In recent years, additive manufacturing has steadily gained attention in both research and industry. Applications range from prototyping to small-scale production, with 3D printing offering reduced logistics overheads, better design flexibility and ease of use compared with traditional fabrication methods. In addition, printer and material costs have also decreased rapidly. These advantages make 3D printing attractive for application in microfluidic chip fabrication. However, 3D printing microfluidics is still a new area. Is the technology mature enough to print complex microchannel geometries, such as droplet microfluidics? Can 3D-printed droplet microfluidic chips be used in biological or chemical applications? Is 3D printing mature enough to be used in every research lab? These are the questions we will seek answers to in our systematic review. We will analyze (1) the key performance metrics of 3D-printed droplet microfluidics and (2) existing biological or chemical application areas. In addition, we evaluate (3) the potential of large-scale application of 3D printing microfluidics. Finally, (4) we discuss how 3D printing and digital design automation could trivialize microfluidic chip fabrication in the long term. Based on our analysis, we can conclude that today, 3D printers could already be used in every research lab. Printing droplet microfluidics is also a possibility, albeit with some challenges discussed in this review.

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This study describes the construction of a compact empirical mathematical model for a flowfocusing microfluidic droplet generator. The application case is a portable, low-cost flow cytometry system for microbiological applications, with water droplet sizes of 50-70 micrometer range and droplet generation rates of 500-1500 per second. In this study, we demonstrate that for the design of reliable microfluidic systems, the availability of an empirical model of droplet generation is a mandatory precondition that cannot be achieved by time-consuming simulations based on detailed physical models. When introducing the concept of a compact empirical model, we refer to a mathematical model that considers general theoretical estimates and describes experimental behavioral trends with a minimal set of easily measurable parameters. By interpreting the experimental results for different water-and oil-phase flow rates, we constructed a minimal 3-parameter droplet generation rate model for every fixed water flow rate by relying on submodels of the water droplet diameter and effective ellipticity. As a result, we obtained a compact model with an estimated 5-10% accuracy for the planned typical application modes. The main novelties of this study are the demonstration of the applicability of the linear approximation model for droplet diameter suppression by the oil flow rate, introduction of an effective ellipticity parameter to describe the droplet form transformation from a bullet-like shape to a spherical shape, and introduction of a machine learning correction function that could be used to fine-tune the model during the real-time operation of the system.