Precise reagent mixing in user-defined ratios is a fundamental requirement in many microfluidic applications, including diagnostics, chemical synthesis, and biological assays. However, existing solutions for ratio-specific mixing often rely on complex active components, such as multiple pressure sources, flow controllers, or on-chip valves, making them costly, bulky, and unsuitable for portable or low-resource settings. In this work, we present a mixer design and a synthesis method for generating 3D-printable microfluidic devices that achieve ratiospecific mixing using only a single constant pressure source. Our method decomposes the desired mixing ratio into additive subcomponents, each represented by a dedicated inlet channel with a tailored length to enforce the correct hydraulic resistance. The method outputs a complete microfluidic layout, ready for direct fabrication via 3D printers. We validate our approach through numerical simulations and physical prototyping across eight diverse mixing scenarios. Results show that the achieved mixing ratios closely resemble the target, demonstrating the method’s accuracy and robustness. This work enables low-cost, portable, and accessible microfluidic devices for ratio-specific solution delivery, broadening the scope of microfluidics in settings where simplicity, reproducibility, and affordability are critical.

Microfluidic biochips play an important role in point-of-care diagnosis. Advanced microfluidic biochips can efficiently perform various fluidic operations and robustly execute bioassays like protein synthesis, drug discovery, and many others. Micro-electrode-dot-array (MEDA) digital microfluidic biochips are one of the promising new generation microfluidic biochips consisting of an array of micro-electrodes with dedicated sensors on each electrode. Its ability to manipulate discrete droplets of different volumes and route them in any direction makes it an advanced microfluidic technology. However, the working principle of MEDA and conventional digital microfluidic biochips is based on the same phenomenon called electro-wetting-on-dielectric. In spite of all the advantages, MEDA still suffers from electrode malfunctioning due to dielectric breakdown. This kind of electrode malfunction happens primarily due to repeated actuation or prolonged actuation. Therefore, it is important to develop new bioassay synthesis methods that ensure reliability and guarantee error-free operations on MEDA. Since repeated and prolonged electrode actuations primarily occur within the modules, the reliability of MEDA biochips can be ensured with an appropriate placement strategy. As fluid routing is an inherent phase to complete the synthesis of any bioassay, we also propose a simple collision avoidance routing algorithm for MEDA. In this article, we propose a two-phase synthesis technique for MEDA biochips to improve the reliability of the biochips. First, a reinforcement learning-based placement method ( RLPM ) is designed for obtaining the reliability-aware placement of rectilinear-shaped microfluidic modules. Then, we propose a heuristic-based approach called collision avoidance MEDA routing ( CAMR ) that determines the collision-free routes for on-chip transportation of droplets of different volumes. RLPM utilizes the power of reinforcement learning and aims to minimize the utilization area of the chip while increasing its reliability. Whereas, RLPM together with CAMR minimizes the total completion time of the bioassay. Simulation results justify that the proposed synthesis technique can reduce the total chip area utilization by 28.6% (on average) without compromising the chip reliability compared to the state-of-the-art techniques.

Real-time execution of bioassays on microelectrode dot array (MEDA) biochips can revolutionize point-of-care diagnostics. Despite numerous design automation efforts for digital microfluidic biochips (DMFBs), there is no online synthesis framework for their advanced counterpart, MEDA biochips, which have various operational advantages. The DMFB-specific virtual topology [Grissom et al., TCAD, 2014] is inadequate for MEDA synthesis as it suffers from significant chip-area fragmentation issues. In this article, we propose a virtual topology that provides an abstraction of the physical geometry of MEDA biochips, thereby reducing the algorithmic runtime for MEDA synthesis by reducing the search space for optimization problems. On top of this, we propose an A rea-efficient O nline P lacer ( AOP ) to achieve placement results with minimum chip-area. However, due to the overuse of some microelectrodes, they may be degraded, which may result in unreliable outcomes of the bioassays. P lacement R eliability E nhancer ( PRE ) is proposed to improve the reliability of MEDA chips. Finally, a F ast O nline S ynthesis ( FOS ) is proposed for MEDA based on the virtual topology and these two methods, which always outperform the existing online synthesis in the average assay completion time and the number of routing sub-problems. Simulations confirm that AOP significantly reduces both occupied area and algorithmic runtime compared with state-of-the-art WLSM and RLPM , albeit at the expense of reliability. By integrating PRE , AOP+PRE achieves reliability similar to WLSM and RLPM , but requires additional chip area compared with AOP while maintaining similar runtime efficiency. Hence, AOP+PRE enables efficient and reliable online synthesis for MEDA biochips.

Sample preparation, where a certain number of reagents must be mixed in a specific volumetric ratio, is an integral step for various bio-assays. A programmable microfluidic device (PMD) is an advanced flow-based microfluidic biochip (FMB) platform, that considered to be very effective for sample preparation. However, the impact of mixer placement, reagents' distribution, and mixing time on the automation of sample preparation has not yet been investigated. We consider a mixing efficiency model controlled by the number of alternations “μ” of reagents along the mixing circulation path and propose a loading-aware placement strategy that maximizes the mixing efficiency. We use satisfiability modulo theories (SMT) and propose a one-pass strategy for placing the mixers and the reagents, that successfully enhance the loading and mixing efficiencies.

promising new generation microfluidic biochips consisting of a sea-of-micro-electrodes with dedicated detection circuit for each microelectrode. Moreover, the ability to manipulate discrete droplets of different volumes and to route them in any direction presents MEDA biochips as an advanced microfluidic technology. Due to similarity in the working principles, the reliability issues of both MEDA biochips and digital microfluidic biochips are similar. In this paper, we propose a module placement technique for MEDA biochips to improve the reliability of biochips. Reinforcement learning based placement method (RLPM) is designed for obtaining the reliability-aware placement of rectilinear shaped microfluidic modules. RLPM aims to minimize the area of a biochip while increasing its reliability. Simulation results confirm that on average RLPM minimizes the chip utilization area by 28.6% while enhancing the reliability of MEDA biochips compared to the state-of-the-art method.

Sample preparation is an essential step in almost all bioprotocols, which can be efficiently achieved via a sequence of mixing steps called mixing graph. In the literature, several techniques have been reported to determine a mixing graph with the minimal number of mixing steps, the minimal usage of reagent fluids, the minimal wastage, or sometimes a combination of them. The retention time of a flow-based microfluidic biochip (FMB) is defined as the maximum duration for which a fluid can be stored within a microchannel without any fluid leakage. However, the retention time has not yet been considered as a scheduling constraint during the automation of the sample preparation using an FMB in order to obtain the scheduled mixing graphs. In this article, we propose a retention time-aware scheduling method called time-aware list scheduling (TALS), which can be used with the state-of-the-art methods, and a new satisfiability-based mixing algorithm called time-aware sample preparation (TASP) to obtain the scheduled mixing graph for a target ratio satisfying the retention time constraint and the number of available on-chip mixers in an FMB. Simulation results suggest that on an average TALS always outperforms a baseline scheduling method while scheduling any mixing graph, whereas TASP can determine the optimal and scheduled mixing graphs compared to the existing mixing methods combined with TALS.

Sample preparation is an inherent procedure of many biochemical applications, and digital microfluidic biochips (DMBs) have proved to be very effective in performing such a procedure. In a single mixing step, conventional DMBs can mix two droplets in a 1:1 ratio only. Due to this limitation, DMBs suffer from heavy fluid wastage and often require a lot of mixing steps. However, the next-generation DMBs, i.e., micro-electrode-dot-array (MEDA) biochips, can realize multiple mixing ratios, which in general helps in minimizing the number of mixing operations. In this article, we present a heuristic-based sample preparation algorithm, specifically a mixing algorithm calledDivision by Factor Method for MEDAthat exploits the mixing models of MEDA biochips. We propose another mixing algorithm for MEDA biochips calledSingle Target Waste Minimization(STWM), which minimizes the wastage of fluids and determines an efficient mixing graph. We also propose an advanced methodology for multiple target reagent mixing problems calledMulti-target Waste Minimization(MTWM), which determines efficient mixing graphs for different target ratios by maximizing the sharing of fluids and minimizing the fluid wastage. Simulation results suggest that the proposedSTWMandMTWMmethods outperform the state-of-the-art methods in terms of minimizing the amount of fluid wastage, reducing the total usage of reagent fluids, and minimizing the number of mixing operations.

Sample preparation is an inherent procedure of many biochemical applications, and digital microfluidic biochips (DMBs) proved to be very effective in performing such a procedure. In a single mixing step, conventional DMBs can mix two droplets in 1:1 ratio only. Due to this limitation, DMBs suffer from heavy fluid wastage and large number of mixing steps. However, the next generation DMBs, i.e., micro-electrode-dot-array (MEDA) biochips can realize multiple mixing ratios and are able to overcome a lot of those limitations. In this paper, we present a heuristic-based sample preparation algorithm, specifically a mixing algorithm called Division by Factor Method for Mixing that exploits the mixing models of MEDA biochips. We propose another mixing algorithm for MEDA biochips called Single Target Waste Minimization (STWM), which minimizes the wastage of fluids and determines an optimized mixing graph. Simulation results confirm that the proposed STWM method outperforms the state-of-the-art method in terms of minimizing the number of waste fluids, reducing the total reagent usage, and minimizing the number of mixing operations.

Microfluidic biochips have recently emerged with significant promise and versatility in automating a variety of biochemical protocols on a tiny chip. Sample preparation, which involves the mixing of fluids with a specified target ratio in the minuscule scale, is an essential component of these protocols. Algorithms that optimize on-chip sample-preparation cost and time are closely intertwined with the underlying mixing model, mixing sequence, and fluidic architecture. Although numerous mixing models have been studied in the literature, their impact on the dynamics of mixing steps is hitherto not fully understood. In this article, we show that various mixing models can be envisaged in the light of prime factorization of integers thus establishing a connection among mixing algorithms, chip architectures, and performance. This insight has led to the development of the proposed factorization-based dilution algorithm (FacDA) considering a generalized mixing model suitable for micro-electrode-dot-array (MEDA) biochips. It further leads to target volume oriented dilution algorithm (TVODA) to cater to user’s demand for an output with a given volume. We formulate the optimization problem on the fabric of the satisfiability modulo theory (SMT) while determining mixing sequences. Simulation results on a large number of test-cases reveal thatFacDAandTVODAoutperform the state-of-the-art dilution algorithms for MEDA biochips with respect to reactant cost, mixing time, and waste production.

Recently, Programmable Microfluidic Device (PMD) has got an attention of the design automation communities as a new type of microfluidic biochips. For the design of PMD chips, one of the important tasks is to minimize the number of flows for loading the reactant fluids into specific cells (by creating some flows of the fluids) before the bio-protocol is executed. Nevertheless of the importance of the problem, there has been almost no work to study this problem. Thus, in this paper, we intensively study this fluid loading problem in PMD chips. First, we successfully formulate the problem as a constraint satisfaction problem (CSP) to solve the problem optimally for the first time. Then, we also propose an efficient heuristic called Determining Flows from the Last (DFL) method for larger problem instances. DFL is based on a novel idea that it is better to determine the flows from the last flow unlike the state-of-the-art method Fluid Loading Algorithm for PMD (FLAP) [Gupta et al., TODAES, 2019]. Simulation results confirm that the exact method can find the optimal solutions for practical test cases, whereas our heuristic can find near-optimal solutions, which are better than those obtained by FLAP.

Microfluidic fully programmable valve array (FPVA) biochips have emerged as general-purpose flow-based microfluidic lab-on-chips (LoCs). An FPVA supports highly re-configurable on-chip components (modules) in the two-dimensional grid-like structure controlled by some software programs, unlike application-specific flow-based LoCs. Fluids can be loaded into or washed from a cell with the help of flows from the inlet to outlet of an FPVA, whereas cell-to-cell transportation of discrete fluid segment(s) is not precisely possible. The simplest mixing module to realize on an FPVA-based LoC is a four-way mixer consisting of a 2 × 2 array of cells working as a ring-like mixer having four valves. In this paper, we propose a design automation method for sample preparation that finds suitable placements of mixing operations of a mixing tree using four-way mixers without requiring any transportation of fluid(s) between modules. We also propose a heuristic that modifies the mixing tree to reduce the sample preparation time. We have performed an extensive simulation and examined several parameters to determine the performance of the proposed solution.

Sample preparation, an essential preprocessing step for biochemical protocols, is concerned with the generation of fluids satisfying specific target ratios and error-tolerance. Recent micro-electrode-dot-array (MEDA)-based DMF biochips provide the advantage of supporting both discrete and dynamic mixing models, the power of which has not yet been fully harnessed for implementing on-chip dilution and mixing of fluids. In this paper, we propose a novel factorization-based algorithm called FacDA for efficient and accurate dilution of sample fluid on a MEDA chip. Simulation results reveal that over a large number of test-cases with the mixing volume constraint in the range of 4--10 units, FacDA requires around 38% fewer mixing steps, 52% less sample units, and generates approximately 23% less wastage, all on average, compared to two prior dilution algorithms used for MEDA chips.