In the case of patterning, these fields have typically been limited to creating simple lines 18 or grids 19 of cells and microparticles due to the limitations imposed by the transducers and channel geometries. To date, acoustic fields have shown their capacity for versatile microscale actuation activities including patterning 8, acoustic streaming 9, 10, 11, droplet manipulation 12, cell cultures 13, 14, mixing 15, 16 and sorting 8, with actuation down to the single cell level 12, 17. Acoustic fields are a particularly useful method for micromanipulation due to their biocompatibility, high force magnitudes arising from MPa order pressures and wavelengths that can match the length scale of individual cells. In the case of hydrodynamic effects there has been some work using numerical simulations 6, 7 to configure channels to optimize their mixing and dilution characteristics, showing the promise of modifying channel shapes for creating optimized microfluidic devices.Īctively applied forces, rather than just hydrodynamic ones, however, are increasingly integrated into microfluidic devices for more refined and dynamic actuation. This has a large impact when hydrodynamic forces are involved, since the channel geometry can affect how, for example, lift and drag forces are distributed. The design of microfluidic systems for these activities, however, typically occurs on a first-principles basis and generally omits parametric optimization of the microchannel features. A major application space in microfluidics is the patterning and long-term retention of living cells patterning is a powerful tool for the formation of cellular spheroids 1, tissue engineering 2, drug screening 1 and single-cell analysis 3, cellular interactions 4, and mechanobiology 5. Microfluidic platforms are an important tool for precise micromanipulation, using force gradients on the length scale of cells and microparticles themselves. We use then this trained DNN to create novel microchannel architectures for designed microparticle patterning. To rapidly generate designed acoustic fields from microchannel elements we utilize a deep learning approach based on a deep neural network (DNN) that is trained on images of pre-solved acoustic fields. ![]() Designing the channel that results in a desired acoustic field, however, is a non-trivial task. In this work we utilize this approach to create novel acoustic fields. Recent work has demonstrated that the interaction between microfluidic channel walls and travelling surface acoustic waves can generate spatially variable acoustic fields, opening the possibility that the channel geometry can be used to control the pressure field that develops. Such fields, however, typically take the form of only periodic one or two-dimensional grids, limiting the scope of patterning activities that can be performed. Acoustic waves can be used to accurately position cells and particles and are appropriate for this activity owing to their biocompatibility and ability to generate microscale force gradients.
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