Due to their easy, fast, and noninvasive natures, acoustic wave methods have been widely used in the physical property measurements of liquids, such as shear waves for viscosity and ultrasound for density. We investigate the physical interaction between approximately GHz longitudinal acoustic waves and simple Newtonian fluids in micro- and nanoscales, and report a longitudinal-acoustic-wave microsensor for microfluidic bulk-modulus measurement. We also verify that the microsensor is insensitive to viscosity, in contrast to shear-wave sensors commonly used for viscosity measurements. With its small size and nanometer wavelengths, the microsensor in nonreflective regimes can be integrated into microfluidics and can make fast measurements even under harsh conditions, such as in limited (15∼200 µm) channel spaces and sample volumes (approximately µl). The longitudinal-acoustic-waves measuring theory is demonstrated by the Mason model, a finite element analytical model, and experiments on glycerol-water mixture samples. The microsensor is originally designed for microfluidic use, nevertheless, it can be used under diverse conditions in which miniaturization matters, namely, in biomedical, chemistry, industry, environmental, healthcare, energy applications, and so on.

Sensors based on the magnetoelastic (ME) effect have been developed for various applications, such as food safety, immunoassays, and health monitoring. A major obstacle to the widespread adoption of ME sensors is the poor understanding of the electrical impedances that constitute the system and how these impedances are influenced by surface loadings. Herein, we derive a lumped-element model to approximately predict the near-resonance electrical behavior of an ME sensor simultaneously loaded with a mass layer and a semi-infinite Newtonian liquid. For more extensive applicability, a hybrid model, combining a modified Butterworth-Van Dyke (mBVD) model and a transmission line model, is derived for the first time to predict the response of the ME sensor under various surface conditions, including rigid solids, fluids, viscoelastic media, or any combinations thereof. The theoretical models are experimentally verified for accuracy and used to quantitatively analyze the mass, viscosity, and modulus of loadings by extracting the electrical parameters. This modeling work is crucial for the development of ME sensors in various applications and for providing insights into the physical mechanisms of advanced devices.

In this paper, we measured bulk modulus of microfluidics using bulk acoustic wave (BAW) MEMS sensors. The 2.5 GHz resonant sensor interacted with fluids by longitudinal bulk acoustic waves. Measurement experiments were carried out on 1 μL glycerol-water sample droplets, and the results were compared with theoretical models. We showed that the longitudinal acoustic wave sensors are sensitive to bulk modulus of microfluidics (sensitivity of 12.42 MHzm ² N ^-1 ). We also validated that the sensors are insensitive to viscosity, which is another important liquid property usually measured by shear acoustic wave sensors.

Rapid hemostasis assessment is essential for managing acute coagulopathy, guiding anticoagulant therapy, and monitoring cardiac surgery patients. Although viscoelastic testing provides valuable guidance for early intervention, its widespread adoption is constrained by large blood usage (~2 mL), prolonged turnaround times (several hours), long assay durations (~25–40 min), and high costs. To overcome these limitations, we developed an innovative, all-in-one magnetoelastic (ME) sensing chip that enables systematic coagulation kinetics monitoring using only 46 μL of whole blood within 15 min. Featuring prepackaged lyophilized reagents and pumpless blood loading, this user-friendly chip is highly cost-effective for disposable use. The experimental results demonstrated good reproducibility for on-chip extrinsic coagulation activation, with clotting parameters maintaining coefficients of variation under 10%. Furthermore, clotting parameters derived from heparin monitoring results exhibited a strong linear correlation that compares favorably with the clinical standard (r = 0.986). Finally, sensitivity evaluation toward various blood components validated the sensor’s dual-mode characterization strategy for identifying coagulation factors and fibrin-related coagulopathies. The proposed ME sensing chip holds promise for bedside testing and flexible, on-demand coagulation monitoring across diverse clinical scenarios.