Channel-based microfluidic devices, often in the form of so-called Lab-on-a-Chip (LoC), have a broad range of applications in domains such as biology, chemistry, medicine, etc. Many of these applications rely on merging of droplets, e.g., in order to trigger some kind of reaction inside the droplets. However, the design process of LoCs is, in general, still in its infancy and mostly relies on simplifications, assumptions, as well as the expertise of the designer-making this process rather error-prone and frequently resulting in a “trial-and-error” approach. Simulation tools can help in this regard. While Computational Fluid Dynamics (CFD) tools can simulate the merging of droplets, their complex setup and computational efforts limit their applicability to rather small components and do not allow simulations of larger microfluidic devices. Instead, considerations on the so-called one-dimensional model (1D model) offer a more abstract and, hence, computationally much faster simulation. However, currently there are no simulators based on the 1D model available that support the merging of droplets-severely restricting the applicability of such simulators. In this work, we address this problem by proposing a concept for droplet merging based on the 1D-model and implementing these ideas on top of an already existing 1D-simulator. The resulting simulator (which is made publicly available as part of the Munich Microfluidics Toolkit (MMFT)) eventually allows for the efficient simulation of channel-based microfluidic devices where droplet merging is an essential part.

Microfluidics is a technology that enables moving analytic processes from expensive and bulky laboratory equipment to small-scale devices. Microfluidic devices, usually in the form of labs-on-a-chips (LoCs), have found many great applications in medicine, biology, and chemistry. In particular, LoCs that utilize channels to transport fluids or droplets between different components on the chip are a promising technology. However, the design process of such channel-based LoCs is in need of further automation efforts since the underlying design steps are still rather complex and conducted mainly by hand. An important task in microfluidic design automation is the so-called channel routing, where components on LoCs are connected by microfluidic channels. Methods that aim to automate this routing task must factor in the specific demands of microfluidic devices. Common requirements for microfluidic routing layouts are to prevent sharp channel bends and to realize a particular length of channels. Unfortunately, most of the available routing algorithms address these requirements only partly and insufficiently. In this work, we propose a router that is able to overcome these shortcomings and allows automatic channel routing with a minimal bending radius as well as a desired length. In order to make the router accessible to users with little to no design automation expertise, the solution is implemented as an online tool with a user-friendly and intuitive interface. The resulting tool can be accessed athttps://www.cda.cit.tum.de/research/microfluidics/channel_router/.

Microfluidic devices, often in the form of Lab-on-a-Chip (LoCs), are successfully utilized in many domains such as medicine, chemistry, biology, etc. However, neither the fabrication process nor the respectively used materials are perfect and, thus, defects are frequently induced into the actual physical realization of the device. This is especially critical for sensitive devices such as droplet-based microfluidic networks that are able to route droplets inside channels along different paths by only exploiting passive hydrodynamic effects. However, these passive hydrodynamic effects are very sensitive and already slight changes of parameters (e.g., in the channel width) can alter the behavior, even in such a way that the intended functionality of the network breaks. Hence, it is important that microfluidic networks become robust against such defects in order to prevent erroneous behavior. But considering such defects during the design process is a non-trivial task and, therefore, designers mostly neglected such considerations thus far. To overcome this problem, we propose a robustness improvement process that allows to optimize an initial design in such a way that it becomes more robust against defects (while still retaining the original behavior of the initial design). To this end, we first utilize a metric to compare the robustness of different designs and, afterwards, discuss methods that aim to improve the robustness. The metric and methods are demonstrated by an example and also tested on several networks to show the validity of the robustness improvement process.

Microfluidics is an emerging field that allows to minimize, integrate, and automate processes that are usually conducted with unwieldy laboratory equipment inside a single device; resulting in so-called “Labs-on-a-Chip” (LoCs). The design process of channel-based LoCs is still mainly conducted manually thus far - resulting in time-consuming tasks and error-prone designs. This also holds for the routing process, where multiple components inside an LoC should be connected according to a specification. In this work, we present a routing tool which considers the particular requirements of microfluidic applications and automates the routing process. In order to make the tool more accessible (even to users with little to no EDA-expertise), it is incorporated into a user-friendly and intuitive online interface.

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.

Sample preparation refers to the task of generating fluids with a specified target concentration. Generally, this is achieved by performing a set of mixing operations between biochemical fluids with a given volumetric ratio. Sample preparation plays a crucial role in several medical applications. Microfluidic devices or labs-on-chips (LoCs) got established as a suitable solution to realize this task in a miniaturized, integrated, and automatic fashion. Over the years, a variety of different microfluidic platforms emerged, which all have their respective pros and cons. Accordingly, numerous approaches aiming at the sample preparation problem have been proposed—each specialized on a single platform only. More precisely, sample preparation methods introduced thus far provide solutions for a particular platform only, i.e., they are platform specific. In this work, we propose a generic approach that generalizes the constraints of the different microfluidic platforms and, by this, provides a platform-independent sample preparation method. This allows designers to quickly check what existing platform is most suitable for the considered task and to easily support upcoming and future microfluidic platforms as well. We evaluated the performance of the proposed method with a wide range of test cases and concluded (from the evaluations) that the proposed generic approach is capable of efficiently generating results for various platforms with a quality that is close to results from dedicated approaches presented thus far.

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.

Sample preparation plays a crucial role in several medical applications. Microfluidic devices or Labs-on-Chips (LoCs) got established as a suitable solution to realize this task in a miniaturized, integrated, and automatic fashion. Over the years,$a$variety of different microfluidic platforms emerged, which all have their respective pros and cons. Accordingly, numerous approaches for sample preparation have been proposed-each specialized on a single platform only. In this work, we propose an idea towards a generic sample preparation approach which will generalize the constraints of the different microfluidic platforms and, by this, will provide a platform-independent sample preparation method. This will allow designers to quickly check what existing platform is most suitable for the considered task and to easily support upcoming and future microfluidic platforms as well. We illustrate the applicability of the proposed method with examples for various platforms.

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.