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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 Exposure of cell membranes to reactive oxygen species can cause oxidation of membrane lipids. Oxidized lipids undergo drastic conformational changes, compromising the mechanical integrity of the membrane and causing cell death. For giant unilamellar vesicles, a classic cell mimetic system, a range of mechanical responses under oxidative assault has been observed including formation of nanopores, transient micron‐sized pores, and total sudden catastrophic collapse (i.e., explosion). However, the physical mechanism regarding how lipid oxidation causes vesicles to explode remains elusive. Here, with light‐induced asymmetric oxidation experiments, the role of spontaneous curvature on vesicle instability and its link to the conformational changes of oxidized lipid products is systematically investigated. A comprehensive membrane model is proposed for pore‐opening dynamics incorporating spontaneous curvature and membrane curling, which captures the experimental observations well. The kinetics of lipid oxidation are further characterized and how light‐induced asymmetric oxidation generates spontaneous curvature in a non‐monotonic temporal manner is rationalized. Using the framework, a phase diagram with an analytical criterion to predict transient pore formation or catastrophic vesicle collapse is provided. The work can shed light on understanding biomembrane stability under oxidative assault and strategizing release dynamics of vesicle‐based drug delivery systems. 

Abstract Automated manipulation of small particles using external (e.g., magnetic, electric and acoustic) fields has been an emerging technique widely used in different areas. The manipulation typically necessitates a reduced‐order physical model characterizing the field‐driven motion of particles in a complex environment. Such models are available only for highly idealized settings but are absent for a general scenario of particle manipulation typically involving complex nonlinear processes, which has limited its application. In this work, the authors present a data‐driven architecture for controlling particles in microfluidics based on hydrodynamic manipulation. The architecture replaces the difficult‐to‐derive model by a generally trainable artificial neural network to describe the kinematics of particles, and subsequently identifies the optimal operations to manipulate particles. The authors successfully demonstrate a diverse set of particle manipulations in a numerically emulated microfluidic chamber, including targeted assembly of particles and subsequent navigation of the assembled cluster, simultaneous path planning for multiple particles, and steering one particle through obstacles. The approach achieves both spatial and temporal controllability of high precision for these settings. This achievement revolutionizes automated particle manipulation, showing the potential of data‐driven approaches and machine learning in improving microfluidic technologies for enhanced flexibility and intelligence. 

The integration of an ingestible dosage form with sensing, actuation, and drug delivery capabilities can enable a broad range of surgical‐free diagnostic and treatment strategies. However, the gastrointestinal (GI) tract is a highly constrained and complex luminal construct that fundamentally limits the size of an ingestible system. Recent advancements in mesoscale magnetic crawlers have demonstrated the ability to effectively traverse complex and confined systems by leveraging magnetic fields to induce contraction and bending‐based locomotion. However, the integration of functional components (e.g., electronics) in the proposed ingestible system remains fundamentally challenging. Herein, the creation of a centralized compartment in a magnetic robot by imparting localized flexibility (MR‐LF) is demonstrated. The centralized compartment enables MR‐LF to be readily integrated with modular functional components and payloads, such as commercial off‐the‐shelf electronics and medication, while preserving its bidirectionality in an ingestible form factor. The ability of MR‐LF to incorporate electronics, perform drug delivery, guide continuum devices such as catheters, and navigate air–water environments in confined lumens is demonstrated. The MR‐LF enables functional integration to create a highly integrated ingestible system that can ultimately address a broad range of unmet clinical needs. An interactive preprint version of the article can be found athttps://doi.org/10.22541/au.166274072.23086985/v1. 

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.