Droplet-based microfluidic networks interconnect multiple microfluidic modules which allow to process (e.g., mix, sort, heat, incubate) so-called payload droplets (i.e., droplets containing a biological sample) on a single microfluidic chip. Inside such networks the path of a droplet and, thus, the module which processes it, can be controlled by microfluidic switches. Thus far, these switches are realized by injecting additional control droplets into the network which allow to trigger the switching mechanism by solely exploiting passive hydrodynamic effects. While this eliminates the need of expensive components such as valves, this droplet-controlled switching concept is very sensitive and already slight deviations, e.g., in the control droplet injection could lead to incorrectly triggered switches. In this work, we address this issue by proposing a new concept of pressure-controlled networks which omit the control droplets (and their drawbacks) and, instead, use a single pump in order to drive the switches. Using design automation expertise together with established models, we derive a corresponding blueprint which realizes this idea for a specific network architecture. Simulations based on established methods and design tools confirmed the suitability of the proposed pressure-controlled networks.

Microfluidics is a prospective field which provides technological advances to the life sciences. However, the design process for microfluidic devices is still in its infancy and frequently results in a "trial-and-error" scheme. In order to overcome this problem, simulation methods provide a powerful solution---allowing for deriving a design, validating its functionality, or exploring alternatives without the need of an actual fabricated and costly prototype. To this end, several physical models are available such as Computational Fluid Dynamics (CFD) or the 1-dimensional analysis model. However, while CFD-simulations have high accuracy, they also have high costs with respect to setup and simulation time. On the other hand, the 1D-analysis model is very efficient but lacks in accuracy when it comes to certain phenomena. In this work, we present ideas to combine these two models and, thus, to provide an accurate and efficient simulation approach for microfluidic networks. A case study confirms the general suitability of the proposed approach.

Droplet-based microfluidic networks allow to process biological or medical samples by standard unit operations, such as mixing, incubating, sorting, or sensing. However, many of these networks usually perform such operations in a predefined way and, thus, lack in their flexibility. To overcome this problem, ring networks are used, since they allow to execute multiple operations in a row. But while several concepts and also prototypical implementations exist that realize such ring networks, the design process for them is still mainly conducted manually thus far. This is a severe drawback since various aspects, such as the dimensions of the channels, the effects of droplets, the used fluids, the volumetric flow rates inside the channels, etc., have to be considered for this purpose. In this article, we propose design automation methods which address this problem. The proposed solution will automatically generate a proper design as well as correspondingly needed droplet sequences. A case study demonstrates the applicability of the resulting methods and simulations confirms the validity of the proposed approach.

Microfluidic networks can be applied to droplet-based Lab-on-a-Chip devices, where droplets are used to confine samples which flow through closed microchannels along different paths in order to execute (bio-)chemical experiments. In order to allow this routing of droplets, the design of the microfluidic network has to be precisely defined and afterward fabricated. However, neither the fabrication process nor the applied materials and components are perfect and, therefore, the fabricated microfluidic device frequently contains defects (produced by fabrication tolerances, properties of the used material, or fluctuation of supply pumps). Those may have a severe impact on the behavior of the microfluidic network and can even render the network useless. Furthermore, these defects complicate the design process, which eventually results in a “trial-and-error”-approach causing high costs with respect to time and money. Consequently, designers want to anticipate how robust their design is against those defects. This article, for the first time, describes how these defects can be abstracted, which eventually allows to evaluate the robustness already in the design process. We additionally introduce models considering single and multiple defects as well as corresponding methods for their analysis. Evaluations on a microfluidic network which is used to screen drug compounds confirm that the resulting robustness analysis indeed provides designers with a simple metric to decide how sensitive their design is against defects. The models and methods proposed in this article are grounded on the established 1-D analysis model.

The complexity of droplet microfluidics grows with the implementation of parallel processes and multiple functionalities on a single device. This poses a severe challenge to the engineer designing the corresponding microfluidic networks. In today’s design processes, the engineer relies on calculations, assumptions, simplifications, as well as his/her experiences and intuitions. To validate the obtained specification of the microfluidic network, usually a prototype is fabricated and physical experiments are conducted thus far. In case the design does not implement the desired functionality, this prototyping iteration is repeated—obviously resulting in an expensive and time-consuming design process. To avoid unnecessary debugging loops involving fabrication and testing, simulation methods could help to initially validate the specification of the microfluidic network before any prototype is fabricated. However, state-of-the-art simulation tools come with severe limitations, which prevent their utilization for practically relevant applications. More precisely, they are often not dedicated to droplet microfluidics, cannot handle the required physical phenomena, are not publicly available, and can hardly be extended. In this work, we present an advanced simulation approach for droplet microfluidics that addresses these shortcomings and, eventually, allows simulating practically relevant applications. To this end, we propose a simulation framework at the one-dimensional analysis model, which directly works on the specification of the design, supports essential physical phenomena, is publicly available, and is easy to extend. Evaluations and case studies demonstrate the benefits of the proposed simulator: While current state-of-the-art tools were not applicable for practically relevant microfluidic networks, the proposed simulator allows reducing the design time and costs, e.g., of a drug screening device from one person month and USD 1200, respectively, to just a fraction of that.