Electrophoresis-Based Adaptive Manipulation of Nanowires in Fluid Suspension

Abstract

Automated, highly precise online manipulation of nanoscale and microscale objects is essential to achieve scalable nanomanufacturing. However, nanoparticles and microparticles exhibit as yet unpredictable and, therefore, uncontrolled variations in their structures or compositions that can limit their functions and properties. In this article, we present an electric field-based adaptive manipulation scheme to precisely control the motion of individual nanowires, online estimate their unknown mobilities, and rapidly plan the desired trajectories with the estimated mobilities. The input saturation is considered in the adaptive controller, and the closed-loop system is proved to be asymptotically stable. The proposed motion planners (MPs), ${{\tt Bi}}$ - ${{\tt iSST}}$ s, are built on and extended by the stable sparse rapidly exploring random tree ( ${{\tt SST}}$ )-based kinodynamic motion-planning algorithms. Two sparse trees are maintained to increase the probability of finding a solution. The ${{\tt Bi}}$ - ${{\tt iSST}}$ algorithms use the workspace information, heuristics, and optimization to effectively guide the search process. When compared with the state-of-the-art algorithms, a ${{\tt Bi}}$ - ${{\tt iSST}}$ variant, the ${{\tt opt}}$ - ${{\tt Bi}}$ - ${{\tt iSST}}$ algorithm, quickly updates feasible solutions with the online estimated mobilities of multiple nanowires and converges to a near-optimal, minimum-time solution to increase the efficiency of simultaneous manipulation of the nanowires. Therefore, without complex characterization of each nanowire’s mobility, the nanowires can be steered simultaneously and efficiently to achieve precisely controlled positions without collisions. Simulation and experimental results confirm that the proposed integrated adaptive manipulation scheme precisely, independently, and simultaneously manipulates the motion of multiple nanowires.