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Register a new userSpontaneous oscillations and negative-conductance transitions in microfluidic networks
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The tendency for flows in microfluidic systems to behave linearly poses a challenge for designing integrated flow control schemes to carry out complex fluid processing tasks. This hindrance has led to the use of numerous external control devices to manipulate flows, thereby thwarting the potential scalability and portability of lab-on-a-chip technology. Here, we devise a microfluidic network exhibiting nonlinear flow dynamics that enable new mechanisms for on-chip flow control. This network is shown to exhibit oscillatory output patterns, bistable flow states, hysteresis, signal amplification, and negative-conductance transitions, all without reliance on dedicated external control hardware, movable parts, flexible components, or oscillatory inputs. These dynamics arise from nonlinear fluid inertia effects in laminar flows that we amplify and harness through the design of the network geometry. We suggest that these results, which are supported by fluid dynamical simulations and theoretical modeling, have the potential to inspire development of new built-in control capabilities, such as on-chip timing and synchronized flow patterns.
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Microfluidic techniques have been extensively developed to realize micro-total analysis systems in a small chip. For microanalysis, electro-magnetic forces have generally been utilized for the trapping of objects, but hydrodynamics has been little explored despite its relevance to pattern formation. Here, we report that water-in-oil (W/O) droplets can be transported in the grid of an array of other large W/O droplets. As each droplet approaches an interspace of the large droplet array, while exhibiting persistent back-and-forth motion, it is conveyed at a velocity equal to the droplet array. We confirm the appearance of closed streamlines in a numerical simulation, suggesting that a vortex-like stream is involved in trapping the droplet. Furthermore, more than one droplet is also conveyed as an ordered cluster with dynamic reposition.
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