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Swimming at the microscale has recently garnered substantial attention due to the fundamental biological significance of swimming microorganisms and the wide range of biomedical applications for artificial microswimmers. These microswimmers invariably find themselves surrounded by different confining boundaries, which can impact their locomotion in significant and diverse ways. In this work, we employ a widely used three-sphere swimmer model to investigate the effect of confinement on swimming at low Reynolds numbers. We conduct theoretical analysis via the point-particle approximation and numerical simulations based on the finite element method to examine the motion of the swimmer along the centerline in a capillary tube. The axisymmetric configuration reduces the motion to one-dimensional movement, which allows us to quantify how the degree of confinement affects the propulsion speed in a simple manner. Our results show that the confinement does not significantly affect the propulsion speed until the ratio of the radius of the tube to the radius of the sphere is in the range of O(1)−O(10), where the swimmer undergoes substantial reduction in its propulsion speed as the radius of the tube decreases. We provide some physical insights into how reduced hydrodynamic interactions between moving spheres under confinement may hinder the propulsion of the three-sphere swimmer. We also remark that the reduced propulsion performance stands in stark contrast to the enhanced helical propulsion observed in a capillary tube, highlighting how the manifestation of confinement effects can vary qualitatively depending on the propulsion mechanisms employed by the swimmers. 

Abstract Microinjection is an essential process in genetic engineering that is used to deliver genetic materials into various biological specimens. Considering the high-throughput requirement for microinjection applications ranging from gene editing to cell therapies, there is a need for an automated, highly parallelized, reproducible, and easy-to-use microinjection strategy. Here we report an on-chip, microfluidic microinjection module designed for compatibility with microfluidic large-scale integration technology that can be fabricated via standard, multilayer soft lithography techniques. The needle-on-chip (NOC) module consists of a two-layer polydimethylsiloxane-based microfluidic module whose puncture and injection operations are reliant solely on Quake valve actuation. As a proof-of-concept, we designed a NOC module to conduct the microinjection of a common genetics model organism, Caenorhabditis elegans ( C. elegans ). The NOC design was analyzed using finite element method simulations for a large range of practically viable geometrical parameters. The computational results suggested that a slight lateral offset (>10 μ m) of the control channel is sufficient for a successful NOC operation with a large fabrication tolerance (50 μ m, 50% channel width). To demonstrate proof-of-concept, the microinjection platform was fabricated and utilized to perform a successful injection of a tracer dye into C. elegans . 

Biological and artificial microswimmers often encounter fluid media with non-Newtonian rheological properties. In particular, many biological fluids such as blood and mucus are shear-thinning. Recent studies have demonstrated how shear-thinning rheology can impact substantially the propulsion performance in different manners. In this work, we examine the effect of geometrical shape upon locomotion in a shear-thinning fluid using a prolate spheroidal squirmer model. We use a combination of asymptotic analysis and numerical simulations to quantify how particle geometry impacts the speed and the energetic cost of swimming. The results demonstrate the advantages of spheroidal over spherical swimmers in terms of both swimming speed and energetic efficiency when squirming through a shear-thinning fluid. More generally, the findings suggest the possibility of tuning the swimmer geometry to better exploit non-Newtonian rheological behaviours for more effective locomotion in complex fluids. 

Particle–wall interactions have broad biological and technological applications. In particular, some artificial microswimmers capitalize on their translation–rotation coupling near a wall to generate directed propulsion. Emerging biomedical applications of these microswimmers in complex biological fluids prompt questions on the impact of non-Newtonian rheology on their propulsion. In this work, we report some intriguing effects of shear-thinning rheology, a ubiquitous non-Newtonian behaviour of biological fluids, on the translation–rotation coupling of a particle near a wall. One particularly interesting feature revealed here is that the wall-induced translation by rotation can occur in a direction opposite to what might be intuitively expected for an object rolling on a solid substrate. We elucidate the underlying physical mechanism and discuss its implications on the design of micromachines and bacterial motion near walls in complex fluids. 

Reinforcement learning control methods can impart robots with the ability to discover effective behavior, reducing their modeling and sensing requirements, and enabling their ability to adapt to environmental changes. However, it remains challenging for a robot to achieve navigation in confined and dynamic environments, which are characteristic of a broad range of biomedical applications, such as endoscopy with ingestible electronics. Herein, a compact, 3D‐printed three‐linked‐sphere robot synergistically integrated with a reinforcement learning algorithm that can perform adaptable, autonomous crawling in a confined channel is demonstrated. The scalable robot consists of three equally sized spheres that are linearly coupled, in which the extension and contraction in specific sequences dictate its navigation. The ability to achieve bidirectional locomotion across frictional surfaces in open and confined spaces without prior knowledge of the environment is also demonstrated. The synergistic integration of a highly scalable robotic apparatus and the model‐free reinforcement learning control strategy can enable autonomous navigation in a broad range of dynamic and confined environments. This capability can enable sensing, imaging, and surgical processes in previously inaccessible confined environments in the human body.