Large-Scale Selective Micropatterning with Robotics nDEP Tweezers and Hydrogel Encapsulation
Kaicheng Huang, Jiewen Lai, Hongliang Ren, Chunhui Wu, Xing Cheng, Henry K. Chu
- Year
- 2024
- Citations
- 3
Abstract
Creating diverse microparticle patterns on a large scale enhances the performance and efficiency of biochemical analytics. Current techniques exhibit limitations in achieving diverse microparticle patterns on a large scale, primarily focusing on patterning particles of the same type with limited flexibility and accessibility. Moreover, accessibility to patterned particles without a fixed formation poses additional challenges. Herein, in this work, we introduce a novel robotic micropatterning system designed to address these challenges. The system facilitates the selection, batch transferring, patterning, and encapsulation of microparticles using negative dielectrophoresis (nDEP)-tweezers, enabling large-scale microparticle patterning on a hydrogel. A multielectrode chip was mounted on a micromanipulator to serve as the nDEP tweezers, and the microparticles scattering on the substrate could be trapped and displaced to different positions on a substrate with an array of holes for large-scale pattern generation. Photosensitive hydrogel was employed for microparticle pattern encapsulation. The effects of configuring different experimental parameters on the patterning efficiency were evaluated and analyzed. Experiments were conducted to explore the stability and performance of the micropatterns. Various patterns with hydrogel encapsulation were created using different color polystyrene microbeads (orange, blue, and green) with varying sizes (50, 100, and 125 μm) under the adjusted environment. Results demonstrate the successful creation of large-scale microbead patterns in a specified form and their encapsulation into an extractable hydrogel using the proposed nDEP tweezer system. The proposed system can be potentially applied to diverse bioparticles for analysis.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002