流体自组装毛细管力和结合位点的建模

K. Böhringer, U. Srinivasan, Roger T. Howe
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引用次数: 93

摘要

大规模平行自组装正在成为一种高效、低成本的替代方法,可以替代传统的微加工组件的拾取和放置组装。我们开发的流体自组装技术利用亲疏水表面图案和结合位点之间黏附液体的毛细力来驱动组装过程。为了获得高对准良率,所需的装配配置必须是(全局)能量最小值,而应避免与不希望的配置相对应的其他(局部)能量最小值。因此,使用这种技术设计一个有效的流体自组装系统需要理解毛细管力所涉及的界面现象;其性能的提高涉及到结合位点形状和表面化学等设计参数的全局优化。本文提出了一种有效分析和模拟流体自组装的模型和计算工具。控制我们的流体自组装技术的强、近距离引力由纯几何模型近似,这允许应用有效的算法来预测系统行为。分析了不同结合位点的设计,并与实验结果进行了比较。对于给定的结合位点设计,该模型通过确定最小能量配置和检测不需要的局部最小值来预测自组装过程的结果,从而估计期望产量。这些结果可用于设计更高效的自组装系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling of capillary forces and binding sites for fluidic self-assembly
Massively parallel self-assembly is emerging as an efficient, low-cost alternative to conventional pick-and-place assembly of microfabricated components. The fluidic self-assembly technique we have developed exploits hydrophobic-hydrophilic surface patterning and capillary forces of an adhesive liquid between binding sites to drive the assembly process. To achieve high alignment yield, the desired assembly configuration must be a (global) energy minimum, while other (local) energy minima corresponding to undesired configurations should be avoided. Thus, the design of an effective fluidic self-assembly system using this technique requires an understanding of the interfacial phenomena involved in capillary forces; improvement of its performance involves the global optimization of design parameters such as binding site shapes and surface chemistry. This paper presents a model and computational tools for the efficient analysis and simulation of fluidic self-assembly. The strong, close range attractive forces that govern our fluidic self-assembly technique are approximated by a purely geometric model, which allows the application of efficient algorithms to predict system behavior. Various binding site designs are analyzed, and the results are compared with experimental observations. For a given binding site design, the model predicts the outcome of the self assembly process by determining minimum energy configurations and detecting unwanted local minima, thus estimating expected yield. These results can be employed toward the design of more efficient self-assembly systems.
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