Optoelectrowetting technology generates virtual electrodes to manipulate droplets by projecting optical patterns onto the photoconductive layer. This method avoids the complex design of the physical circuitry of dielectricwetting chips, compensating for the inability to reconstruct the electrode. However, the current technology relies on operators to manually position the droplets, draw optical patterns, and preset the droplet movement paths. It lacks real-time feedback on droplet information and the ability for independent droplet control, which can lead to droplet miscontrol and contamination. This paper presents a combination of optoelectrowetting with deep learning algorithms, integrating software and a photoelectric detection platform, and develops an optoelectrowetting intelligent control system. First, a target detection algorithm identifies droplet characteristics in real-time and automatically generate virtual electrodes to control movement. Simultaneously, a tracking algorithm outputs trajectories and ID information for efficient droplet arrays tracking. The results show that the system can automatically control the movement and fusion of multiple droplets in parallel and realize the automatic arrangement and storage of disordered droplet arrays without any additional electrodes and sensing devices. Additionally, through the automated control of the system, the cell suspension can be precisely cultured in the specified medium according to experimental requirements, and the growth trend is consistent with that observed in the well plate, significantly enhancing the experiment's flexibility and accuracy. In this paper, we propose an intelligent method applicable to the automated manipulation of discrete droplets. This method would play a crucial role in advancing the applications of digital microfluidic technology in biomedicine and other fields.

中文翻译：

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智能光电润湿数字微流控系统，用于生物液滴阵列的实时选择性并行操作。


光电润湿技术通过将光学图案投射到光电导层上来产生虚拟电极以操纵液滴。这种方法避免了介电润湿芯片物理电路的复杂设计，弥补了无法重建电极的问题。然而，当前的技术依赖于操作员手动定位液滴、绘制光学图案和预设液滴移动路径。它缺乏对液滴信息的实时反馈和独立的液滴控制能力，这可能导致液滴失控和污染。本文提出了光电润湿与深度学习算法的结合，集成软件和光电检测平台，开发了光电润湿智能控制系统。首先，目标检测算法实时识别液滴特征并自动生成虚拟电极来控制运动。同时，跟踪算法输出轨迹和 ID 信息，以实现高效的液滴阵列跟踪。结果表明，该系统可以自动控制多个液滴并联的运动和融合，实现无序液滴阵列的自动排列和存储，而无需任何额外的电极和传感装置。此外，通过系统的自动化控制，可以根据实验要求在指定培养基中精确培养细胞悬液，并且生长趋势与在孔板中观察到的生长趋势一致，显着提高了实验的灵活性和准确性。在本文中，我们提出了一种适用于离散液滴自动操作的智能方法。 该方法将在推进数字微流控技术在生物医学等领域的应用方面发挥关键作用。