finite element analysis. To determine the optimal design parameters, the COM simulation was conducted on the SAW sensing device with a one-port resonator pattern for sensing extreme temperature, which allows for a larger Q-value and low insertion loss. Experimental results validate the theoretical simulation. In addition, the corresponding high-temperature characteristics of the prepared sensing device were investigated.

We propose a new theoretical method and experimental investigation to characterize the growth process of rime ice through surface acoustic waves (SAWs). The Biot theory of dual-phase porous media was employed to construct a theoretical model to describe the acoustic wave propagation in porous rime ice; the transient changes in acoustic propagation attenuation and velocity were simulated; and the icing sensing mechanism of the acoustic wave propagation attenuation (∼18 dB) and the transient change in velocity (0.8 m/s) during the icing process was obtained. A 177.5 MHz waveguide type SAW micro–nano-device with an SU8/ST-90°X quartz multilayer composite film structure and a microfluidic cavity on the top of the acoustic device were designed and fabricated, and the transient attenuation in the growth of rime ice was evaluated experimentally. The experimental results indicated that the appearance of rime ice can be monitored accurately by utilizing the transient changes in acoustic wave velocity and attenuation. Therefore, the acoustic method can aid in the early warning of the development status of rime ice.

a novel configuration of wireless and passive Love wave based ice sensing device employing waveguide structure of SiO 2 /36°YX LiTaO 3 with high mass sensitivity and excellent temperature stability was presented in this work. A reflective delay line consists of a transducer and three reflectors on 36° YX LiTaO 3 substrate with SiO 2 guiding layer was developed as the sensor element. Prior to sensing device development, the coupling of modes (COM) modeling was performed to device simulation. Using the typical lithography technology, the sensing device was developed at 433 MHz, and packaged with a permeable film. Using the experimental setup composed of a high-low temperature chamber, wireless transceiver module and sensing device, the response in time domain of the developed sensing device was characterized. The mass loading effect in icing process modulates the propagation of Love wave, resulting in the corresponding phase response shift. By wireless measurement, the phase change from water to ice was detected, and the measured results addressed the feasibility and effectiveness of the proposed wireless and passive Love wave ice sensor.

Icing causes instantaneous attenuation of surface acoustic wave (SAW), which can be leveraged for fast and highly sensitive detection of ice accretion. However, the specific mechanism of acoustic attenuation during the icing process is unclear, leading to sensor design and signal decoupling challenges. In this research work, we extract the microscopic pore characteristics of icing and establish a three-dimensional pore model for simulation. A simplified ice-covered SAW propagation model was constructed using the COMSOL multiphysics analysis method, which reveals the coupling and transfer process of acoustic surface wave energy with ice-covered through transient simulation, thus elucidating the influence of ice coverage on SAW propagation. First, part of the acoustic energy from the substrate is guided into the ice accretion layer via a “pseudo-waveguide effect,” with the amount of energy penetration being related to the ice thickness. Second, the pore structure of the ice layer causes a discontinuity of acoustic impedance, and the “acoustic-absorbing-like effect” of the ice pores leads to significant acoustic attenuation, the amount of which is related to the porosity of the ice layer. This conclusion was further verified through actual icing experiments and the numerical calculation model developed also verified the reliability of the simulation results and experimental results. These findings clarify the detection mechanism of SAW-based icing sensing for the first time, advancing the potential applications of SAW for ice accretion detection.

Surface acoustic wave (SAW) gas sensors are widely used in electronic nose systems due to high sensitivity, rapid response, and wide detection range. However, their baseline is highly susceptible to significant drift caused by environmental temperature/humidity fluctuations, severely degrading gas concentration detection accuracy. To address this, this paper proposes a baseline drift prediction method for SAW sensors, termed the Dynamic Recurrent Kalman Network (DRKN). DRKN employs a three-stage prediction-correction-denoising architecture. It integrates ambient temperature/humidity variations, combines the dynamic modeling capability of RNNs with the state estimation framework of KalmanNet, and incorporates a segment-optimized denoising mechanism to achieve precise prediction of nonlinear baseline drift. Experimental results on two real-world collected datasets demonstrate the superior performance of DRKN in both short- and long-term prediction tasks. Notably, for long-term prediction, DRKN outperforms the suboptimal model by 90% and 58% on the respective datasets. This approach provides an effective solution for mitigating baseline drift and enhancing SAW gas sensor accuracy.