Many micro-organisms find themselves immersed in fluids displaying non-Newtonian rheological properties such as viscoelasticity and shear-thinning viscosity. The effects of viscoelasticity on swimming at low Reynolds numbers have already received considerable attention, but much less is known about swimming in shear-thinning fluids. A general understanding of the fundamental question of how shear-thinning rheology influences swimming still remains elusive. To probe this question further, we study a spherical squirmer in a shear-thinning fluid using a combination of asymptotic analysis and numerical simulations. Shear-thinning rheology is found to affect a squirming swimmer in non-trivial and surprising ways; we predict and show instances of both faster and slower swimming depending on the surface actuation of the squirmer. We also illustrate that while a drag and thrust decomposition can provide insights into swimming in Newtonian fluids, extending this intuition to problems in complex media can prove problematic.

Artificial microswimmers offer exciting opportunities for biomedical applications. The goal of these synthetics is to swim like natural micro‐organisms through biological environments and perform complex tasks such as drug delivery and microsurgery. Extensive efforts in the past several decades have focused on generating propulsion at the microscopic scale, which has engendered a variety of artificial microswimmers based on different physical and physicochemical mechanisms. Yet, these major advancements represent only the initial steps toward successful biomedical applications of microswimmers in realistic scenarios. A next step is to design “smart” microswimmers that can adapt their locomotion behaviors in response to environmental factors. Herein, recent progress in the development of microswimmers with intelligent behaviors is surveyed in three major areas: 1) adaptive locomotion across different media, 2) tactic behaviors in response to environment stimuli, and 3) multifunctional swimmers that can perform complex tasks. The emerging technologies and novel approaches used in developing these “smart” microswimmers, which enable them to display behaviors similar to biological cells, are discussed.

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.

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.

Micro-organisms encounter heterogeneous viscous environments consisting of networks of obstacles embedded in a viscous fluid medium. In this paper we analyse the characteristics of swimming in a porous medium modelled by the Brinkman equation via a spherical squirmer model. The idealized geometry allows an analytical and exact solution of the flow surrounding a squirmer. The propulsion speed obtained agrees with previous results using the Lorentz reciprocal theorem. Our analysis extends these results to calculate the power dissipation and hence the swimming efficiency of the squirmer in a Brinkman medium. The analytical solution enables a systematic analysis of the structure of the flow surrounding the squirmer, which can be represented in terms of singularities in Brinkman flows. We also discuss the spatial decay of flows due to squirming motion in a Brinkman medium in comparison with the decay in a purely viscous fluid. The results lay the foundation for subsequent studies on hydrodynamic interactions, nutrient transport and uptake by micro-organisms in heterogeneous viscous environments.

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.