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.

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.

Microfluidic lab-on-chips offer promising technology for the automation of various biochemical laboratory protocols on a minuscule chip. Sample preparation (SP) is an essential part of any biochemical experiments, which aims to produce dilution of a sample or a mixture of multiple reagents in a certain ratio. One major objective in this area is to prepare dilutions of a given fluid with different concentration factors, each with certain volume, which is referred to as the demand-driven multiple-target (DDMT) generation problem. SP with microfluidic biochips requires proper sequencing of mix-split steps on fluid volumes and needs storage units to save intermediate fluids while producing the desired target ratio. The performance of SP depends on the underlying mixing algorithm and the availability of on-chip storage, and the latter is often limited by the constraints imposed during physical design. Since DDMT involves several target ratios, solving it under storage constraints becomes even harder. Furthermore, reduction of mix-split steps is desirable from the viewpoint of accuracy of SP, as every such step is a potential source of volumetric split error. In this article, we propose a storage-aware DDMT algorithm that reduces the number of mix-split operations on a digital microfluidic lab-on-chip. We also present the layout of the biochip with -storage cells and their allocation technique for . Simulation results reveal the superiority of the proposed method compared to the state-of-the-art multi-target SP algorithms.

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.

Digital microfluidic biochips have fueled a paradigm shift in implementing bench-top laboratory experiments on a single tiny chip, thus replacing costly and bulky equipment. However, because of imprecise fluidic functions, several volumetric split errors may occur during the execution of bioassays. Earlier approaches to error-correcting sample preparation addressed this problem by using a cyberphysical system yielding several drawbacks such as increased sample preparation cost and time, and uncertainty in assay completion time. In addition, error correction for only a single-target sample has been considered so far, although many assays require the production of multi-target samples. In this work, we present an error-free dilution technique that guarantees the correctness of the resulting concentration factor of a sample without performing any additional roll-back or roll-forward action. To the best of our knowledge, we are the first to present a solution strategy for tackling dispensing errors during sample preparation. We use micro-electrode-dot-array biochips that offer the advantages of manipulating fractional volumes of droplets (aliquots) for navigation, as well as mix-split operations. Instead of performing traditional mix-and-split steps with integral-volume droplets, we execute only an aliquoting-and-mix sequence using differential-size aliquots. Thus, all split operations, which are the main source of errors in conventional digital microfluidic biochips, are completely eliminated, and hence neither sensing nor any correcting action is needed, and further, no management of intermediate waste droplets is needed. Additionally, the procedure can be fully parallelized for accurately producing multiple dilutions of a sample. Experimental results corroborate the superiority of the proposed method in terms of error management, as well as sample preparation cost and time.

Microfluidic biochips enable low-cost automation of biochemical protocols with numerous applications to medical diagnostics, forensics, molecular biology, and drug design. An important component of protocol design is sample preparation, which involves dilution or mixing of two or more fluids in a desired ratio of concentration factors (CF). Existing continuous-flow microfluidic biochips deploy either free-flowing networks where only a single layer of flow-channels is used devoid of any control valves, or valve-based technology where the flow-layer is augmented with a control layer of valves. While the former is easy to fabricate, reliable, and less expensive, they are typically hardwired for specific applications only. The latter class, although programmable, is expensive and prone to various manufacturing and operational defects. In this paper, we present the physical design of a microfluidic network that is free-flowing as well as programmable. The proposed valve-free network resembles a complete binary tree with serpentine obstacles embedded within its channels, and can be used to achieve a desired dilution of a sample just by proper selection of fluid concentrations to be fed as inputs under constant pressure. Simulation with COMSOL Multiphysics Software shows that the proposed network provides a powerful and versatile architecture for solution preparation with minimal control, outperforming prior approaches in terms of the accuracy of CFs and time for convergence.

It is expected that as digital microfluidic biochips (DMFBs) mature, the hardware design flow will begin to resemble the current practice in the semiconductor industry: design teams send chip layouts to third-party foundries for fabrication. These foundries are untrusted and threaten to steal valuable intellectual property (IP). In a DMFB, the IP consists of not only hardware layouts but also of the biochemical assays (bioassays) that are intended to be executed on-chip. DMFB designers therefore must defend these protocols against theft. We propose to “lock” biochemical assays by inserting dummy mix-split operations. We experimentally evaluate the proposed locking mechanism, and show how a high level of protection can be achieved even on bioassays with low complexity. We also demonstrate a new class of attacks that exploit the side-channel information to launch sophisticated attacks on the locked bioassay.

Sample preparation is a fundamental preprocessing step needed in almost all biochemical assays and is conveniently automated on a microfluidic lab-on-chip. In digital microfluidics, it is accomplished by a sequence of droplet-mix-split steps on a biochip. Many real-life applications require a sample with multiple concentration factors (CFs). Existing algorithms, while producing multi-CF targets, attempt to share the mix-split steps in order to reduce reactant-cost and sample-preparation time. However, all prior approaches have two limitations: 1) sharing of intermediate droplets can be best effected only when all required target CFs are known a priori and 2) the processing time may vary depending on the allowable error-tolerance in target-CFs. In this paper, we present a cost-effective solution to multi-CF-dilution on-demand, by using only one (or two) mix-split step(s). In order to service dynamically arriving requests of multiple CFs quickly, we prepare dilutions of the sample with a few CFs in advance (called source-CFs), and fill on-chip reservoirs with these fluids. For minimizing the number of such preprocessed CFs, we present an integer linear programming-based method, an approximation algorithm, and a heuristic algorithm. The proposed methods also allow the users to tradeoff the number of on-chip reservoirs against service time for various applications. Simulation results for several target sets demonstrate the superiority of the proposed techniques over prior art in terms of the number of mix-split steps, waste droplets, and reactant usage when the on-chip reservoirs are preloaded with source-CFs using a customized droplet-streaming engine.

Microfluidic chips are now being increasingly used for fast and cost-effective implementation of biochemical protocols. Sample preparation involves dilution and mixing of fluids in certain ratios, which are needed for most of the protocols. On a digital microfluidic biochip (DMFB), these tasks are usually automated as a sequence of droplet mix-split steps. In the most widely used (1:1) mix-split operation for DMFBs, two equal-volume droplets are mixed followed by a split operation, which, ideally, should produce two daughter-droplets of equal volume (balanced splitting). However, because of uncertain variabilities in fluidic operations, the outcome of droplet-split operations often becomes erroneous, i.e., they may cause unbalanced splitting. As a result, the concentration factor (CF) of each constituent fluid in the mixture may become erroneous during sample preparation. All traditional approaches aimed to recover from such errors deploy on-chip sensors to detect possible volumetric imbalance, and adopt either checkpointing-based rollback or roll-forward techniques. Most of them suffer from significant overhead in terms of assay-completion time, reactant-cost, and uncertainties in termination due to randomly occurring split-errors. In this paper, we propose a new approach to accurate dilution preparation on a DMFB that is oblivious to volumetric split-errors. It does not need any sensor and can handle multiple split-errors, deterministically. The proposed method is customized for each target-CF based on the criticality of split-errors in each mix-split step. Simulation experiments on various test-cases demonstrate the effectiveness of the proposed method.

Albeit sample preparation is well-studied for digital microfluidic biochips, very few prior work addressed this problem in the context of continuous-flow microfluidics from an algorithmic perspective. In the latter class of chips, microvalves and micropumps are used to manipulate on-chip fluid flow through microchannels in order to execute a biochemical protocol. Dilution of a sample fluid is a special case of sample preparation, where only two input reagents (commonly known as sample and buffer) are mixed in a desired volumetric ratio. In this paper, we propose a satisfiability-based dilution algorithm assuming the generalized mixing models supported by an N-segment, continuous-flow, rotary mixer. Given a target concentration and an error limit, the proposed algorithm first minimizes the number of mixing operations, and subsequently, reduces reagent-usage. Simulation results demonstrate that the proposed method outperforms existing dilution algorithms in terms of mixing steps (assay time) and waste production, and compares favorably with respect to reagent-usage (cost) when 4- and 8-segment rotary mixers are used. Next, we propose two variants of an algorithm for handling the open problem of k-reagent mixture-preparation (k ≥ 3) with an N-segment continuous-flow rotary mixer, and report experimental results to evaluate their performance. A software tool called flow-based sample preparation algorithm has also been developed that can be readily used for running the proposed algorithms.

Digital (droplet-based) microfluidic technology offers an attractive platform for implementing a wide variety of biochemical laboratory protocols, such as point-of-care diagnosis, DNA analysis, target detection, and drug discovery. A digital microfluidic biochip consists of a patterned array of electrodes on which tiny fluid droplets are manipulated by electrical actuation sequences to perform various fluidic operations, for example, dispense, transport, mix, or split. However, because of the inherent uncertainty of fluidic operations, the outcome of biochemical experiments performed on-chip can be erroneous even if the chip is tested a priori and deemed to be defect-free. In this article, we address an important error recoverability problem in the context of sample preparation. We assume a cyberphysical environment, in which the physical errors, when detected online at selected checkpoints with integrated sensors, can be corrected through recovery techniques. However, almost all prior work on error recoverability used checkpointing-based rollback approach, that is, re-execution of certain portions of the protocol starting from the previous checkpoint. Unfortunately, such techniques are expensive both in terms of assay completion time and reagent cost, and can never ensure full error-recovery in deterministic sense. We consider imprecise droplet mix-split operations and present a novel roll-forward approach where the erroneous droplets, thus produced, are used in the error-recovery process, instead of being discarded or remixed. All erroneous droplets participate in the dilution process and they mutually cancel or reduce the concentration-error when the target droplet is reached. We also present a rigorous analysis that reveals the role of volumetric-error on the concentration of a sample to be prepared, and we describe the layout of a lab-on-chip that can execute the proposed cyberphysical dilution algorithm. Our analysis reveals that fluidic errors caused by unbalanced droplet splitting can be classified as being either critical or non-critical , and only those of the former type require correction to achieve error-free sample dilution. Simulation experiments on various sample preparation test cases demonstrate the effectiveness of the proposed method.