A pattern-control digital biochip is proposed for fast medical tests. With integrated circuit modules in each basic element, also known as micro-electrode, this biochip can achieve digital microfluidic operations, capacitive sensing, and thermal cycle via different control patterns. As a result, bio-protocols can be derived from target biomedical tests to reach better test accuracy on the proposed chip. For the mentioned fast medical tests, samples/reagents can be identified first by capacitive sensing, followed by microfluidic and thermal cycle operations. Preliminary measurements show that heating/cooling rate of 5°C/sec can be achieved and demonstrate each thermal cycle$(95\rightarrow 55\rightarrow 72)$for polymerase chain reaction (PCR) can be completed in less than 20 seconds with power consumption of 256–444 uW per micro-electrode while dealing with nano-liter samples. This implies both test time and power consumption per sample test can be further improved, making our proposed biochip very suitable for point-of-care test (POCT) applications.

Microfluidic biochips are being utilized for clinical diagnostics, including COVID-19 testing, because of they provide sample-to-result turnaround at low cost. Recently, microelectrode-dot-array (MEDA) biochips have been proposed to advance microfluidics technology. A MEDA biochip manipulates droplets of nano/picoliter volumes to automatically execute biochemical protocols. During bioassay execution, droplets are transported in parallel to achieve high-throughput outcomes. However, a major concern associated with the use of MEDA biochips is microelectrode degradation over time. Recent work has shown that formulating droplet transportation as a reinforcement-learning (RL) problem enables the training of policies to capture the underlying health conditions of microelectrodes and ensure reliable fluidic operations. However, the above RL-based approach suffers from two key limitations: 1) it cannot be used for concurrent transportation of multiple droplets; 2) it requires the availability of CCD cameras for monitoring droplet movement. To overcome these problems, we present a multi-agent reinforcement learning (MARL) droplet-routing solution that can be used for various sizes of MEDA biochips with integrated sensors, and we demonstrate the reliable execution of a serial-dilution bioassay with the MARL droplet router on a fabricated MEDA biochip. To facilitate further research, we also present a simulation environment based on the PettingZoo Gym Interface for MARL-guided droplet-routing problems on MEDA biochips.

Sample preparation, as a key procedure in many biochemical protocols, mixes various samples, and/or reagents into solutions that contain the target concentrations. Digital microfluidic biochips (DMFBs) have been adopted as a platform for sample preparation because they provide automatic procedures that require less reactant consumption and reduce human-induced errors. However, the most existing methods only consider two-reactant sample preparation, and they cannot be used for many biochemical applications that involve multiple reactants. In addition, the existing methods that can be used for multiple-reactant sample preparation were proposed on traditional DMFBs where only the (1:1) mixing model is available. In the (1:1) mixing model, only two droplets of the same volume can be mixed at a time, which results in higher completion time and the wastage of valuable reactants. To overcome this limitation, the micro-electrode-dot-array (MEDA) architecture has been introduced; it provides the flexibility of mixing multiple droplets of different volumes in a single operation. In this article, we present a generic multiple-reactant sample preparation algorithm that exploits the novel fluidic operations on MEDA biochips. We also propose an enhanced algorithm that increases the operation-sharing opportunities when multiple target concentrations are needed, and therefore the usage of reactants can be further reduced. The simulated experiments show that the proposed method outperforms existing methods in terms of saving reactant cost, minimizing the number of operations, and reducing the amount of waste.

Sample preparation, as a key procedure in many biochemical protocols, mixes various samples and/or reagents into solutions that contain the target concentrations. Digital microfluidic biochips (DMFBs) have been adopted as a platform for sample preparation because they provide automatic procedures that require less reactant consumption and reduce human-induced errors. However, traditional DMFBs only utilize the (1:1) mixing model, i.e., only two droplets of the same volume can be mixed at a time, which results in higher completion time and the wastage of valuable reactants. To overcome this limitation, a next-generation micro-electrode-dot-array (MEDA) architecture that provides flexibility of mixing multiple droplets of different volumes in a single operation was proposed. In this paper, we present a generic multiple-reactant sample preparation algorithm that exploits the novel fluidic operations on MEDA biochips. Simulated experiments show that the proposed method outperforms existing methods in terms of saving reactant cost, minimizing the number of operations, and reducing the amount of waste.

This paper presents a new approach with frequency selection for fast medical tests. Many medical tests need high speed sensor because of its short reaction time. High frequency biosensor with high sensitivity plays a vital role in fast medical tests. Each medical test has its own reaction time. How to select proper frequency for specific medical test is an important issue. In this paper, one frequency-based capacitive sensor is proposed to prove this new approach with salt water. According to the experimental results, the limit of detection (LOD) can be improved if proper working frequency is selected. The 0.1%wt salt water in 20ul can be detected on proposed system and coefficient of variation (CV) is only 0.19% at 40MHz.