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    Convex surfaces enhance droplet mixing in acoustomicrofluidics more than concave surfaces, despite lower flow velocity. This improved mixing efficiency stems from stronger chaotic advection, not just flow speed.

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    Area of Science:

    • Acoustics
    • Microfluidics
    • Fluid Dynamics

    Background:

    • Efficient micromixing is essential for microfluidic lab-on-a-chip applications.
    • Acoustic waves are utilized to enhance mixing in droplet acoustomicrofluidic systems.
    • Geometric surface design of acoustic transducers can influence fluid behavior.

    Purpose of the Study:

    • To investigate how concave and convex geometric surfaces affect micromixing efficiency in droplet acoustomicrofluidic systems.
    • To determine the impact of surface geometry on acoustic streaming velocity and power consumption.
    • To elucidate the mechanisms behind acoustically enhanced mixing.

    Main Methods:

    • Generating acoustic waves on concave and convex surfaces within droplet acoustomicrofluidic systems.
    • Measuring acoustic streaming velocity and mixing efficiency.
    • Analyzing the relationship between surface geometry, acoustic field, and chaotic advection.

    Main Results:

    • Convex surfaces resulted in approximately 45% lower acoustic streaming velocity compared to concave surfaces.
    • Despite lower velocity, convex surfaces yielded disproportionately higher mixing efficiency (up to 43% increase) compared to concave surfaces (up to 25% increase).
    • Mixing enhancement on convex surfaces is attributed to stronger chaotic advection driven by a diverging acoustic field.

    Conclusions:

    • The geometric design of acoustic transducers significantly impacts micromixing performance in droplet acoustomicrofluidics.
    • Convex surfaces offer a more efficient method for enhancing mixing through chaotic advection, potentially reducing power requirements.
    • Mixing enhancement is not solely dependent on acoustic streaming velocity but also on the nature of the induced fluid motion.