This article presents a thermal control design method for a surface acoustic wave (SAW) device. We designed a heat-dissipation structure and packaging scheme to solve three key issues observed in SAW devices using anisotropic crystals as piezoelectric substrates in acoustofluidics (e.g., lithium niobate): SAW chip cracking caused by thermal stress, SAW chip cracking caused by mismatched thermal expansion coefficients of the packaging materials, and enhancement of the structural strength and stability of the SAW chip. This study establishes the physical model of the designed structure and the relationship between the steady-state working temperature and the physical properties of the material. By comparing these physical properties and numerical calculations, we identified nanosilver adhesive as the most effective bonding material between the SAW chip and the heat sink. In addition to designing and fabricating, we also evaluated our SAW devices experimentally. The results not only confirmed that the abovementioned three key problems were solved but also demonstrated the significant enhancement of the stability of the SAW device.

This study proposes a method to drive a solid by liquid–solid coupling and designs and manufactures a surface acoustic wave actuator to drive a wetted solid ball. The solid ball moves under surface acoustic wave microfluidic acoustic streaming. By theoretical analysis and experimental testing, the driving model is systematically examined in terms of the influence of the device frequency, input power, droplet viscosity, and other parameters on the movement of the ball. The speed at the mark end of the ball under 4.17 W of input power and driving at 60-MHz frequency in pure water reaches 0.175 m/s. Compared with the driving method of a surface acoustic wave linear motor, this wetted solid-driven method easily ensures that the solid ball drives to reach the same order of speed, avoiding numerous problems present in the existing surface acoustic wave linear motors. The proposed method provides important guidance and is of practical significance for the application of surface acoustic wave technology in micromotors and micromanipulation.

This paper proposes an innovative bearing fault diagnosis method aimed at enhancing the accuracy and effectiveness of transfer learning. The innovation lies in the signal preprocessing stage, where a Noise Eliminated Intrinsic Time-Scale Decomposition (NEITD) algorithm is introduced. This algorithm adaptively decomposes unified-phase sine wave signals to effectively extract the geometric mean of the intrinsic rotational component, and selects the optimal decomposition result based on the orthogonality index, significantly improving the quality and reliability of the signals. In addition, fault diagnosis parameters are adaptively optimized using an improved adaptive deep transfer learning (ADTL) network combined with the Jellyfish Search (JS) algorithm, further enhancing diagnostic performance. By innovatively combining signal noise reduction, feature extraction, and deep learning optimization techniques, this method significantly improves fault diagnosis accuracy and robustness. Comparative simulations and experimental analyses show that the NEITD algorithm outperforms traditional methods in both signal decomposition performance and diagnostic accuracy. Furthermore, the NEITD-ADTL-JS method demonstrates stronger sensitivity and recognition capabilities across various fault types, achieving a 5.29% improvement in accuracy.

SAW-assisted swing-angle spray (SAW-SAS) uses frequency modulation to steer an aerosol jet (5–45°). This breaks the conventional 22° Rayleigh angle constraint, enabling precise drug delivery for accelerated in vivo wound healing.