GNN-Based Concentration Prediction With Variable Input Flow Rates for Microfluidic Mixers.

Recent years have witnessed significant advances brought by microfluidic biochips in automating biochemical protocols. Accurate preparation of fluid samples is an essential component of these protocols, where concentration prediction and generation are critical. Equipped with the advantages of convenient fabrication and control, microfluidic mixers demonstrate huge potential in sample preparation. Although finite element analysis (FEA) is the most commonly used simulation method for accurate concentration prediction of a given microfluidic mixer, it is time-consuming with poor scalability for large biochip sizes. Recently, machine learning models have been adopted in concentration prediction, with great potential in enhancing the efficiency over traditional FEA methods. However, the state-of-the-art machine learning-based method can only predict the concentration of mixers with fixed input flow rates and fixed sizes. In this paper, we propose a new concentration prediction method based on graph neural networks (GNNs), which can predict output concentrations for microfluidic mixters with variable input flow rates. Moreover, a transfer learning method is proposed to transfer the trained model to mixers of different sizes with reduced training data. Experimental results show that, for microfluidic mixers with fixed input flow rates, the proposed method obtains an average reduction of 88% in terms of prediction errors compared with the state-of-the-art method. For microfluidic mixers with variable input flow rates, the proposed method reduces the prediction error by 85% on average. Besides, the proposed transfer learning method reduces the training data by 84% for extending the pre-trained model for microfluidic mixers of different sizes with acceptable prediction error.

ColoriSens: An open-source and low-cost portable color sensor board for microfluidic integration with wireless communication and fluorescence detection.

Microfluidic colorimetric biosensors have shown promising potential for detecting metal cations, anions, organic dyes, drugs, pesticides. As for today, most colorimetric sensors are read by a smartphone or professional optical imaging system, and there is still a lack of an affordable and reliable colorimetric detector for the microfluidic chip. Integrating those reading and detection capabilities into a microfluidic system is essential for point-of-care (POC) detection and can enable more complex microfluidic operations, such as lab-on-a-chip experiments or programmable microfluidics. We developed an open-source colorimetric detection sensor board that can be integrated into the existing microfluidic system. This sensor board has a built-in UV source that enables fluorescence detection. With built-in USB and Wi-Fi connectivity and a set of simple APIs, microfluidic systems can communicate directly with this sensor board, even wirelessly. The sensor was designed for low-cost. With a total build cost of less than 12 EUR per unit, it is ideal for low-cost systems and DIY microfluidic users. Along with the sensor board, we also designed a companion microfluidic chip carrier cartridge which can be modified depending on the chip's dimension. To demonstrate the sensor, we also developed a cross-platform open-source client application to demonstrate the communication APIs and the functionality of the sensor board.

Portable all-in-one automated microfluidic system (PAMICON) with 3D-printed chip using novel fluid control mechanism.

State-of-the-art microfluidic systems rely on relatively expensive and bulky off-chip infrastructures. The core of a system-the microfluidic chip-requires a clean room and dedicated skills to be fabricated. Thus, state-of-the-art microfluidic systems are barely accessible, especially for the do-it-yourself (DIY) community or enthusiasts. Recent emerging technology-3D-printing-has shown promise to fabricate microfluidic chips more simply, but the resulting chip is mainly hardened and single-layered and can hardly replace the state-of-the-art Polydimethylsiloxane (PDMS) chip. There exists no convenient fluidic control mechanism yet suitable for the hardened single-layered chip, and particularly, the hardened single-layered chip cannot replicate the pneumatic valve-an essential actuator for automatically controlled microfluidics. Instead, 3D-printable non-pneumatic or manually actuated valve designs are reported, but their application is limited. Here, we present a low-cost accessible all-in-one portable microfluidic system, which uses an easy-to-print single-layered 3D-printed microfluidic chip along with a novel active control mechanism for fluids to enable more applications. This active control mechanism is based on air or gas interception and can, e.g., block, direct, and transport fluid. As a demonstration, we show the system can automatically control the fluid in microfluidic chips, which we designed and printed with a consumer-grade 3D-printer. The system is comparably compact and can automatically perform user-programmed experiments. All operations can be done directly on the system with no additional host device required. This work could support the spread of low budget accessible microfluidic systems as portable, usable on-the-go devices and increase the application field of 3D-printed microfluidic devices.

Pressure-Aware Control Layer Optimization for Flow-Based Microfluidic Biochips.

Flow-based microfluidic biochips are attracting increasing attention with successful biomedical applications. One critical issue with flow-based microfluidic biochips is the large number of microvalves that require peripheral control pins. Even using the broadcasting addressing scheme, i.e., one control pin controls multiple microvalves simultaneously, thousands of microvalves would still require hundreds of control prins, which is unrealistic. To address this critical challenge in control scalability, the control-layer multiplexer is introduced to effectively reduce the number of control pins into log scale of the number of microvalves. There are two practical design issues with the control-layer multiplexer: (1) the reliability issue caused by the frequent control-valve switching, and (2) the pressure degradation problem caused by the control-valve switching without pressure refreshing from the pressure source. This paper addresses these two design issues by the proposed Hamming-distance-based switching sequence optimization method and the XOR-based pressure refreshing method. Simulation results demonstrate the effectiveness and efficiency of the proposed methods with an average 77.2% (maximum 89.6%) improvement in total pressure refreshing cost, and an average 88.5% (maximum 90.0%) improvement in pressure deviation.