Collective Behaviors of Active Matter Learning from Natural Taxes Across Scales

Artificial Intelligence-Assisted Multimode Microrobot Swarm Behaviors

Mimicking the swarm behaviors in nature, the microswarm has shown dynamic transformations and flexible assemblies in complex physiological environments, garnering increasing attention for its potential medical applications. However, because of the complexity of swarm behaviors and the corresponding influencing factors, achieving controllability, stability, and diversity of an artificial microswarm remains challenging. Here, a physically assisted artificial intelligence analysis framework was employed to predict the multimode swarm behaviors of a magnetic microswarm. By modulating 12 different parameters of a programmable magnetic field, we obtained various swarm patterns, including liquid, rod, network, ribbon, flocculence, and vortex. A physical model was developed to simulate the programmable 3D magnetic field and the corresponding collective behaviors. Explainable artificial intelligence analysis uncovered the relationship between control parameters and magnetic swarm patterns, achieving a prediction accuracy of 83.87% for pattern classification. Our stability analysis revealed that rod and vortex patterns exhibited higher stability, making them ideal for precise manipulation tasks. Leveraging this framework, we demonstrated environmentally adaptive swarm navigation through complex channels and swarm hunting of specific targets. This study could not only advance the understanding of microswarm control but also provide a strategy for targeted delivery and micromanipulation in potential clinical applications.

Biased Lévy Walk Enables Light Gradient Sensing in Euglena gracilis

We examine the navigation behavior of the photosensitive alga Euglena gracilis in confined environments. Under uniform lighting conditions, E. gracilis exhibits stochastic movements with nearly straight runs interrupted by abrupt directional changes. The lengths of these runs follow a long-tailed distribution typical of a Lévy walk, with scaling exponents that vary with light intensity. In gradient lighting conditions, the cells modulate their run durations-extending them upon detecting an increase in light intensity and shortening them when a decrease is detected. This adjustment effectively biases the Lévy walk, enabling the cells to ascend the spatial light gradient. This behavior mirrors well-known prokaryotic stochastic navigation strategies, such as bacterial chemotaxis, offering a eukaryotic parallel. The experimental observations under varied lighting conditions are consistently replicated through an agent-based model.

Metareview: a survey of active matter reviews

In the past years, the amount of research on active matter has grown extremely rapidly, a fact that is reflected in particular by the existence of more than 1000 reviews on this topic. Moreover, the field has become very diverse, ranging from theoretical studies of the statistical mechanics of active particles to applied work on medical applications of microrobots and from biological systems to artificial swimmers. This makes it very difficult to get an overview over the field as a whole. Here, we provide such an overview in the form of a metareview article that surveys the existing review articles and books on active matter. Thereby, this article provides a useful starting point for finding literature about a specific topic.

Non-Reciprocity, Metastability, and Dynamic Reconfiguration in Co-Assembly of Active and Passive Particles

Living organisms often exhibit non-reciprocal interactions where the forces acting on the objects are not equal in magnitude or opposite in direction. The combination of reciprocal and non-reciprocal interactions between synthetic building blocks remains largely unexplored. Here, out-of-equilibrium assemblies of non-motile isotropic passive and metal-patched motile active particles are formed by overlapping bulk interactions with directed self-propulsion. An external alternating current (AC) electric field generates concurrent dipolar and induced-charge electrophoretic forces between the particles which are evaluated using microscopy. The interaction force measurements allow to determine the degree of reciprocity in interactions, which is tunable by designing the active particle and its trajectory. While linearly-propelled active particles evade assembly with passive particles, helically propelled active particles form active-passive clusters with dynamic reconfiguration and long-lived metastability. Large clusters display programmable fluctuations and reconfigurability by controlling the fraction of active particles. The study establishes principles of integrating reciprocal and non-reciprocal interactions in guided colloidal assembly of reconfigurable metastable structures.

Swarm Autonomy: From Agent Functionalization to Machine Intelligence

Swarm behaviors are common in nature, where individual organisms collaborate via perception, communication, and adaptation. Emulating these dynamics, large groups of active agents can self-organize through localized interactions, giving rise to complex swarm behaviors, which exhibit potential for applications across various domains. This review presents a comprehensive summary and perspective of synthetic swarms, to bridge the gap between the microscale individual agents and potential applications of synthetic swarms. It is begun by examining active agents, the fundamental units of synthetic swarms, to understand the origins of their motility and functionality in the presence of external stimuli. Then inter-agent communications and agent-environment communications that contribute to the swarm generation are summarized. Furthermore, the swarm behaviors reported to date and the emergence of machine intelligence within these behaviors are reviewed. Eventually, the applications enabled by distinct synthetic swarms are summarized. By discussing the emergent machine intelligence in swarm behaviors, insights are offered into the design and deployment of autonomous synthetic swarms for real-world applications.

Miniature Robots for Battling Bacterial Infection

Micro/nanorobots have shown great promise for minimally invasive bacterial infection therapy. However, bacterial infections usually form biofilms inside the body by aggregation and adhesion, preventing antibiotic penetration and increasing the likelihood of recurrence. Moreover, a substantial portion of the infection happens in those hard-to-access regions, making delivery of antibiotics to infected sites or tissues difficult and exacerbating the challenge of addressing bacterial infections. Micro/nanorobots feature exceptional mobility and controllability, are able to deliver drugs to specific sites (targeted delivery), and enhance drug penetration. In particular, the emergence of bioinspired microrobot surface design strategies have provided effective alternatives for treating infections, thereby preventing the possible development of bacterial resistance. In this paper, we review the recent advances in design, mechanism, and actuation modalities of micro/nanorobots with exceptional antimicrobial features, highlighting active therapy strategies for bacterial infections and derived complications at various organs, from the laboratory bench to in vivo applications. The current challenges and future research directions in this field are summarized. Those breakthroughs in micro/nanorobots offer a huge potential for clinical translation for bacterial infection therapy.

Advanced materials for micro/nanorobotics

Autonomous micro/nanorobots capable of performing programmed missions are at the forefront of next-generation micromachinery. These small robotic systems are predominantly constructed using functional components sourced from micro- and nanoscale materials; therefore, combining them with various advanced materials represents a pivotal direction toward achieving a higher level of intelligence and multifunctionality. This review provides a comprehensive overview of advanced materials for innovative micro/nanorobotics, focusing on the five families of materials that have witnessed the most rapid advancements over the last decade: two-dimensional materials, metal-organic frameworks, semiconductors, polymers, and biological cells. Their unique physicochemical, mechanical, optical, and biological properties have been integrated into micro/nanorobots to achieve greater maneuverability, programmability, intelligence, and multifunctionality in collective behaviors. The design and fabrication methods for hybrid robotic systems are discussed based on the material categories. In addition, their promising potential for powering motion and/or (multi-)functionality is described and the fundamental principles underlying them are explained. Finally, their extensive use in a variety of applications, including environmental remediation, (bio)sensing, therapeutics, etc., and remaining challenges and perspectives for future research are discussed.

Accelerating and breaking adaptive nano-colloids (<100 nm) into unsteady state operation via push-pull effects

Unlike conventional colloids showing random mobility because of Brownian motion, active colloids contain nanomotors that translate chemical or physical triggers into directed movement. Whereas the acceleration of such particles works well, it is difficult to decelerate them by request. Compared to the existing literature on microscaled swimmers/robots, the main question of the current paper is whether nanoscaled colloids (<100 nm) can also be actively controlled despite the stronger relevance of rotational diffusion at such dimensions. We developed nanoparticles comprising two independent mechanisms for propulsion: a chemical engine associated with a Janus-type modification of organosilica nanoparticles and physical locomotion because of a superparamagnetic core inside these particles. Both triggers can be used independently to initiate the particles' directed and anisotropic movement. The magnetic forces can be tuned, most importantly concerning the angle defining the chemical acceleration. Superposition and a boost state are adopted for a parallel alignment. However, when the magnetic field acting on the particles is turned to an antiparallel orientation, a rapid deceleration can be observed, and the colloids halt.

Potential field mechanisms and distributed learning for enhancing the navigation of micro/nanorobot in biomedical environments

In complex systems, single micro/nanorobots encounter challenges related to limited loading capacity and navigation, hindering their effective utilization in targeted therapy and drug delivery. To solve these challenges, this paper explores potential field mechanisms as a means to simulate natural collective behavior. This approach aims to enhance the navigation and efficiency of micro/nanorobots in high-demand therapeutic areas. The mechanism enables micro/nanorobots to dynamically adapt to environmental gradients, minimizing off-target effects while maximizing therapeutic efficacy and enhancing robustness through redundancy. Additionally, this study introduces innovative distributed learning and cooperative control strategies. Each micro/nanorobot updates its navigation strategy through local interactions and influences with the dynamic environment. This allows micro/nanorobots to share information and improve their navigation toward therapeutic targets. The simulation results demonstrate that collective behavior and potential field mechanisms can enhance the precision and efficiency of targeted therapy and drug delivery in dynamically changing environments. In conclusion, the proposed approach can improve the limitations of single micro/nanobot, offering new possibilities for the development of advanced therapeutics and drug delivery systems.

Learning interpretable dynamics of stochastic complex systems from experimental data

Complex systems with many interacting nodes are inherently stochastic and best described by stochastic differential equations. Despite increasing observation data, inferring these equations from empirical data remains challenging. Here, we propose the Langevin graph network approach to learn the hidden stochastic differential equations of complex networked systems, outperforming five state-of-the-art methods. We apply our approach to two real systems: bird flock movement and tau pathology diffusion in brains. The inferred equation for bird flocks closely resembles the second-order Vicsek model, providing unprecedented evidence that the Vicsek model captures genuine flocking dynamics. Moreover, our approach uncovers the governing equation for the spread of abnormal tau proteins in mouse brains, enabling early prediction of tau occupation in each brain region and revealing distinct pathology dynamics in mutant mice. By learning interpretable stochastic dynamics of complex systems, our findings open new avenues for downstream applications such as control.

Insect-Scale Biped Robots Based on Asymmetrical Friction Effect Induced by Magnetic Torque

Multimodal and controllable locomotion in complex terrain is of great importance for practical applications of insect-scale robots. Robust locomotion plays a particularly critical role. In this study, a locomotion mechanism for magnetic robots based on asymmetrical friction effect induced by magnetic torque is revealed and defined. The defined mechanism overcomes the design constraints imposed by both robot and substrate structures, enabling the realization of multimodal locomotion on complex terrains. Drawing inspiration from human walking and running locomotion, a biped robot based on the mechanism is proposed, which not only exhibits rapid locomotion across substrates with varying friction coefficients but also achieves precise locomotion along patterned trajectories through programmed controlling. Furthermore, apart from its exceptional locomotive capabilities, the biped robot demonstrates remarkable robustness in terms of load-carrying and weight-bearing performance. The presented locomotion and mechanism herein introduce a novel concept for designing magnetic robots while offering extensive possibilities for practical applications in insect-scale robotics.

Methamphetamine Removal from Aquatic Environments by Magnetic Microrobots with Cyclodextrin Chiral Recognition Elements

The growing consumption of drugs of abuse together with the inefficiency of the current wastewater treatment plants toward their presence has resulted in an emergent class of pollutants. Thus, the development of alternative approaches to remediate this environmental threat is urgently needed. Microrobots, combining autonomous motion with great tunability for the development of specific tasks, have turned into promising candidates to take on the challenge. Here, hybrid urchin-like hematite (α-Fe2O3) microparticles carrying magnetite (Fe3O4) nanoparticles and surface functionalization with organic β-cyclodextrin (CD) molecules are prepared with the aim of on-the-fly encapsulation of illicit drugs into the linked CD cavities of moving microrobots. The resulting mag-CD microrobots are tested against methamphetamine (MA), proving their ability for the removal of this psychoactive substance. A dramatically enhanced capture of MA from water with active magnetically powered microrobots when compared with static passive CD-modified particles is demonstrated. This work shows the advantages of enhanced mass transfer provided by the externally controlled magnetic navigation in microrobots that together with the versatility of their design is an efficient strategy to clean polluted waters.

Magnetic Microrobot Swarms with Polymeric Hands Catching Bacteria and Microplastics in Water

The forefront of micro- and nanorobot research involves the development of smart swimming micromachines emulating the complexity of natural systems, such as the swarming and collective behaviors typically observed in animals and microorganisms, for efficient task execution. This study introduces magnetically controlled microrobots that possess polymeric sequestrant "hands" decorating a magnetic core. Under the influence of external magnetic fields, the functionalized magnetic beads dynamically self-assemble from individual microparticles into well-defined rotating planes of diverse dimensions, allowing modulation of their propulsion speed, and exhibiting a collective motion. These mobile microrobotic swarms can actively capture free-swimming bacteria and dispersed microplastics "on-the-fly", thereby cleaning aquatic environments. Unlike conventional methods, these microrobots can be collected from the complex media and can release the captured contaminants in a second vessel in a controllable manner, that is, using ultrasound, offering a sustainable solution for repeated use in decontamination processes. Additionally, the residual water is subjected to UV irradiation to eliminate any remaining bacteria, providing a comprehensive cleaning solution. In summary, this study shows a swarming microrobot design for water decontamination processes.

Weak Ion-Exchange Based Magnetic Swarm for Targeted Drug Delivery and Chemotherapy

Swimming microrobots that are actuated by multiple stimuli/fields display various intriguing collective behaviors, ranging from phase separation to clustering and giant number fluctuation; however, it is still chanllenging to achieve multiple responses and functionalities within one colloidal system to emulate high environmental adaptability and improved tasking capability of natural swarms. In this work, a weak ion-exchange based swarm is presented that can self-organize and reconfigure by chemical, light, and magnetic fields, showing living crystal, amorphous glass, liquid, chain, and wheel-like structures. By changing the frequency and strength of the rotating magnetic field, various well-controlled and fast transformations are obtained. Experiments show the high adaptability and functionality of the microrobot swarm in delivering drugs in confined spaces, such as narrow channels with turns or obstacles. The drug-carrying swarm exhibits excellent chemtherapy for Hela and CT26 cells due to the pH-enhanced drug release and locomotion. This reconfigurable microswarm provides a new platform for biomedical and environmental applications.

Persistent and responsive collective motion with adaptive time delay

It is beneficial for collective structures to simultaneously have high persistence to environmental noise and high responsivity to nontrivial external stimuli. However, without the ability to differentiate useful information from noise, there is always a tradeoff between persistence and responsivity within the collective structures. To address this, we propose adaptive time delay inspired by the adaptive behavior observed in the school of fish. This strategy is tested using particles powered by optothermal fields coupled with an optical feedback-control system. By applying the adaptive time delay with a proper threshold, we experimentally observe the responsivity of the collective structures enhanced by approximately 1.6 times without sacrificing persistence. Furthermore, we integrate adaptive time delay with long-distance transportation and obstacle-avoidance capabilities to prototype adaptive swarm microrobots. This research demonstrates the potential of adaptive time delay to address the persistence-responsivity tradeoff and lays the foundation for intelligent swarm micro/nanorobots operating in complex environments.

Collective Molecular Machines: Multidimensionality and Reconfigurability

Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work. During the last 60 years, designing molecular structures capable of generating unidirectional mechanical motion at the nanoscale has been the topic of intense research. Effective progress has been made, attributed to advances in various fields such as supramolecular chemistry, biology and nanotechnology, and informatics. However, individual molecular machines are only capable of producing nanometer work and generally have only a single functionality. In order to address these problems, collective behaviors realized by integrating several or more of these individual mechanical units in space and time have become a new paradigm. In this review, we comprehensively discuss recent developments in the collective behaviors of molecular machines. In particular, collective behavior is divided into two paradigms. One is the appropriate integration of molecular machines to efficiently amplify molecular motions and deformations to construct novel functional materials. The other is the construction of swarming modes at the supramolecular level to perform nanoscale or microscale operations. We discuss design strategies for both modes and focus on the modulation of features and properties. Subsequently, in order to address existing challenges, the idea of transferring experience gained in the field of micro/nano robotics is presented, offering prospects for future developments in the collective behavior of molecular machines.

Chemical multiscale robotics for bacterial biofilm treatment

A biofilm constitutes a bacterial community encased in a sticky matrix of extracellular polymeric substances. These intricate microbial communities adhere to various host surfaces such as hard and soft tissues as well as indwelling medical devices. These microbial aggregates form a robust matrix of extracellular polymeric substances (EPSs), leading to the majority of human infections. Such infections tend to exhibit high resistance to treatment, often progressing into chronic states. The matrix of EPS protects bacteria from a hostile environment and prevents the penetration of antibacterial agents. Modern robots at nano, micro, and millimeter scales are highly attractive candidates for biomedical applications due to their diverse functionalities, such as navigating in confined spaces and targeted multitasking. In this tutorial review, we describe key milestones in the strategies developed for the removal and eradication of biofilms using robots of different sizes and shapes. It can be seen that robots at different scales are useful and effective tools for treating bacterial biofilms, thus preventing persistent infections, the loss of costly implanted medical devices, and additional costs associated with hospitalization and therapies.

Reconfigurable Assembly of Planar Colloidal Molecules via Chemical Reaction and Electric Polarization

Colloidal molecules, ordered structures assembled from micro- and nanoparticles, serve as a valuable model for understanding the behavior of real molecules and for constructing materials with tunable properties. In this work, we introduce a universal strategy for assembling colloidal molecules consisting of a central active particle surrounded by several passive particles as ligands. During the assembly process, active particles attract the surrounding passive particles through phoresis and osmosis resulting from the chemical reactions on the surface of the active particles, while passive particles repel each other due to the electric polarization induced by an alternating current (AC) electric field. By carefully selecting particles of varying structures and sizes, we have assembled colloidal molecules of symmetric and asymmetric dimers, trimers, and multimers. Furthermore, the coordination number of these colloidal molecules can be regulated in real time and in situ by tuning the interaction forces between the constituent particles. Brownian dynamics simulations reproduced the formation of the colloidal molecules and validated that the self-assembly arises from chemically induced attraction and electrical dipolar repulsion. This strategy for reconfigurable colloidal assemblies poses the potential for designing adaptive micro-nanomachines.

Open Questions of Chemically Powered Nano- and Micromotors

Chemically powered nano- and micromotors are microscopic devices that convert chemical energy into motion. Interest in these motors has grown over the past 20 years because they exhibit interesting collective behaviors and have found potential uses in biomedical and environmental applications. Understanding how these motors operate both individually and collectively and how environments affect their operation is of both fundamental and applied significance. However, there are still significant gaps in our knowledge. This Perspective highlights several open questions regarding the propulsion mechanisms of, interactions among, and impact of confinements on nano- and micromotors driven by self-generated chemical gradients. These questions are based on my own experience as an experimentalist. For each open question, I describe the problem and its significance, analyze the status-quo, identify the bottleneck problem, and propose potential solutions. An underlying theme for these questions is the interplay among reaction kinetics, physicochemical distributions, and fluid flows. Unraveling this interplay requires careful measurements as well as a close collaboration between experimentalists and theoreticians/numerical experts. The interdisciplinary nature of these challenges suggests that their solutions could bring new revelations and opportunities across disciplines such as colloidal sciences, material sciences, soft matter physics, robotics, and beyond.

Untethered Micro/Nanorobots for Remote Sensing: Toward Intelligent Platform

Untethered micro/nanorobots that can wirelessly control their motion and deformation state have gained enormous interest in remote sensing applications due to their unique motion characteristics in various media and diverse functionalities. Researchers are developing micro/nanorobots as innovative tools to improve sensing performance and miniaturize sensing systems, enabling in situ detection of substances that traditional sensing methods struggle to achieve. Over the past decade of development, significant research progress has been made in designing sensing strategies based on micro/nanorobots, employing various coordinated control and sensing approaches. This review summarizes the latest developments on micro/nanorobots for remote sensing applications by utilizing the self-generated signals of the robots, robot behavior, microrobotic manipulation, and robot-environment interactions. Providing recent studies and relevant applications in remote sensing, we also discuss the challenges and future perspectives facing micro/nanorobots-based intelligent sensing platforms to achieve sensing in complex environments, translating lab research achievements into widespread real applications.

Bioinspired self-assembled colloidal collectives drifting in three dimensions underwater

Active matter systems feature a series of unique behaviors, including the emergence of collective self-assembly structures and collective migration. However, realizing collective entities formed by synthetic active matter in spaces without wall-bounded support makes it challenging to perform three-dimensional (3D) locomotion without dispersion. Inspired by the migration mechanism of plankton, we propose a bimodal actuation strategy in the artificial colloidal systems, i.e., combining magnetic and optical fields. The magnetic field triggers the self-assembly of magnetic colloidal particles to form a colloidal collective, maintaining numerous colloids as a dynamically stable entity. The optical field allows the colloidal collectives to generate convective flow through the photothermal effect, enabling them to use fluidic currents for 3D drifting. The collectives can perform 3D locomotion underwater, transit between the water-air interface, and have a controlled motion on the water surface. Our study provides insights into designing smart devices and materials, offering strategies for developing synthetic active matter capable of controllable collective movement in 3D space.

Micro/nanorobots for remediation of water resources and aquatic life

Nowadays, global water scarcity is becoming a pressing issue, and the discharge of various pollutants leads to the biological pollution of water bodies, which further leads to the poisoning of living organisms. Consequently, traditional water treatment methods are proving inadequate in addressing the growing demands of various industries. As an effective and eco-friendly water treatment method, micro/nanorobots is making significant advancements. Based on researches conducted between 2019 and 2023 in the field of water pollution using micro/nanorobots, this paper comprehensively reviews the development of micro/nanorobots in water pollution control from multiple perspectives, including propulsion methods, decontamination mechanisms, experimental techniques, and water monitoring. Furthermore, this paper highlights current challenges and provides insights into the future development of the industry, providing guidance on biological water pollution control.

Tuning Multistep Biocatalysis through Enzyme and Cofactor Colocalization in Charged Porous Protein Macromolecular Frameworks

Spatial organization of biocatalytic activities is crucial to organisms to efficiently process complex metabolism. Inspired by this mechanism, artificial scaffold structures are designed to harbor functionally coupled biocatalysts, resulting in acellular materials that can complete multistep reactions at high efficiency and low cost. Substrate channeling is an approach for efficiency enhancement of multistep reactions, but fast diffusion of small molecule intermediates poses a major challenge to achieve channeling in vitro. Here, we explore how multistep biocatalysis is affected, and can be modulated, by cofactor-enzyme colocalization within a synthetic bioinspired material. In this material, a heterogeneous protein macromolecular framework (PMF) acts as a porous host matrix for colocalization of two coupled enzymes and their small molecule cofactor, nicotinamide adenine dinucleotide (NAD). After formation of the PMF from a higher order assembly of P22 virus-like particles (VLPs), the enzymes were partitioned into the PMF by covalent attachment and presentation on the VLP exterior. Using a collective property of the PMF (i.e., high density of negative charges in the PMF), NAD molecules were partitioned into the framework via electrostatic interactions after being conjugated to a polycationic species. This effectively controlled the localization and diffusion of NAD, resulting in substrate channeling between the enzymes. Changing ionic strength modulates the PMF-NAD interactions, tuning two properties that impact the multistep efficiency oppositely in response to ionic strength: cofactor partitioning (colocalization with the enzymes) and cofactor mobility (translocation between the enzymes). Within the range tested, we observed a maximum of 5-fold increase or 75% decrease in multistep efficiency as compared to free enzymes in solution, which suggest both the colocalization and the mobility are critical for the multistep efficiency. This work demonstrates utility of collective behaviors, exhibited by hierarchical bioassemblies, in the construction of functional materials for enzyme cascades, which possess properties such as tunable multistep biocatalysis.

Magnetically Driven Self-Degrading Zinc-Containing Cystine Microrobots for Treatment of Prostate Cancer

Prostate cancer is the most commonly diagnosed tumor disease in men, and its treatment is still a big challenge in standard oncology therapy. Magnetically actuated microrobots represent the most promising technology in modern nanomedicine, offering the advantage of wireless guidance, effective cell penetration, and non-invasive actuation. Here, new biodegradable magnetically actuated zinc/cystine-based microrobots for in situ treatment of prostate cancer cells are reported. The microrobots are fabricated via metal-ion-mediated self-assembly of the amino acid cystine encapsulating superparamagnetic Fe₃ O₄ nanoparticles (NPs) during the synthesis, which allows their precise manipulation by a rotating magnetic field. Inside the cells, the typical enzymatic reducing environment favors the disassembly of the aminoacidic chemical structure due to the cleavage of cystine disulfide bonds and disruption of non-covalent interactions with the metal ions, as demonstrated by in vitro experiments with reduced nicotinamide adenine dinucleotide (NADH). In this way, the cystine microrobots served for site-specific delivery of Zn²⁺ ions responsible for tumor cell killing via a "Trojan horse effect". This work presents a new concept of cell internalization exploiting robotic systems' self-degradation, proposing a step forward in non-invasive cancer therapy.

Enzymatic Janus Liposome Micromotors

A liposome-based micromotor system that utilizes regional enzymatic conversion and gas generation to achieve directional motion in water is presented. Constituted mainly of a low-melting lipid and a high-melting lipid together with cholesterol, these liposomes maintain stable Janus configuration at room temperature as a result of lipid liquid-liquid phase separation. Local placement of enzymes such as horseradish peroxidase is realized via affinity binding between avidin and biotin, the latter as a lipid conjugate sorted specifically into one domain of these Janus liposomes as a minor component. In the presence of the substrate, hydrogen peroxide, these enzyme-decorated Janus liposomes undergo directional motion, yielding velocities exceeding thermal diffusion by three folds in some cases. Experimental details on liposome size control, motor assembly, and substrate distribution are presented; effects of key experimental factors on liposome motion, such as substrate concentration and liposome Janus ratio, are also examined. This work thus provides a viable approach to building asymmetrical lipid-assembled, enzyme-attached colloids and, in addition, stresses the importance of asymmetry in achieving particle directional motion.

Probing Fast Transformation of Magnetic Colloidal Microswarms in Complex Fluids

The rapidly transformed morphology of natural swarms enables fast response to environmental changes. Artificial microswarms can reconfigure their swarm patterns like natural swarms, which have drawn extensive attention due to their active adaptability in complex environments. However, as a prerequisite for biomedical applications of microswarms in confined environments, achieving on-demand control of pattern transformation rates remains a challenge. In this work, we report a strategy for optimizing pattern transformation rates of colloidal microswarms by coordinating the inner interactions. The influences of magnetic field parameters on pattern transformation rates are theoretically and experimentally studied, which elucidates the mechanism for optimal transformation rate control. The feasibility of the strategy is then validated in viscous Newtonian fluids and non-Newtonian biofluids. Moreover, the strategy is further validated in dynamic flow environments, exhibiting a promising future for practical applications in targeted delivery tasks with an optimal pattern transformation manner.