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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. 

Micro- and nanorobots excel in navigating the intricate and often inaccessible areas of the human body, offering immense potential for applications such as disease diagnosis, precision drug delivery, detoxification, and minimally invasive surgery. Despite their promise, practical deployment faces hurdles, including achieving stable propulsion in complex in vivo biological environments, real-time imaging and localization through deep tissue, and precise remote control for targeted therapy and ensuring high therapeutic efficacy. To overcome these obstacles, we introduce a hydrogel-based, imaging-guided, bioresorbable acoustic microrobot (BAM) designed to navigate the human body with high stability. Constructed using two-photon polymerization, a BAM comprises magnetic nanoparticles and therapeutic agents integrated into its hydrogel matrix for precision control and drug delivery. The microrobot features an optimized surface chemistry with a hydrophobic inner layer to substantially enhance microbubble retention in biofluids with multiday functionality and a hydrophilic outer layer to minimize aggregation and promote timely degradation. The dual-opening bubble-trapping cavity design enables a BAM to maintain consistent and efficient acoustic propulsion across a range of biological fluids. Under focused ultrasound stimulation, the entrapped microbubbles oscillate and enhance the contrast for real-time ultrasound imaging, facilitating precise tracking and control of BAM movement through wireless magnetic navigation. Moreover, the hydrolysis-driven biodegradability of BAMs ensures its safe dissolution after treatment, posing no risk of long-term residual harm. Thorough in vitro and in vivo experimental evidence demonstrates the promising capabilities of BAMs in biomedical applications. This approach shows promise for advancing minimally invasive medical interventions and targeted therapeutic delivery. 

Shear-thinning viscosity is a non-Newtonian behaviour that active particles often encounter in biological fluids such as blood and mucus. The fundamental question of how this ubiquitous non-Newtonian rheology affects the propulsion of active particles has attracted substantial interest. In particular, spherical Janus particles driven by self-diffusiophoresis, a major physico-chemical propulsion mechanism of synthetic active particles, were shown to always swim slower in a shear-thinning fluid than in a Newtonian fluid. In this work, we move beyond the spherical limit to examine the effect of particle eccentricity on self-diffusiophoretic propulsion in a shear-thinning fluid. We use a combination of asymptotic analysis and numerical simulations to show that shear-thinning rheology can enhance self-diffusiophoretic propulsion of a spheroidal particle, in stark contrast to previous findings for the spherical case. A systematic characterization of the dependence of the propulsion speed on the particle's active surface coverage has also uncovered an intriguing feature associated with the propulsion speeds of a pair of complementarily coated particles not previously reported. Symmetry arguments are presented to elucidate how this new feature emerges as a combined effect of anisotropy of the spheroidal geometry and nonlinearity in fluid rheology. 

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. 

The use of machine learning techniques in the development of microscopic swimmers has drawn considerable attention in recent years. In particular, reinforcement learning has been shown useful in enabling swimmers to learn effective propulsion strategies through its interactions with the surroundings. In this work, we apply a reinforcement learning approach to identify swimming gaits of a multi-link model swimmer. The swimmer consists of multiple rigid links connected serially with hinges, which can rotate freely to change the relative angles between neighboring links. Purcell [“Life at low Reynolds number,” Am. J. Phys. 45, 3 (1977)] demonstrated how the particular case of a three-link swimmer (now known as Purcell's swimmer) can perform a prescribed sequence of hinge rotation to generate self-propulsion in the absence of inertia. Here, without relying on any prior knowledge of low-Reynolds-number locomotion, we first demonstrate the use of reinforcement learning in identifying the classical swimming gaits of Purcell's swimmer for case of three links. We next examine the new swimming gaits acquired by the learning process as the number of links increases. We also consider the scenarios when only a single hinge is allowed to rotate at a time and when simultaneous rotation of multiple hinges is allowed. We contrast the difference in the locomotory gaits learned by the swimmers in these scenarios and discuss their propulsion performance. Taken together, our results demonstrate how a simple reinforcement learning technique can be applied to identify both classical and new swimming gaits at low Reynolds numbers. 

Abstract Swimming microorganisms switch between locomotory gaits to enable complex navigation strategies such as run-and-tumble to explore their environments and search for specific targets. This ability of targeted navigation via adaptive gait-switching is particularly desirable for the development of smart artificial microswimmers that can perform complex biomedical tasks such as targeted drug delivery and microsurgery in an autonomous manner. Here we use a deep reinforcement learning approach to enable a model microswimmer to self-learn effective locomotory gaits for translation, rotation and combined motions. The Artificial Intelligence (AI) powered swimmer can switch between various locomotory gaits adaptively to navigate towards target locations. The multimodal navigation strategy is reminiscent of gait-switching behaviors adopted by swimming microorganisms. We show that the strategy advised by AI is robust to flow perturbations and versatile in enabling the swimmer to perform complex tasks such as path tracing without being explicitly programmed. Taken together, our results demonstrate the vast potential of these AI-powered swimmers for applications in unpredictable, complex fluid environments. 

The effects of viscoelasticity have been shown to manifest themselves via symmetry breaking. In this investigation, we show a novel phenomenon that arises from this idea. We observe that when a dense sphere is rotated near a wall (the rotation being aligned with the wall-normal direction and gravity), it levitates to a fixed distance away from the wall. Since the shear is larger in the gap (between the sphere and the wall) than in the open side of the sphere, the shear-induced elastic stresses are thus asymmetric, resulting in a net elastic vertical force that balances the weight of the sphere. We conduct experiments, theoretical models and numerical simulations for rotating spheres of various sizes and densities in a Boger-type fluid. In the small-Deborah-number range, the results are collapsed into a universal trend by considering a dimensionless group of the ratio of elastic to gravitational forces.