Acoustic Streaming Efficiency in a Microfluidic Biosensor with an Integrated CMUT

Abstract

The effect of microchannel height on acoustic streaming velocity and capacitive micromachined ultrasound transducer (CMUT) cell damping was investigated. Microchannels with heights ranging from 0.15 to 1.75 mm were used in experiments, and computational microchannel models with heights varying from 10 to 1800 micrometers were simulated. Both simulated and measured data show local minima and maxima of acoustic streaming efficiency associated with the wavelength of the `bulk acoustic wave excited at 5 MHz frequency. Local minima occur at microchannel heights that are multiples of half the wavelength (150 μm), which are caused by destructive interference between excited and reflected acoustic waves. Therefore, microchannel heights that are not multiples of 150 μm are more favorable for higher acoustic streaming effectiveness since destructive interference decreases the acoustic streaming effectiveness by more than 4 times. On average, the experimental data show slightly higher velocities for smaller microchannels than the simulated data, but the overall observation of higher streaming velocities in larger microchannels is not altered. In additional simulation, at small microchannel heights (10–350 μm), local minima at microchannel heights that are multiples of 150 μm were observed, indicating the interference between excited and reflected waves and causing acoustic damping of comparatively compliant CMUT membranes. Increasing the microchannel height to over 100 μm tends to eliminate the acoustic damping effect as the local minima of the CMUT membrane swing amplitude approach the maximum value of 42 nm, which is the calculated amplitude of the freely swinging membrane under the described conditions. At optimum conditions, an acoustic streaming velocity of over 2 mm/s in a 1.8 mm-high microchannel was achieved.

Four-Channel Ultrasonic Sensor for Bulk Liquid and Biochemical Surface Interrogation

“…The digital signal processing and measurement algorithm can be custom-designed and flexibly adapted since Python with SciPy library utilization is supported. During the testing and experiments described in this paper, a simple pulse amplitude tracking algorithm was used [32]. As the digital signal processing algorithm can operate outside of the real-time process, the practical limitations of the algorithm complexity are only due to the available program memory.…”