Inertial microfluidics uses the intrinsic fluid inertia in confined channels to manipulate the particles and cells in a simple, high-throughput, and precise manner. Inertial focusing in a straight channel results in several equilibrium positions within the cross sections. Introducing channel curvature and adjusting the cross-sectional aspect ratio and shape can modify inertial focusing positions and can reduce the number of equilibrium positions. In this work, we introduce an innovative way to adjust the inertial focusing and reduce equilibrium positions by embedding asymmetrical obstacle microstructures. We demonstrated that asymmetrical concave obstacles could break the symmetry of original inertial focusing positions, resulting in unilateral focusing. In addition, we characterized the influence of obstacle size and 3 asymmetrical obstacle patterns on unilateral inertial focusing. Finally, we applied differential unilateral focusing on the separation of 10- and 15-μm particles and isolation of brain cancer cells (U87MG) from white blood cells (WBCs), respectively. The results indicated an excellent cancer cell recovery of 96.4% and WBC rejection ratio of 98.81%. After single processing, the purity of the cancer cells was dramatically enhanced from 1.01% to 90.13%, with an 89.24-fold enrichment. We believe that embedding asymmetric concave micro-obstacles is a new strategy to achieve unilateral inertial focusing and separation in curved channels.
Node localization is the basic task of underwater wireless sensor networks (UWSNs). Most of the existing underwater localization methods rely on ranging accuracy. Due to the special environment conditions in the ocean, beacon nodes are difficult to deploy accurately. The narrow bandwidth and high delay of the underwater acoustic communication channel lead to large errors. In order to reduce the ranging error and improve the positioning accuracy, we propose a localization algorithm based on ranging correction and inertial coordination. The algorithm can be divided into two parts, Range Correction based Localization algorithm (RCL) and Inertial Coordination based Localization algorithm (ICL). RCL uses the geometric relationship between the node positions to correct the ranging error and obtain the exact node position. However, when the unknown node deviates from the deployment area with the movement of the water flow, it cannot communicate with enough beacon nodes in a certain period of time. In this case, the node uses ICL algorithm to combine position data with motion information of neighbor nodes to update its position. The simulation results show that the proposed algorithm greatly improves the positioning accuracy of unknown nodes compared with the existing localization methods.
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