Paul Ratul, Zhao Yuwen, Coster Declan, Qin Xiaochen, Islam Khayrul, Wu Yue, Liu Yaling
Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, 18015, USA.
Department of Bioengineering, Lehigh University, Bethlehem, PA, 18015, USA.
Nat Commun. 2023 Jul 27;14(1):4520. doi: 10.1038/s41467-023-40119-x.
Microfluidic devices have found extensive applications in mechanical, biomedical, chemical, and materials research. However, the high initial cost, low resolution, inferior feature fidelity, poor repeatability, rough surface finish, and long turn-around time of traditional prototyping methods limit their wider adoption. In this study, a strategic approach to a deterministic fabrication process based on in-situ image analysis and intermittent flow control called image-guided in-situ maskless lithography (IGIs-ML), has been proposed to overcome these challenges. By using dynamic image analysis and integrated flow control, IGIs-ML provides superior repeatability and fidelity of densely packed features across a large area and multiple devices. This general and robust approach enables the fabrication of a wide variety of microfluidic devices and resolves critical proximity effect and size limitations in rapid prototyping. The affordability and reliability of IGIs-ML make it a powerful tool for exploring the design space beyond the capabilities of traditional rapid prototyping.
微流控装置在机械、生物医学、化学和材料研究中有着广泛的应用。然而,传统原型制作方法的高初始成本、低分辨率、较差的特征保真度、低重复性、粗糙的表面光洁度以及较长的周转时间限制了它们的更广泛应用。在本研究中,为克服这些挑战,提出了一种基于原位图像分析和间歇流控制的确定性制造工艺的策略方法,称为图像引导原位无掩模光刻(IGIs-ML)。通过使用动态图像分析和集成流控制,IGIs-ML在大面积和多个装置上提供了密集排列特征的卓越重复性和保真度。这种通用且稳健的方法能够制造各种微流控装置,并解决了快速原型制作中的关键邻近效应和尺寸限制。IGIs-ML的可承受性和可靠性使其成为探索超越传统快速原型制作能力的设计空间的有力工具。