Abstract

Digital microfluidics based on sessile droplets has emerged as a promising technology for various applications including biochemical assays, clinical diagnostics, and drug screening. Digital microfluidic platforms provide an isolated microenvironment to prevent cross-contamination and require reduced sample volume. Despite these advantages, the droplet-based technology has the inherent limitation of the quiescent flow conditions at low Reynolds number, which causes mixing samples confined within the droplets to be challenging. Recently, solutal Marangoni flows induced by volatile liquids have been utilized for sessile droplet mixing to address the above-mentioned limitation. The volatile liquid vaporized near a sessile droplet induces a surface tension gradient throughout the droplet interface, leading to vortical flows inside a droplet. This Marangoni flow-based droplet mixing method does not require an external energy source and is easy to operate. However, this passive method requires a comparably long time of a few tens of seconds for complete mixing since it depends on the natural evaporation of the volatile liquid. Here, we propose an improved ultrasound-induced heating method based on a nature-inspired ultrasound-absorbing layer and apply it to enhance solutal Marangoni effect. The heater consists of an interdigital transducer deposited on a piezoelectric substrate and a silver nanowire-polydimethylsiloxane composite as an ultrasound-absorbing layer. When the transducer is electrically actuated, surface acoustic waves are produced and immediately absorbed in the composite layer by viscoelastic wave attenuation. The conversion from acoustic to thermal energy occurs, leading to rapid heating. The heating-mediated enhanced vaporization of a volatile liquid accelerates the solutal Marangoni flows and thus enables mixing high-viscosity droplets, which is unachievable by the passive solutal Marangoni effect. We theoretically and experimentally investigated the enhanced Marangoni flow and confirmed that rapid droplet mixing can be achieved within a few seconds. The proposed heater-embedded sessile droplet mixing platform can be fabricated in small size and easily integrated with other digital microfluidic platforms. Therefore, we expect that the proposed sample mixing method can be utilized for various applications in digital microfluidics and contribute to the advancements in the medical and biochemical fields.

Highlights

  • Along with the advancements of continuous flow microfluidics, digital microfluidics based on micro-/nano-liter sessile droplets has attained much attention as a promising technology for its various advantages and broad applicability [1,2,3]

  • A pair of the solutal Marangoni vortices was observed in all cases, and the solutal Marangoni flow velocity was measured to increase as the temperature of the volatile liquid increased

  • It was attributed that the increased vapor concentration induced by the increased temperature of the volatile liquid led to the enhanced vapormediated solutal Marangoni effect driven by increased local surface tension gradient along the droplet interface

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Summary

Introduction

Along with the advancements of continuous flow microfluidics, digital microfluidics based on micro-/nano-liter sessile droplets has attained much attention as a promising technology for its various advantages and broad applicability [1,2,3]. Small-volume sessile droplets allow efficient use of samples, short reaction time, and miniaturization of the engineering system, which reduces manufacturing costs and can be adapted to portable devices [3, 4]. Basic unit operations within sessile droplets are required such as mixing, migration, merging, and splitting [7]. Rapid and uniform mixing of the sample inside sessile droplets is essential for various biochemical and medical assays in digital microfluidic platforms [8, 9]. The sessile droplet-based microfluidic platform has the inherent limitation of the quiescent flow conditions at low Reynolds number, which results in sample mixing induced by Brownian diffusion [10, 11]

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