Chlamydomonas-Inspired Water-Air Interface Mini-Robot with Intricate Tectonics, Programmable Locomotion, and Multifunctional Execution

Chlamydomonas-Inspired Water-Air Interface Mini-Robot with Intricate Tectonics, Programmable Locomotion, and Multifunctional Execution | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Chlamydomonas-Inspired Water-Air Interface Mini-Robot with Intricate Tectonics, Programmable Locomotion, and Multifunctional Execution Lei Ren, Lihuang Li, Libing Huang, Wenyi Liao, Guangshan Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6560275/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract With the rapid development of micro-robotics, non-mechanical stimulus-responsive water-air interface mini-robots have become a prominent focus in intelligent materials and environmentally responsive systems. However, their versatile application is challenged by a fundamental trade-off: simpler structures enable precise motion control, while complex configurations are often required for task execution, making it difficult to balance controllable locomotion with functional complexity. Inspired by Chlamydomonas, we have designed a water-air interface mini-robot with a sophisticated multifunctional architecture (CI-Robot), enabling both programmable motion and multifunctional execution, which demonstrated tremendous potential for application in confined aquatic environments and complex pipelines. The robot can achieve ultra-fast linear and rotational speeds (11.43 body/s, 8.98π rad/s), exceeding biological counterparts by 1.37- and 4.24-fold, via synergistic surface tension gradients and flagellar capillary mechanisms. The fluid-solid coupling simulation reveals the motion mechanism of CI-Robot in the transitional Reynolds regimes, in which the inertial force stabilizes the propulsion force, and the driving torque rapidly decreases to equilibrium (~15.21 μN, ~10⁻⁹ N·m), providing a theoretical basis for the analysis and regulation of the robot's motion behavior. The safe separation distance (~2/3 body length) without interference is determined by collective motion analysis, which guides the reasonable arrangement of CI-Robot group operation. Integrating propulsion and functional modules, the CI-Robot excels in obstacle avoidance, complex path planning, microplastic collection (up to 10 2 particles/mL), bacterial sampling (up to 100 CFU/mL) and site-specific molecular release, retaining samples for >30 minutes. This innovative mini-robot combining unparalleled speed, adaptability, and multifunctionality, will pave the way for transformative applications in cargo delivery, environmental monitoring, microplastic collection, and site-specific sampling in confined space. Physical sciences/Engineering Physical sciences/Physics/Fluid dynamics Physical sciences/Materials science Bionic Mini-Robot Self-propel Motion control Delivery Sampling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Water-air interface mini-robots 1 , 2 represent an emerging frontier in miniature robotics, evolving into a versatile technological platform capable of executing high-precision tasks at the interface between water and air, including water quality monitoring 3 – 5 , maintenance operations 6 , medical interventions 7 , sample collection 8 , 9 , and cargo transportation 4 , 10 . Compared to conventional mechanical methods, non-mechanical mini-robots driven by external stimuli 11 – 13 are particularly well-suited for confined aquatic environments, as their structures facilitate miniaturization and eliminate the need for airtight enclosures required in electromechanical systems. Additionally, they enable more flexible and non-invasive manipulation of delicate biological cells 14 , 15 , microparticle capture 16 , and soft material assembly 17 . Over the past decade, stimulus-responsive water-air interface mini-robots with diverse structures and functionalities have been developed, leveraging propulsion mechanisms such as magnetic 18 , 19 , acoustic 20 , optical 21 , bio-actuation 22 , chemical 23 , and Marangoni propulsion 24 . These mechanisms generate directional gradient fields or driving forces that guide robots along predictable motion trajectories 25 . However, such robots are still constrained by low energy conversion efficiency (e.g., light-driven systems achieving < 5%) 26 , environmental sensitivity (e.g., pH and ionic concentration dependencies) 27 , 28 , limited control precision, response latency, and other performance bottlenecks. Moreover, most existing designs—such as particles 16 , bars 29 , 30 , and thin films 31 , 32 —feature simplistic structures, which hinder their ability to regulate motion speed or perform complex functional tasks. While simpler structures enhance control precision, task execution often necessitates more intricate designs, posing a fundamental challenge in balancing motion control and task performance 2 . In terms of locomotion, water-air interface mini-robots must counteract gravitational forces and resist disturbances from water currents while maintaining buoyancy 33 . Their buoyancy is typically governed by the hydrophobicity of their materials, external geometries, and immersion depths 34 . However, existing studies have primarily focused on quantitative buoyancy analysis for mini devices with regular geometry, such as elongated rods 35 , prisms 36 , and spheres 37 , while research on irregularly shaped robots remains scarce. Once stable flotation is achieved, optimizing motion speed and stability becomes a critical objective, as a robot's mobility is determined by the interplay between driving forces and hydrodynamic resistance, with the latter being positively correlated with the robot’s geometry and instantaneous velocity 38 . Under constant geometric conditions, increased propulsion results in higher transient acceleration, ultimately leading to an elevated steady-state velocity once force equilibrium is reached. In current studies on water-air interface mini-robots, the Marangoni effect—driven by solute release—is a primary mechanism for generating high propulsion forces 39 , 40 . However, the hydrodynamic disturbances induced by Marangoni-driven flows can significantly disrupt the robot’s directional stability 41 . Therefore, achieving a balance between propulsion force, motion resistance, and hydrodynamic disturbances is a critical challenge in designing high-performance water-air interface mini-robots. To address these challenges, we present a Chlamydomonas-inspired miniature robot (CI-Robot) that integrates a non-mechanical stimulus-responsive system with a complex internal structure while enabling multimodal motion control (Fig. 1 ). This novel design allows precise and programmable execution of multiple tasks, significantly advancing the capabilities of non-mechanical mini-robots. The CI-Robot’s shape is inspired by Chlamydomonas who exemplifies nature’s solutions to micro-scale energy autonomy, motion control and environmental adaptation. The robot incorporates an internal buoyancy chamber, flagella fuel channels, a microporous barrier, and a rear fuel channel. By optimizing the shape and buoyancy cavity design, the load capacity of the CI-Robot is significantly enhanced. Moreover, various motion patterns—including rotary motion ( J Flagella >0), variable rectilinear motion ( J Rear >0), and static equilibrium ( J = 0)—are achieved by regulating fuel flux ( J ) through different channel exits. To improve maneuverability and remote navigation, we integrate magnetic eye structures using a magnetic gel-based material. Additionally, we leverage the strong mass transfer effect induced by the Marangoni effect to develop interactive functionalities, such as controlled content release and environmental sampling. Notably, through the synergistic action of strong mass transfer and the capillary liquid retention effect, the CI-Robot successfully captures and retains both non-living microplastics and live bacteria, demonstrating its potential for advanced environmental and biomedical applications. Results Structure and motion design of CI-Robot The CI-Robot was inspired by the morphology and kinematic characteristics of Chlamydomonas 42 . The complex locomotion behavior of Chlamydomonas could be divided into rotational and straight mode (Fig. 2 A). For rotational mode, the flagellum of Chlamydomonas beat with different phases and orientation. The unbalanced beating efficiency caused Chlamydomonas to produce a total tug force ( F Cr ), which application point was away from the center of body. Since the Chlamydomonas was in a low Reynolds number fluid, there must be a viscous friction force ( f Cr ) and a viscous friction moment ( M fC ) opposite to the direction of rotation under the equilibrium of forces. For straight mode, the flagellum of Chlamydomonas beat with synchronous phase and orientation. The symmetric beating efficiency also caused Chlamydomonas to produce a total tug force ( F Cs ), but which application point is located in the center of body. Similarly, the fluid provides a viscous frictional force ( f Cs ) in the opposite direction of straight. The detailed discussion about morphology (Fig. S1 ) and locomotion behavior (Fig. S2) of Chlamydomonas was shown in Supplementary Materials with corresponding text illustration. During the rotational and straight motion of Chlamydomonas, the total tug force ( F C ) and total power ( P C ) are similarly distributed in these two motion modes (5 ~ 15 pN, 0.5 ~ 1.5 fW), but the total friction moment ( M fC ) only has a large value in the rotation mode (40 ~ 90 aN·m) (Fig. 2 B). From the analysis data, we can conclude that Chlamydomonas switched their swimming modalities by adjusting the application point of total tug force. With the complex coupling of straight and rotation, the Chlamydomonas can exhibit abundance of modalities in swimming behavior that enables them to adapt to the complex physical environment (Movie S1). Considering the workspace of the actuator and the morphology of Chlamydomonas, the CI-Robot was designed as an ellipsoid whose length was 6 mm with two flagella (Fig. S3A). Moreover, we also designed magnetic eyes for navigation, and buoyancy chamber for reducing resistance (Fig. S3B). Inspired by the application point of total tug force of Chlamydomonas, we designed the structure and motion mode of CI-Robot that can rotate by an off-center driving force generating through the flagella channel or moving forward by an on-center driving force generating through the rear channel. The flagella channel was separated from the rear channel by a microporous barrier. By loading ethanol in different channel, the CI-Robot could produce different driving effects that was as same as the rotational and straight motion of Chlamydomonas. When the rear channel was loaded with ethanol, due to the capillary pressure of the microchannels in microporous barrier, ethanol was difficult to diffuse into the flagella channel and only be released by the rear channel exit. At this time, the released ethanol formed a concentration gradient at the rear channel exit, which generated a driving force ( F Rs ) on the rear channel exit. As the robot moves, the fluid generates a friction force ( f Rs ) in the opposite direction of the motion (Fig. 2 C). Since the direction of F Rs passed through the CI-Robot center, the CI-Robot generated a straight motion (Fig. 2 F). Further, we studied the influence of the flagella shape of CI-Robot on the fluid resistance through simulation calculation and optimized the morphology of the flagella design (Fig. S4). The numerical simulation of fluid dynamics models and computational formulas are shown in Supplementary Materials. Firstly, we explored the influence of flagella epitaxial angle (𝜃) on fluid resistance. With the increase of flagella epitaxial angle, the fluid pressure of CI-Robot was mainly distributed in the front face of the model and the position of flagella (Fig. S5A), and the viscous stress per unit area was mainly concentrated in the end of flagella (Fig. S5B). Additionally, the resistance of CI-Robot gradually decreases with the increase of the epitaxial angle and tends to be flat when it was greater than 5π/6 (Fig. S6). Then we explored the influence of flagella bending radius ( R ) on fluid resistance. The maximum fluid pressure of CI-Robot was insensitive to the flagella bending radius (Fig. S7A). The distribution of viscous stress per unit area remained largely unchanged when the model body contour design parameters were fixed. However, the viscous stress at the flagellum's end decreases as the flagellar bending radius increases. (Fig. S7B). However, with the increase of the flagellar bending radius, the projected area of the flagella on the incoming flow increased, which leads to an increase of pressure resistance (Fig. S8). Finally, we explored the influence of flagella length ( L ) on fluid resistance. Similarly, the maximum fluid pressure of CI-Robot was insensitive to the flagella length (Fig. S9A), but the viscous stress per unit area became more concentrated distribution on the end of flagella with the increase of the flagella length (Fig. S9B). This results in a linear increase in resistance as the flagella length increases, which was mainly due to the linear increase of the extension segment of the flagella (Fig. S10). By comparing the numerical solutions of the fluid resistance, the epitaxial angle and bending radius of the flagella mainly affect the fluid resistance, while the length of the flagella mainly affects the concentrated distribution of stress points. Based on the above studies, we proposed 𝜃=5/6𝜋, R = 2 mm, and L = 4.8 mm as parameters for the shape design of CI-Robot. In this optimized design, the distribution of viscous stress (Fig. 2 D) and pressure (Fig. 2 G) of the CI-Robot in straight motion at 100 mm/s was mainly concentrated in the front of the flagella. The fluid resistance increased gradually with the immersion depth ( H ) of the CI-Robot, and the fluid resistance increased slightly when the immersion depth exceeds half of the CI-Robot thickness (i.e. H > 2.5 mm) (Fig. 2 E). Under full immersion, the fluid resistance of the CI-Robot increases as a power function with the increase of motion speed (Fig. 2 H). The change of fluid resistance conforms to the fitting of the hydrodynamic equation: $$\:f=0.01{v}^{1.64}$$ 1 In addition, the fluid resistance of the CI-Robot in straight motion ( f ) was mainly provided by the pressure resistance ( f p ), and the proportion of viscous resistance ( f k ) can be ignored. When the flagella channel was loaded with ethanol, the unbalanced placement of flagella channel in the process of placing CI-Robot on water would generate a small gravitational potential energy difference, which induces the higher flagella channel exit to release ethanol while the lower flagella channel exit to indrawn environmental water due to the capillary action. At this time, the released ethanol formed a concentration gradient at the flagella channel exit, which generated a driving force ( F Rr ) on the flagella channel exit. The friction force ( f Rr ) and the friction moment ( M fR ) generated by the fluid during the rotation of robot change with the motion speed of the rotation (Fig. 2 I). Since the direction of F Rr deviated from the CI-Robot center, the CI-Robot generated a rotational motion (Fig. 2 L). In the rotational motion with 10π rad/s, the distribution of viscous stress (Fig. 2 J) and pressure (Fig. 2 M) of the CI-Robot was mainly concentrated at the end of the flagella. The fluid resistance moment increased significantly before the immersion depth reached half of the CI-Robot thickness (i.e. H 2.5 mm) (Fig. 2 K). Under full immersion, the fluid resistance moment of the CI-Robot increases as a power function with the increase of rotational speed (Fig. 2 N). The change of fluid resistance moment conforms to the fitting of the hydrodynamic equation: $$\:{M}_{f}=0.31{\omega\:}^{1.64}$$ 2 Similarly, the fluid resistance moment of the CI-Robot in rotation motion ( M f ) was provided by the pressure resistance moment ( M fp ) rather than viscous resistance moment ( M fk ). According to this design, we manufactured the front and back components of the CI-Robot using a high temperature resistant resin through 3D projection microstereolithography. The front component is composed of flagella fuel channel, magnetic eyes chamber, buoyancy chamber and fuel storage chamber in the front half, while the back component consists of rear fuel channel, microporous barrier, buoyancy chamber and fuel storage chamber in the back half. Under microscopic observation, the printing effect of each channel of the component is consistent with the design. Following the CI-Robot was microassembled to encapsulate the two components through light curing glue. The buoyancy chamber and fuel storage module of CI-Robot was assembled into closed compartments. Under the combined action of surface tension and buoyancy, the CI-Robot was able to float stably on water-air interface (Fig. S11). The rotation mode of CI-Robot The above analyses provided a decouple of the swimming performance of the CI-Robot, which was influenced by various kinematic parameters. Based on the rotational behavior generated by the fuel flux of flagella fuel channel, three preset channel programs were designed to generate the Chlamydomonas-inspired multimodal rotational swimming gait including mode-1, mode-2 and mode-3. During the mode-1 motion of the CI-Robot, all channels were open, where ethanol was outflows from the flagella channel on one side, and water in the environment inflows the fuel storage compartment from the other flagella channel and rear channel (Fig. 3 A). The CI-Robot in mode-1 rotates continuously (Fig. 3 B) and completes 30 laps within 20 s (Fig. 3 C, Movie S2). In this typical rotational motion (Fig. 3 D), after 0.08 s variable angular acceleration (T1 stage), the CI-Robot was transformed into high-speed uniform rotational movement (T2 stage) and then performed variable angular acceleration movement (T3 stage) and adjusted low speed uniform rotational movement (T4 stage). The driving moment ( M F ) of the CI-Robot during its rotational motion meets the following requirements: $$\:{M}_{F}-{M}_{f}=I\alpha\:$$ 3 Where I is the inertia moment of CI-Robot, which measured by modeling software as 3.89×10 − 11 kg∙m 2 , α is the instantaneous angular acceleration of the robot. According to Eq. ( 2 ) and Eq. ( 3 ), the relationship between angular acceleration and angular velocity under rotational motion could be described as: $$\:{\omega\:}^{1.64}=-\frac{I\alpha\:}{0.31}+\frac{{M}_{F}}{0.31}$$ 4 Therefore, the instantaneous driving moment and resistance moment of CI-Robot for rotational motion could be obtained by iterated angular acceleration and angular velocity into Eq. ( 4 ) (Fig. 3 E-F). According to the fitting results of each lap, the driving moment was much greater than the resistance moment before the ten turns, then became equal after the ten turns. That is to say, the driving moment and resistance moment of CI-Robot in rotational motion reach equilibrium in a very short time. Moreover, the rotation of CI-Robot can be started with a small driving moment (~ 120 nN·m) and maintained the constant rotation with a smaller force moment (~ 20 nN·m). Further, to reveal the reason of the rapid reduction of driving moment in the rotation process, the release of ethanol was simulated by finite element analysis. The simulation analysis results demonstrated that the ethanol concentration of solutions near the flagella channel outlet increases with the increase of the laps of CI-Robot (Fig. 3 G). This phenomenon was caused by the fact that the ethanol diffusion was uncoordinated with the robot rotation speed. After a quick rotation of the CI-Robot, the ethanol released in the initial position had not fully diffused, so the concentration gradient near the flagella channel outlet gradually decreases after each rotation. According to Fick's law (equation S1) and the Marangoni effect formulation (Eq. 8 – 9 ) (38, 41 ), the flux J and the surface tension gradient ∆γ are proportional to the concentration gradient. So as the concentration gradient decreases, J and d decrease, v decreases according to equation (S2). So as the concentration gradient decreases, J and ∆γ decreases which cause v decreases according to J = ρ e ·(L·∆γ-kv n ) dt/dm . After several laps, the concentration gradient became stable as the number of laps increased, and v was also stabilized (Fig. 3 H). Therefore, in the rotation mode, the driving moment of the CI-Robot decreased rapidly first and then tended to be stable with the increase of the number of laps. This phenomenon is further proved by the coincidence of ethanol emission in experiment and simulation (Fig. 3 I). For mode-2, the bilateral flagella fuel channel was open and the rear fuel channels were closed (Fig. S12A). Thus, a continuous rotation with a slightly faster velocity like mode-1 is produced (Fig. S12B) and rotated 30 laps within 20 seconds (Fig. 3 J, Movie S2). However, when only one of the flagella fuels channels was open in mode-3 (Fig. S13A), and the CI-Robot exhibited intermittent motion (Fig. S13B). Due to the long and vapidity deceleration rotation, the CI-Robot only rotated 1.5 laps in 20 seconds in mode-3 (Fig. 3 K, Movie S2). By comparing the angular velocities of the three rotational motions (Fig. 3 L), the angular velocity of mode-1 and mode-2 increases from 0 rad/s to the maximum of 10π rad/s, then maintained for a period before 2.5 s, and finally maintained with a lower and stable angular velocity of 2π rad/s. In the intermittent motion in mode-3, the intervals were about 2 s, consisting of a rapid rotation of 0.4 s and a deceleration of 1.6 s. These results show that the rotation speed of CI-Robot could be controlled by controlling the channel exit mode. In addition, we also adjusted the rotation velocities of the CI-Robot by the control of different fuels, different concentrations ethanol and different viscosity solution (Fig. S14). Detailed discussion is provided in Supplementary Materials. Driven by 100% ethanol fuel, the average angular velocity of mode-1 CI-Robot in aqueous solution can reach 8.98π rad /s, which is 4.24 times the average angular velocity of Chlamydomonas (Fig. S15). The straight mode of CI-Robot Adjusting the mode of channel exit also enables to control the straight mode of CI-Robot. In mode-4, ethanol was filled in the rear fuel channel of the CI-Robot, while the double-flagellar fuel channel remained empty (Fig. 4 A). Currently, the external solution was sucked in from the flagellar channel outlet, and ethanol fuel was released from the tail channel outlet. At this point, the CI-Robot started moving rapidly in a straight line (Fig. 4 B, Movie S3), covering approximately 20 mm in 0.5 s (Fig. 4 C). In this typical straight motion (Fig. 4 D), the CI-Robot transformed into high-speed uniform motion (T2 stage) after 0.08 s variable acceleration motion (T1 stage). However, since the driving force of Marangoni effect decreased rapidly in the early stage, which was insufficient to balance the fluid resistance, the CI-Robot further performed variable acceleration motion (T3 stage) and then adjusted the low-speed uniform motion (T4 stage). The driving force ( F ) of the CI-Robot during its straight motion meet the following requirements: $$\:\:\:F-f=ma$$ 5 Where m is the mass of CI-Robot, which measured by modeling software as 0.0385 g, a is the instantaneous acceleration of the CI-Robot. According to Eq. ( 1 ) and Eq. ( 5 ), the relationship between acceleration and velocity under straight motion could be described as: $$\:{v}^{1.64}=-\frac{ma}{0.01}+\frac{F}{0.01}$$ 6 Therefore, the instantaneous driving force and resistance of CI-Robot for straight motion could be obtained by iterated acceleration and angular velocity into Eq. ( 6 ) (Fig. 3 E-F). The driving force was greater than the fluid resistance in the T1 stage when CI-Robot started, which corresponds to the rapid increase of the motion speed of the CI-Robot. At the T3 stage, the rapid reduction in driving force was insufficient to sustain the CI-Robot's resistance during high-speed movement at the T2 stage, resulting in variable acceleration and deceleration. From the T2 to T4 stage, the driving force was almost in a flat state with fluid resistance, which corresponds to the relatively uniform movement of the CI-Robot. Similarly to rotational motion, the driving force and resistance of CI-Robot in straight motion reach equilibrium in a very short time. In addition, the measurement of the surface height when the CI-Robot moves indicated that the CI-Robot presents a unique form of undulation propulsion (Fig. 4 G). The wake flow pattern induced by the CI-Robot is consistent with the Chlamydomonas. The surface wave at 2 mm from the rear outlet has an amplitude of 0.3 mm and a period of 6 ms (Fig. 4 H). Based on this, the ethanol release during the CI-Robot's straight motion was simulated. The analysis showed that the released ethanol diffuses along the CI-Robot's trajectory ( Fig. 4 I). From the trajectory of the ethanol concentration distribution, the ethanol concentration increases with decreasing distance at the outlet, specially producing a sharp increase within 10 mm. The large ethanol concentration gradient near the outlet is the reason for the fast movement of the CI-Robot. In addition, when the CI-Robot started, the flux at the outlet of rear fuel channel dropped rapidly and remain unchanged, indicating that the CI-Robot quickly switches to approximately uniform motion after a brief variable acceleration movement. Therefore, the force at the rear was almost constant during motion in mode-4, and the dynamics of the CI-Robot was completely determined by the flux at the rear fuel channel outlet. For mode-5, the flagella channel closed and the rear channel open (Fig. S16A), the CI-Robot exhibits periodic straight motion with a period of about 2.4 s, consisting of rapid in-line movement of 0.4 s and inertial drift with an interval of 2 s (Fig. S16B). Different from mode-4, in this single-opening design, the CI-Robot trajectory fails to maintain a linear trajectory but has a certain lateral deviation (Fig. 4 J, Movie S3). This phenomenon occurs because there is no driving force during the inertial drift stage, and the lateral resistance from the fluid boundary effect becomes the dominant factor influencing the motion trajectory. In the mode-6 design of CI-Robot without the flagella channel (Fig. S17A), a similar motion of mode-5 the CI-Robot was exhibited (Fig. S17B). However, due to the lack of flagella orientation, the lateral resistance generated by the fluid changes more and the CI-Robot cannot maintain the stability of the forward direction, thus showing a planar circular motion on the trajectory (Fig. 4 K, Movie S3). By comparing the velocities of the three straight motions (Fig. 4 L), the velocity of mode-4 increased from 0 mm/s to the maximum of 120 mm/s, then maintained with a lower and stable angular velocity of 75 mm/s. In the intermittent motion in mode-5 and mode 6, the peak velocity was roughly the same at about 15 mm/s. These results show that the straight speed of CI-Robot could also be controlled by controlling the channel exit mode. Moreover, we also verified that the straight speed of the CI-Robot can be regulated by different fuels, different ethanol concentrations and different viscosity solutions (Fig. S18), which has detailed discussed in Supplementary Materials. Driven by 100% concentration ethanol fuel, the average displacement speed of mode-4 CI-Robot in aqueous solution can reach 11.43 body/s, which is 1.37 times the displacement speed of Chlamydomonas (Fig. S19). The collective motion of CI-Robot In nature, Chlamydomonas often exhibit collective movement 43 , demonstrating synergistic performance than individual cells swimming alone. This inspired us to design collective motion of CI-Robot to improve work efficiency. On this basis, we investigate the collective motion of CI-Robot approaching each other under rotating motion. As shown in Fig. 5 A, two CI-Robots with radius R respectively perform synchronous anti-directional rotation when the distance is D . For example, Robot-1 has a driving force F 11 at the exit of its own flagella, while Robot-2 has a reverse driving force F 21 . The angle formed by the two driving directions is related to the phase angle of the CI-Robot at this time. In addition, the CI-Robot itself generates a corresponding resistance torque due to its movement. The force of robot-2 is symmetric with robot-1. From this mechanical state, when two CI-Robots are close to each other, their rotational speed will be restricted by each other. To avoid the influence of motion state between multiple CI-Robots, it is necessary to study the safe working distance between two CI-Robots without mutual restriction. Based on this, we numerically study the release of ethanol fuel when two CI-Robots approach each other. The results show that when D/R is less than 2.5, the ethanol concentration in the middle water area of the two robots will be significantly higher than that in the outer water area (Fig. S20A), which will lead to the reduction of surface tension in the middle water area (Fig. S20B). In addition, the flow velocity of the intermediate waters is close to rest when D/R is greater than 4, and the flow velocity gradually increases with the decrease of D/R (Fig. 5 B). The repulsive force against the rotational motion of the two CI-Robots is composed of the Marangoni effect ( F m ) and the hydrodynamic force ( F u ). For comparison, we divide these two forces by the total tension of the CI-Robot in water ( F 0 ) to obtain a dimensionless force, and the detailed calculation formula shows in the supporting materials (Fig. 5 C). As the D/R value decreases, the repulsion force between the two CI-Robots gradually increases. In addition, the increase of F m is smaller than that of F u . This indicates that the hydrodynamic force is the main factor that interferes with the velocity of two CI-Robots when they are near each other. When D/R > 4, the repulsive force obstructing the rotational motion is close to 0, that is, the two CI-Robots do not interfere with each other. The results show that the safe working distance between the two CI-Robots is D/R greater than 4. The experimental results also prove this conclusion. As shown in Fig. 5 D-E (Movie S4), when two CI-Robots run separately at D/R = 2, the flagella are close to each other, the rotation speed of both is significantly reduced. The slower moving CI-Robot will further slow down due to the reaction force of the faster CI-Robots. However, when the two CI-Robots run separately at D/R = 4, the motion state between them does not affect each other. When the two CI-Robots are close to each other, there is no change in their rotational speed. In addition, we also studied the motion and stress state of linear array arranged CI-Robot in fluid through simulation (Fig. 5 F). The robot achieves orderly navigation by constructing its own magnetic eye and an external navigation system (Fig. S21). When the fluid velocity is 5 mm/s, the offset distance of CI-Robot decreases with the increase of array spacing (Fig. 5 G). When the array spacing reaches 2 times the body length of CI-Robot, the offset distance of CI-Robot does not change, which indicates that the non-influence distance of CI-Robot in linear array arrangement is 6 mm. Under this arrangement of column spacing, we compare the state of CI-Robot in simulation and real experiment (Fig. 5 H, Movie S5). In the real experiment, the arrangement of CI-Robot does not maintain the direction of head to tail as in the simulation, but there is no obvious difference in the displacement distance when the fluid velocity is 5 mm/s. In addition, as the fluid flow rate increases from 0 mm/s to 5 mm/s, the deviation distance of CI-Robot basically maintains a small change (Fig. 5 I). In the array arrangement of five CI-Robots, the CI-Robot at the front of the oncoming position is subjected to the greatest resistance, followed by the CI-Robot at the end (Fig. 5 J). These results show that there is no congestion when the CI-Robot is running in the pipeline with an array spacing of 6 mm, which provides control parameters for controlling the efficient movement of the CI-Robot. Programed locomotion and multifunctional execution of CI-Robot in complex scenes The small size, rapid movement, and complex coupled swimming capabilities make the CI-Robot highly promising for a variety of applications, particularly in programmable complex path planning and cargo handling. We have realized the path planning of the CI-Robot under the programming of the magnetic field. For example, the CI-Robot could move in a straight line and rotates at the specified position. Then finally returns to the starting position according to the planned path (Fig. S22, Movie S6). Besides the magnetic navigation system, the autonomous movement of the CI-Robot in the maze was realized by utilizing the fluid reaction force generated by the boundary conditions (Fig. S23). In order to further verify the obstacle avoidance ability of CI-Robot in complex water scenes, we carried out the obstacle avoidance operation of the robot in the narrow water area of the array cylinder (Fig. S24, Movie S6). Furthermore, we have achieved the fixed-point cargo handling capabilities of CI-Robot (Fig. S25, Movie S6). The detailed operation mode and related discussion of programmatic movement and multifunctional execution of ci robots in these complex scenarios are shown in Supplementary Materials. Unique applications for narrow water surface in pipelines with CI-Robot Miniature robots oriented to work in narrow pipes play an important role in pipe monitoring with sophisticated instruments (such as circulating pipe systems in spacecraft), complex and long pipe maintenance (such as industrial pipes), sample collection of pipe water quality (such as water supply pipes), and biomedical procedures (such as therapeutic cargo carrying and release in the intestinal tract) 44 . However, moving in narrow pipe environments is always a big challenge for miniature robots, because narrow gaps, curved paths and complex pipe interfaces often occur on navigation paths that prevent miniature robots from achieving exploration goals and operational tasks. Therefore, after successfully controlling the movement behavior control of various complex paths of the CI-Robot, we further explored the application of the CI-Robot in the pipeline for microplastics and bacteria sampling, environmental monitoring and antibacterial (Fig. 6 A, Movie S7). With the help of the structural design of the CI-Robot's capillary channel, a mass transfer effect occurred when ethanol was released, causing water to be absorbed from the flagella into the CI-Robot's fuel chamber. After the ethanol was released, the water inside the channel was well retained due to the capillary's retention ability. Utilizing this feature, we demonstrated the sampling of microplastics in a curved pipe. In a pipeline model, the pipeline was filled with microsphere solutions of different concentrations, sizes, and materials as a microplastic model. After loading fuel, the CI-Robot was magnetically guided into the curved pipe to perform the sampling operation. Once the sampling was completed, the CI-Robot was removed, and the obtained sample was extracted from its capillary structure for flow counting analysis. Additionally, after drying the sampled CI-Robot, the capillary structure at its antennae was dissected, and the interior was characterized by SEM to observe the presence of the obtained microplastics. As shown in Fig. 6 B, under the guidance of magnetic navigation, the CI-Robot can move smoothly in the U-shaped acrylic pipe with an inner diameter of φ = 15mm and carry out rotating sampling operations at the turning point. Here we use polystyrene and silica microspheres with different sizes as microplastic models. From SEM images, CI-Robot can sample the microspheres into its flagellar channels (Fig. 6 C). The experimental results show that CI-Robot has a stronger ability to sample larger diameter microspheres under the same material, and that to larger density microspheres under the same size (Fig. S26A). Moreover, the sampling operation of CI-Robot was mainly concentrated in the first 20 seconds, after which the number of microspheres sampled remained basically unchanged (Fig. S26B). Additionally, we compared the impact of the three operating modes on sampling. The CI-Robot achieved the fastest movement and maximum sampling capacity when all passageways were open. When only one passageway was opened, the CI-Robot's fuel mainly diffused and released, without generating significant fluid mass transfer, resulting in poor sampling ability. When the CI-Robot was not filled with fuel, mass transfer was absent, and diffusion solely relied on the free Brownian motion of the microspheres, leading to an almost nonexistent sampling capability. (Fig. S26C). Further, we discuss the sampling capability of CI-Robot in solution environments with different concentrations of microspheres. As the concentration of microspheres in the environment increased, the number of samples taken by the CI-Robot increased linearly, and its minimum sampling limit was 10 3 number/mL (Fig. S26D). After the CI-Robot performs sampling, it is important to consider whether the captured sample may leak due to the robot's need to operate in the pipeline for an extended period. To address this concern, we designed an experiment to compare immediate sampling analysis with delayed sampling analysis. As shown in Fig. 6 D-E, the proportion of microspheres in both immediate and delayed sampling analyses remains essentially unchanged. Numerical analysis clearly indicates that the PS-L microspheres captured by the CI-Robot experience only a minor, non-significant reduction during delayed sampling, with no substantial leakage observed in the solution (Fig. S26E). The slight variation may be attributed to minor handling errors during the transfer of the CI-Robot. These results demonstrate that the CI-Robot effectively captures and retains samples in the sample area, with no significant changes over time or in varying environmental conditions. Moreover, we evaluated the ability of CI-Robot to transport small molecule compounds or drugs in pipes. Taking environmental pH monitoring as an example, the fuel containing phenolphthalein indicator was loaded inside the CI-Robot, and the photoresponsive gel was used to plug each fuel channel outlet of the CI-Robot. When magnetically driven to a specified tank, a near-infrared laser was used to illuminate the gel closure of the CI-Robot. At this time, the fuel outlet was opened, and phenolphthalein dissolved in ethanol fuel was released into the tank with the rotating motion of the CI-Robot, and the diffusion process of phenolphthalein in the tank was accelerated due to the self-driven rotation of the CI-Robot. In this process, when the solution was alkaline, it would produce a color reaction, and then the acid and alkali of the environmental water could be quantitatively analyzed according to the colorimetric method. As shown in Fig. 6 F, the CI-Robot can run into the specified tank to release phenolphthalein at a fixed point. At the same time, the accompanying rotating motion can play a similar role of agitation, so as to display uniform different color states according to the pH of the solution. By comparing the gray value of the image (Fig. 6 G), the color change of the region can be qualitatively obtained, and the acid-alkalinity of the environment can be monitored at a fixed point. The results demonstrate that the CI-Robot can release small molecule compounds and enhance the uniform diffusion of these compounds in the region through its self-driving capabilities during the release process. We further investigated the collecting and eliminate live bacteria ability of CI-Robot within pipelines. Using Escherichia coli ( E. coli ) as the model bacteria, the CI-Robot sampled bacterial solutions of varying concentrations, with subsequent analysis via qPCR. As the bacterial concentration increased, the amplification curve of CI-Robot samples shifted significantly to the right, while parallel experimental groups showed only slight changes (Fig. 6 H). The Ct values exhibited a clear linear relationship with bacterial concentration, with a detection limit of 100 CFU/mL (Fig. 6 I). Additionally, samples taken by CI-Robot immediately after sampling and samples taken after soaking for 30 min had consistent results in the amplification curve characterization of qPCR, indicating that the CI-Robot could successfully capture live microorganisms and lock in sample without leakage (Fig. 6 J). CI-Robots loaded with ethanol fuel showed significant bacterial capture due to fluid mass transfer, whereas those without fuel captured few bacteria relying only on microorganism movement (Fig. 6 K-L). When loaded with Kana antibiotic solutions, the CI-Robot exhibited self-driving behavior through the Marangoni effect, enhancing antibiotic diffusion and distribution. In contrast, the CI-Robot with pure water did not generate self-drive and had no mass transfer effect. In both low- and high-concentration bacterial solutions, the self-driven CI-Robot loaded with antibiotic fuel showed rapid antibacterial action (Fig. S27). Conversely, the non-driven CI-Robot did not release antibiotics effectively, failing to exhibit significant antibacterial effects. Quantification of antimicrobial properties revealed that the self-driven CI-Robot had an antibacterial rate of over 95%, compared to less than 50% for the undriven CI-Robot, indicating a substantial difference in performance. These results demonstrate that the self-driven CI-Robot, loaded with antibiotic fuel, can effectively release and accelerate the diffusion of antibiotics, while the non-driven version does not achieve effective bacterial killing. Discussion In summary, we have designed self-propelled water-air interface mini-robots using Chlamydomonas as a prototype, focusing on shape design and motion bionics. Through comprehensive design optimization, motion simulation, and control of motion direction and speed, we have reconstructed the complex, long-duration, and multi-modal movement of these mini-robots, enabling their multi-functional application in various narrow water areas and pipeline operations. The design principles of the mini-robots we propose are primarily based on the drag-reducing shape characteristics of Chlamydomonas, optimized through fluid-structure coupling simulations. Unlike most existing sheet-like or thin-film water-air interface mini-robots, our design incorporates a complex three-dimensional structure. The combination of the buoyancy chamber and the buoyancy enhancement effect, stemming from the shape design, significantly improves the load capacity of the mini-robots, reaching up to 2.968×10 − 3 N. Building on this, we designed a Y-shaped capillary fuel release system driven by the Marangoni effect, inspired by the driving force direction and application points observed in different Chlamydomonas motion modes. This system facilitates the reproduction of both linear and rotational motion behaviors by adjusting the form, size, and direction of the driving force generated by the fuel release. The mini-robots achieve a linear speed of 11.43 body/s and a rotational speed of 8.98π rad/s, surpassing the linear speed of 5.25 body/s and rotational speed of 1.8π rad/s of Chlamydomonas. These results demonstrate that the designed water-air interface mini-robots possess efficient driving capabilities. In response to the lack of systematic research on the fluid state at the gas-liquid interface in current water-air interface robot research, we developed a dynamic simulation model based on kinematic theory and calculated the magnitude of the driving force for both straight and rotational motion of the mini-robots. The results indicate that the driving force required for the mini-robots to start straight motion from rest is at least on the order of 10 − 3 N, while maintaining straight motion after reaching a specified linear speed requires only a stable driving force on the order of 10 − 5 N. Initiating rotational motion requires a driving torque on the order of 10 − 6 N·m, while maintaining stable rotation after reaching the specified rotational speed requires a driving torque on the order of 10 − 9 N·m. The time-dependent variation in driving force closely aligns with the temporal changes in tension generated by the Marangoni effect, enabling rapid startup and stable operation through Marangoni-effect-driven propulsion. Additionally, by simulating fuel diffusion and release distributions under various motion states of the driving system, we observed that fuel diffusion and release decrease with increasing motion speed. This leads to a negative feedback regulation of motion speed, allowing the mini-robot to continue operating at a relatively stable speed. Addressing the inherent challenges of water-air interface robots driven by the Marangoni effect, such as rapid fluid diffusion caused by surface tension gradients and significant effects of boundary conditions on flow direction and path, we propose a control strategy to adjust the propulsion mode, speed, lifespan, and directionality of the mini-robots. This is achieved by designing fuel channels, controlling the ethanol flow rate, and applying long-range magnetic navigation. By adjusting the fuel channel outlet status and ethanol flow rate, we demonstrate various straight and rotational motion behaviors of the mini-robots. Remote navigation based on magnetic fields allows for programmable trajectory motion. The results show that the designed water-air interface robots can accomplish tasks such as obstacle avoidance, complex path planning, and fixed-point transportation of small goods in complex water bodies, such as narrow channels, array column channels, and curved pipelines. These findings indicate that the mini-robots exhibit strong motion performance and high environmental adaptability. Furthermore, in response to the potential interference during the operation of mini-robot collectives, we have determined through experimental and simulation calculations that the distance at which mini-robot collectives do not interfere with each other during operation is 2/3 of their body length. Currently, water-air interface robots often require external functional modules to perform specific tasks, which limit their practical application in aquatic environments. To address this limitation, we propose integrating the functional actuator and driving mechanism into a single unit by utilizing the strong mass transfer effect of the Marangoni effect and the liquid retention capability of the capillary tube. This integration enables applications in environmental detection, antibacterial agent transport and release, and pollutant sampling. The results show that the designed capillary-based drive system can release small-molecule compounds dissolved in the fuel during operation, enabling environmental detection and antibacterial applications. In pipeline environments, the capillary-based system demonstrates the ability to capture non-active microplastics and active bacteria under strong mass transfer and liquid retention capabilities. It also ensures that samples can be retained for extended periods (> 30 min) without loss after sampling. These results indicate that the mini-robots performs well in pipeline environments, both in operation and in sampling analysis. We expect this study to help decouple the complex gaits generated by Chlamydomonas, leading to a deeper understanding of their underlying mechanisms. Additionally, this robot can be functionalized with various responsive hydrogels and functional materials, with potential applications in environmental remediation and drug delivery. Moreover, it could inform the design and operation of future bionic robots with programmed swimming gaits, opening new avenues for control, sensing, and driving mechanisms. Methods Materials Ethanol, methanol, acetone, N, N-dimethylformamide (DMF), N, N-dimethylaniline (DMA), glycerinum, 2,4,6-trimethylbenzoyl phenylphosphinate acid ethyl ester (TPO-L), and hydrochloric acid (HCl) were purchased from Xilong Scientific Co.,Ltd. (Shanghai, China). Ferric chloride anhydrous (FeCl 3 ), ferrous chloride tetrahydrate (FeCl 2 ·4H 2 O), trisodium citrate dihydrate (C 6 H 5 Na 3 O 7 ·2H 2 O), sodium hydroxide (NaOH), acrylamide (AAm), bis-acrylamide (BIS), polyvinylpyrrolidone (PVP), sodium borohydride (NaBH 4 ), hexadecyl trimethyl ammonium bromide (CTAB), sodium oleate (NaOA), silver nitrate (AgNO 3 ), N-isopropyl acrylamide (NIPAm), L-ascorbic acid, and phenolphthalein were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Chloroauric acid (HAuCl 4 ) was purchased from Shanghai Bojing Chemical Co.,Ltd. (Shanghai, China). Polystyrene microsphere and silicon dioxide microsphere were obtained from Beijing HumaDX Tech Co., Ltd. (Beijing, China). LB agar was purchased from BBI Life Sciences Co., Ltd. (Shanghai, China). Kanamycin (Kana) was purchased from Beijing Solarbio Science&Technology Co.,Ltd. (Beijing, China). The qPCR reagent kit was purchased from TransGen Biotech Co., Ltd. (Beijing, China). Ultrapure water (Millipore system, 18.2 MΩ cm) was used to prepare aqueous solutions throughout the experiments. All other reagents were of analytical grade and used as received. Fabrication of CI-Robot The CI-Robot was inspired and designed by Chlamydomonas, and all the parts of CI-Robot were fabricated by a projection micro stereolithography 3D printing machine (nanoArch S140, BMF Material Technology Inc., Shenzhen, China) with high temperature resistant resin (HTL, BMF Material Technology Inc., Shenzhen, China). The spatial resolution for the 3D printing fabrication was 10 µm and the layer thickness was 100 µm. A 405 nm 55.4 mW/cm 2 ultraviolet light source was employed to cure the resin material, and the exposure time during the printing was 1 s. To facilitate the removal of support materials for 3D printing, the CI-Robot was divided into the front part, microporous barrier part and back part in the middle. After removing excess support material, the CI-Robot was bonded from the front part, microporous barrier part and back part using photo-curing glue in a clean room. Each CI-Robot weighs 0.0381 g after assembly and was soaked overnight in ultrapure water to test the tightness of the bonding. Preparation of magnetic navigation system The magnetic navigation system consisted of a magnetic gel installed on the CI-Robot and an external magnetic field control device. The magnetic gel was synthesized by dispersing ferric oxide nanoparticles (Fe 3 O 4 NPs) in polyacrylamide gel. Briefly, 0.162 g FeCl 3 , 0.994 g FeCl 2 ·4H 2 O, and 1.47 g C 6 H 5 Na 3 O 7 ·2H 2 O were dissolved in 40 mL ultrapure water and heated to 90 ℃ with 800 rpm. Following, 10 mL 0.24 g/mL NaOH solution were added and maintain the reaction temperature at 90 ℃ for 2 h. The product was purified three times by centrifugation with ultrapure water at 10000 rpm for 20 min and redissolved in 50 mL ultrapure water as Fe 3 O 4 NPs solution for future use. 7 g AAm, 0.3 g BIS, and 3 g PVP were dissolved in 30 mL the prepared Fe 3 O 4 NPs solution and stirred at 500 rpm for 10 min. Subsequently, this precast gel was stored in 4 ℃ for 24 h to complete cross-linking. Finally, 5 mg prepared magnetic gel was filled in mounting hole of the CI-Robot and dry at 60 ℃ for 2 h to make sure magnetic gel adhere. The external magnetic field control device was composed of a three-dimensional moving platform with stepper motors. A magnetic pole array with a diameter of 5 mm was arranged in this platform. Therefore, the magnetic navigation field could be constructed by controlling the movement of the 3D mobile platform. Preparation of photoresponse channel switch The photoresponse channel switch, which was synthesized by dispersing gold nanorods (AuNRs) in polyisopropylacrylamide gel, was used for remote controlling the drive mode of the CI-Robot. Briefly, 0.3645 g CTAB and 2.3 mg of NaBH 4 were dissolved in 10 mL 0.25 mM HAuCl 4 with 1500 rpm for 2 min and stood for 0.5 h as seed solution. 1.4 g CTAB, 0.2468 g NaOA and 3.3 mg of AgNO 3 were dissolved in 100 mL 0.5 mM HAuCl 4 with 500 rpm for 1.75 h, then 0.3 mL HCl was added with 500 rpm for another 0.25 h. Subsequently, 0.25 mL 64 mM L-ascorbic acid solution and 0.2 mL seed solution were added with 1500 rpm for 1 min, and the mix solution was stood in a 30 ℃ water bath for 12 h. The product was purified three times by centrifugation with ultrapure water at 10000 rpm for 20 min and redissolved in 50 mL ultrapure water as AuNRs solution for future use. 10 g NIPAm, 0.7 g AAm, 0.3 g BIS, 3 g PVP and 0.6 mL 10wt% TPO-L ethanol solution were dissolved in 30 mL the prepared AuNRs solution with dark stirring 500 rpm for 10 min. Following, this precast photoresponse gel was stored in 4 ℃ with dark for future use. After the fuel was loaded in the CI-Robot, 0.2 µL the precast photoresponse gel was used to block the outlet of channel of the CI-Robot and then complete the preparation of photoresponse channel switch after 30 s of ultraviolet light irradiation. Fabrication of flume and pipeline model The annular pipe flume, cylinder array flume and circular labyrinth flume were designed by AutoCAD, and all the flumes were fabricated by a melt deposition 3D printing machine (Neptune 3 Pro, ELEGOO intelligent technology Inc., Shenzhen, China) with polylactic acid wire (ELEGOO PLA wire, ELEGOO intelligent technology Inc., Shenzhen, China). The temperature of sprinkler head was 215 ℃ and baseplate was 65 ℃. The complex pipeline model was fabricated by assembling transparent acrylic pipe with 15 mm inner diameter and plate with thickness 2.5 mm using photo-curing glue. Culture of Chlamydomonas Chlamydomonas were inoculated into a conical bottle containing 100 mL sterile SE medium in a super-clean table and placed in a light incubator for expansion culture. The culture conditions were as follows: temperature 25℃, light intensity 2000 Lux, light-dark cycle ratio 16 h: 8 h, hand shaking 6 times a day and randomly changing the position of the conical bottle. Movement analysis of CI-Robot The movement behaviors of the CI-Robot were filmed with NIKON D780 camera in various flumes and pipeline model. Ethanol was encapsulated in the CI-Robot before the experiment and using photoresponse channel switch to block the outlet according to the design of motion mode. Programmable trajectories were designed by magnetic navigation system, which has been reported in our previous study. In short, a three-dimensional robotic arm carrying a strong magnetic magnet was moved to build the desired field gradient by preset trajectory. The magnetic field strength was determined by the distance between the magnet and the driving plane. All experiments were carried out at room temperature, and the flume was filled with tap water for propulsion experiments. The movement videos were analyzed by Tracker.jar to record the corresponding position coordinates frame by frame in the Open-Source Physics Java framework. The data acquired were used to calculate displacement, velocity, acceleration, rotation angle, angular velocity and angular acceleration. Cargo delivery experiments of CI-Robot The cargo delivery experiments of CI-Robot were demonstrated by transporting small goods, phenolphthalein indicator and antibiotics. In a small goods transportation experiment, a black disc as the goods model was bonded to the flagellar channel outlet of the CI-Robot through photoresponse channel switch. When the CI-Robot moved to the designated area under magnetic navigation, the goods were released by the near-infrared laser (2.5 W/cm 2 ) illuminated switch. In the phenolphthalein indicator transportation experiment, 3 µL 0.01g/mL phenolphthalein ethanol solution was loaded in the CI-Robot and using photoresponse channel switch to block all outlet of the CI-Robot. After the CI-Robot was run through the pipeline to the pool with different pH environments, the indicator was released by the near-infrared laser (2.5 W/cm 2 ) illuminated switch. The color changes in the pool were recorded by NIKON D780 camera and quantified the gray value by Image J software. This experiment was repeated 3 times for each group. In antibiotics transportation experiment, 3 µL 50 mg/mL Kana ethanol solution and 3 µL 50 mg/mL Kana water solution was loaded in the CI-Robot respectively. The E. coli solution with 10 2 CFU/mL and 10 4 CFU/mL were used to construct a pipeline model. After the CI-Robot released antibiotics for 5 minutes, 5 mL of water was taken from the pipeline and incubated for 6 hours, then evenly spread on LB agar and allowed to grow for 24 hours. Finally, the obtained bacteria trays were photographed by the gel imager and counted the colonies by Image J software. Each experiment was repeated three times. The antibacterial rate was calculated by the following formula: $$\:Antibacterial\:rate\:\left(\%\right)=\left(1-\frac{{A}_{t}}{{A}_{0}}\right)\times\:100\%$$ 7 Where A 0 is the bacterial colony of the blank group, and A t is the bacterial colony of the experimental group or control group. Sampling experiments of CI-Robot The pipeline sampling experiment of CI-Robot was demonstrated by microplastics sampling and microbial sampling. Firstly, polystyrene microspheres and silica microspheres with different sizes as microplastic models, and ampicillin resistant E. coli as bacterial models were pre-placed in the pipeline respectively. The CI-Robot runs in the pipeline and collects samples through strong fluid mass transfer of the Marangoni effect. Subsequently, the CI-Robot, which had completed the sampling task, was removed and carefully wiped the residual water on the surface. The collected samples were obtained from the channel of CI-Robot with 100 µL eluent. In microplastics sampling experiment, the CI-Robot after finishing microsphere sampling task was observed in situ by scanning electron microscope. As another appraisal, the collected samples in CI-Robot were eluted with ultrapure water and counted by flow cytometry. To verify the accuracy of microsphere sampling, a comparison test between immediate sampling analysis and delayed sampling analysis was conducted. Simply, 10 5 number/mL polystyrene microspheres with low scattering light (PS-L) as the sample solution and 10 3 number/mL polystyrene microspheres with high scattering light (PS-H) as the eluent solution were carried in this experiment. For immediate sampling analysis, after finishing microsphere sampling task the CI-Robot was taken out soon and eluted the samples for flow cytometry. For delayed sampling analysis, after finishing microsphere sampling task the CI-Robot was placed in ultrapure water for 30 min and then eluted the samples for flow cytometry. Since the concentration of PS-H is identical, the leakage degree of PS-L which sampled by the CI-Robot in delayed process can be reflected by the proportion of the two gates in flow cytometry pattern. This experiment was repeated eight times. In microbial sampling experiment, the collected samples in CI-Robot were eluted with pH = 7.5 PBS solution and characterized by real-time fluorescence quantitative polymerase chain reaction (qPCR) assay and standard plate count. In qPCR assay, the forward primer sequence of ampicillin resistance gene (Amp-F) was TTACCAATGCTTAATCAGTGAGGCAC, and the reverse primer sequence of ampicillin resistance gene (Amp-R) was ATGAGTATTCAACATTTCCGTGTCGC. For delayed sampling analysis, the CI-Robot which finishes bacterial sampling task was placed in pH = 7.5 PBS solution for 30 min and then eluted the samples for qPCR assay. In standard plate count, 100 µL collected samples were evenly coated on LB agar and incubated with 37.5 ℃ for 24 h. Finally, the obtained bacteria trays were photographed by the gel imager and counted the colonies by Image J software. This experiment was repeated five times. Numerical simulation of fluid dynamics In the research of the CI-Robot motion mechanism, the 2D fluid dynamics of the CI-Robot under different driving forces were established, meshed, and simulated by COMSOL Multiphysics 6.1. The multi-physics simulation of fluid-structure interaction includes k-ε turbulent flow module, solid mechanics module and moving mesh deforming domain module. Where the calculation domain of the straight motion was 100 mm × 20 mm rectangle region, and the calculation domain of the rotational motion was Φ50 mm round region. The wall around calculation domain were open boundaries and set 0 Pa press point constraint at the corner of the wall. Numerical simulation of ethanol diffusion flux In the research of the ethanol diffusion flux, the motion parameters of CI-Robot as the boundary conditions were used to simulate the ethanol concentration distribution. Transport of diluted species modules and moving mesh modules were used in this simulation. Where the calculation domain was same as the fluid dynamics simulation, the initial value of ethanol was 17.2 M, and the velocity of the model was defined by the moving mesh module. In numerical solution, the driving force generated by the Marangoni effect ( F m ) was obtained by integrating the surface tension (γ, Fig. S28): $$\:{F}_{m}={\oint\:}_{L}^{\:}{\gamma\:}_{2}\:dL-{\oint\:}_{L}^{\:}{\gamma\:}_{1}\:dL$$ 8 $$\:\gamma\:=0.07202-0.01236{ln}\left(1+0.5842{C}_{Ethanol}\right)$$ 9 Where L is the contact line between drive system and fluid, γ 1 is the surface tension at the L, γ 2 is the surface tension at parallel to the L with 1 mm, C Ethanol is the concentration of ethanol. Statistical analysis All Data were presented as mean ± standard deviation unless otherwise noted. Differences between groups were compared by analysis of one-way analysis of variance using Origin software (OriginLab, Northampton, MA, USA). Results were considered statistically significant when P < 0.05. The sample numbers for each statistical analysis were listed in the methods section and figure legends. Declarations Acknowledgments This work is supported by the National Natural Science Foundation of China (Grant No. 52273305 and 32271469), National Natural Science Foundation of China under the Basic Science Center Program for "Space Robot Intelligent Manipulation" (Grant No. T2388101), Natural Science Foundation of Xiamen, China (Grant No. 3502Z20227010), Natural Science Foundation of Fujian Province of China (Grant No. 2023J05012), Fundamental Research Funds for the Central Universities (Grant No. 20720230037), State Key Laboratory of Vaccines for Infectious Diseases, and Xiang An Biomedicine Laboratory (Grant No. 2023XAKJ0103071). Author contributions L.L., M.W., and L.R. contributed to the conception of the work; L.L., G.W., and H.S. conceived the experimental method; L.L., W.L., and M.W. performed the experiments; L.L. and L.H. contributed to data processing and analysis; M.W. and L.R. administrated the project; H.S., M.W., and L.R. supervised the study; L.L. and L.H. discussed the results and contributed to the writing of the original draft; L.L. and M.W. contributed to the writing, review & editing of the original manuscript. Competing interests Authors declare that they have no competing interests. Data availability All data are available in the manuscript or the supplementary materials. Additional information Supplementary information The online version contains supplementary material available at xxx. Correspondence and requests for materials should be addressed to Miao Wang, Hao Sun or Lei Ren. Peer review information Nature Communications thanks all reviewers for their contribution to the peer review of this work. A peer reviewer file is available. Reprints and permission information is available at http://www.nature.com/reprints. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Chen, Y. et al. A biologically inspired, flapping-wing, hybrid aerial-aquatic microrobot. Sci. Robot. 2 , 5619 (2017). Wang, D. et al. Vibration-induced-flow mechanism and its application in water surface robot. Research 7 , 0449 (2024). Li, L. et al. Aerial-aquatic robots capable of crossing the air-water boundary and hitchhiking on surfaces. Sci. Robot. 7 , 6695 (2022). Hartmann, F. et al. Highly agile flat swimming robot. Sci. Robot. 10 , 0721 (2025). Zhang, J. et al. Self-propelled nanocellulose aerogel eco-robots for self-powered aquatic environment perception. ACS Energy Lett. 9 , 4852-4863 (2024). Hong, C. et al. Wireless flow-powered miniature robot capable of traversing tubular structures. Sci. Robot. 9 , 5155 (2024). Chen, B. W. et al. Cooperative magnetic interfacial microrobot couple for versatile non-contact biomedical applications. Adv. Mater. 37 , 2417416 (2025). Vaquero, T. S. et al. EELS: Autonomous snake-like robot with task and motion planning capabilities for ice world exploration. Sci. Robot. 9 , 8332 (2024). Lv, X., Wang, W., Clancy, A. J. & Yu, H. High-speed, heavy-load, and direction-controllable photothermal pneumatic floating robot. ACS Appl. Mater. Interfaces 13 , 23030-23037 (2021). Piñan Basualdo, F. N., Bolopion, A., Gauthier, M. & Lambert, P. A microrobotic platform actuated by thermocapillary flows for manipulation at the air-water interface. Sci. Robot. 6 , 3557 (2021). Li, Z. W., Myung, N. V. & Yin, Y. D. Light-powered soft steam engines for self-adaptive oscillation and biomimetic swimming. Sci. Robot. 6 , 4523 (2021). Park, S.-J. et al. Phototactic guidance of a tissue-engineered soft-robotic ray. Science 353 , 158-162 (2016). Ma, Y. et al. Bioinspired high-power-density strong contractile hydrogel by programmable elastic recoil. Sci. Adv. 6 , 2520 (2020). Tang, S. et al. Enzyme-powered Janus platelet cell robots for active and targeted drug delivery. Sci. Robot. 5 , 6137 (2020). Ran, H. et al. Programmable ultrasound-mediated swarms manipulation of bacteria–red blood cell microrobots for tumor-specific thrombosis and robust photothermal therapy. Trends Biotechnol. 43 , 868-892 (2024). Li, S. et al. Particle robotics based on statistical mechanics of loosely coupled components. Nature 567 , 361-365 (2019). Li, G. et al. Self-powered soft robot in the Mariana Trench. Nature 591 , 66-71 (2021). Gardi, G., Ceron, S., Wang, W., Petersen, K. & Sitti, M. Microrobot collectives with reconfigurable morphologies, behaviors, and functions. Nat. Commun. 13 , 2239 (2022). Wang, W. et al. Order and information in the patterns of spinning magnetic micro-disks at the air-water interface. Sci. Adv. 8 , 0685 (2022). Han, H. et al. Imaging-guided bioresorbable acoustic hydrogel microrobots. Sci. Robot. 9 , 3593 (2024). Li, S. et al. Jellyfish-inspired visual and sensory bubbling robots with automatic 3D morphable films for underwater environmental interactions. ACS Nano 18 , 20694-20705 (2024). Li, Z. X. et al. Inhalable biohybrid microrobots: a non-invasive approach for lung treatment. Nat. Commun. 16 , 666 (2025). Huang, Y., Guo, J. H., Li, Y. F., Li, H. Z. & Fan, D. E. 2D-material-integrated micromachines: competing propulsion strategy and enhanced bacterial disinfection. Adv. Mater. 34 , 2203082 (2022). Xiong, W. et al. Marangoni-driven deterministic formation of softer, hollow microstructures for sensitivity-enhanced tactile system. Nat. Commun. 15 , 5596 (2024). Ke, X., Yong, H., Xu, F., Ding, H. & Wu, Z. Stenus-inspired, swift, and agile untethered insect-scale soft propulsors. Nat. Commun. 15 , 1491 (2024). Dong, R. F., Cai, Y. P., Yang, Y. R., Gao, W. & Ren, B. Y. Photocatalytic micro/nanomotors: from construction to applications. Acc. Chem. Res. 51 , 1940-1947 (2018). Nguyen, V. D. et al. pH-sensitive magnetic nanoparticle-mediated natural-killer-cell-based microrobots for dual-targeted delivery and induction of pro-inflammatory macrophage polarization. Small Struct. 5 , 2400149 (2024). Zheng, Z. et al. Ionic shape-morphing microrobotic end-effectors for environmentally adaptive targeting, releasing, and sampling. Nat. Commun. 12 , 411 (2021). Wang, Y. et al. Meniscus-climbing system Inspired 3D printed fully soft robotics with highly flexible three-dimensional locomotion at the liquid–air interface. ACS Nano 16 , 19393-19402 (2022). Zheng, Z. et al. Programmable aniso-electrodeposited modular hydrogel microrobots. Sci. Adv. 8 , 6135 (2022). Wang, X. et al. Multistimuli-responsive hydroplaning superhydrophobic microrobots with programmable motion and multifunctional applications. ACS Nano 16 , 14895-14906 (2022). Mao, G. et al. Ultrafast small-scale soft electromagnetic robots. Nat. Commun. 13 , 4456 (2022). Chen, Y., Doshi, N., Goldberg, B., Wang, H. & Wood, R. J. Controllable water surface to underwater transition through electrowetting in a hybrid terrestrial-aquatic microrobot. Nat. Commun. 9 , 2495 (2018). Qing, H. et al. Spontaneous snapping-induced jet flows for fast, maneuverable surface and underwater soft flapping swimmer. Sci. Adv. 10 , 4222 (2024). Wang, Q. et al. Enhanced buoyancy and propulsion in 3D printed swimming micro-robots based on a hydrophobic nano-fibrillated cellulose aerogel and porous lead-free piezoelectric ceramics. Nano Energy 131 , 110254 (2024). Peng, X., Urso, M., Ussia, M. & Pumera, M. Shape-controlled self-assembly of light-Powered microrobots into ordered microchains for cells transport and water remediation. ACS Nano 16 , 7615-7625 (2022). Xu, Z. et al. Bubble-inspired multifunctional magnetic microrobots for integrated multidimensional targeted biosensing. Nano Lett. 24 , 13945-13954 (2024). Mao, J. W., Han, D. D., Zhou, H., Sun, H. B. & Zhang, Y. L. Bioinspired superhydrophobic swimming robots with embedded microfluidic networks and photothermal switch for controllable marangoni propulsion. Adv. Funct. Mater. 33 , 2208677 (2023). Sun, M. et al. Individual and collective manipulation of multifunctional bimodal droplets in three dimensions. Sci. Adv. 10 , 1439 (2024). Wang, X. D., Dai, L. G., Jiao, N. D., Tung, S. & Liu, L. Q. Superhydrophobic photothermal graphene composites and their functional applications in microrobots swimming at the air/water interface. Chem. Eng. J. 422 , 129394 (2021). Lyu, L. X. et al. Bio-inspired untethered fully soft robots in liquid actuated by induced energy gradients. Natil. Sci. Rev. 6 , 970-981 (2019). Ginger, M. L., Portman, N. & McKean, P. G. Swimming with protists: perception, motility and flagellum assembly. Nat. Rev. Microbiol. 6 , 838-850 (2008). Nelson, G. et al. Cells collectively migrate during ammonium chemotaxis in Chlamydomonas reinhardtii. Sci. Rep. 13 , 10781 (2023). Li, Q. W. et al. Magnetically actuated soft microrobot with environmental adaptative multimodal locomotion towards targeted delivery. Adv. Sci. 11 , 2406600 (2024). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryMaterial.pdf Supplementary Materials MovieS1.mp4 Movie S1 MovieS2.mp4 Movie S2 MovieS3.mp4 Movie S3 MovieS4.mp4 Movie S4 MovieS5.mp4 Movie S5 MovieS6.mp4 Movie S6 MovieS7.mp4 Movie S7 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6560275","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":452094695,"identity":"363e801a-b799-4821-9ae2-1e5b5fa39523","order_by":0,"name":"Lei Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYHACxgMMBjYQJg+xeoBa0kjWwnCYBC3m7b0HDnwoOJ+4dkYC44O3bQzy5oS0yJw5l3BwhsFtY7MbCcyGc9sYDHc2ENAiIZFjcJjH4LYcUAubNG8bQ4LBAUJa5N8YHP5jcI4HqIX9N3FaJHgMDjMYHADbwkycFp4cg4M9BsnGZmceNkvOOSdhuIGgFvYzhg9+/LFL3HY8+eCHN2U28gRtQQKMDSAjiFc/CkbBKBgFowA3AABJBj7Idxs3UQAAAABJRU5ErkJggg==","orcid":"","institution":"Xiamen University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ren","suffix":""},{"id":452094696,"identity":"75dc162c-ee1c-48b1-8df5-3c7836e349cb","order_by":1,"name":"Lihuang Li","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"prefix":"","firstName":"Lihuang","middleName":"","lastName":"Li","suffix":""},{"id":452094697,"identity":"498a6a3b-21d7-489b-8741-60c88003328c","order_by":2,"name":"Libing Huang","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Libing","middleName":"","lastName":"Huang","suffix":""},{"id":452094698,"identity":"15c9b6e0-09a8-4459-9fdb-e79c7d695182","order_by":3,"name":"Wenyi Liao","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Wenyi","middleName":"","lastName":"Liao","suffix":""},{"id":452094699,"identity":"77de9f12-6a17-4ca3-8a18-f81c0d06479a","order_by":4,"name":"Guangshan Wang","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Guangshan","middleName":"","lastName":"Wang","suffix":""},{"id":452094700,"identity":"0216707d-c1ad-44c7-8b06-3a3e4aa8b297","order_by":5,"name":"Hao Sun","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Sun","suffix":""},{"id":452094701,"identity":"5f9c2241-35e7-4118-834b-d4060901cce8","order_by":6,"name":"Miao Wang","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-04-30 02:20:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6560275/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6560275/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82078002,"identity":"5d2256f9-74db-471b-a91d-b5ca9af3fc9a","added_by":"auto","created_at":"2025-05-06 14:07:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":725949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic of Chlamydomonas-inspired mini-robot (CI-Robot).\u003c/strong\u003e The CI-Robot is composed of magnetic eye, flagella fuel channel, microporous barrier, rear fuel channel, and buoyancy cavity. Its shape design is based on the drag-reducing shape of Chlamydomonas adapted to fluid movement, and the magnetic eye for remote navigation is constructed by the bionic sensing and navigation of Chlamydomonas eye points. The rate of fuel release can be expressed in terms of flux (\u003cem\u003eJ\u003c/em\u003e). The rotary motion was achieved by the fuel release in flagella fuel channel (\u003cem\u003eJ\u003c/em\u003e\u003csub\u003e\u003cem\u003eFlagella\u003c/em\u003e\u003c/sub\u003e). The rectilinear motion was achieved by the fuel release in rear fuel channel (\u003cem\u003eJ\u003c/em\u003e\u003csub\u003e\u003cem\u003eRear\u003c/em\u003e\u003c/sub\u003e), and hover on the water surface at a rest position was achieved by closed all fuel channel (\u003cem\u003eJ\u003c/em\u003e=0). For the CI-Robot, the relationship between \u003cem\u003eJ\u003c/em\u003e and \u003cem\u003ev\u003c/em\u003e can be described as \u003cem\u003eJ=ρ\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e·(L·∆γ-kv\u003c/em\u003e\u003csup\u003e\u003cem\u003en\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) dt/dm\u003c/em\u003e, where \u003cem\u003ev\u003c/em\u003e is the velocity of the robot. Detailed formula derivation can be seen in Supplementary Materials. Through the strong mass transfer effect of the Marangoni effect and the capillary fluid retention ability, the robot drive mechanism is cleverly combined with the operating mechanism to demonstrate the application of the robot in the fluid sampling and release.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/fae704c5d139ce4fc3cedb07.png"},{"id":82078005,"identity":"f5bae9ff-c8fc-422c-9665-8215e40f9be0","added_by":"auto","created_at":"2025-05-06 14:07:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304554,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe motion characterization of Chlamydomonas and CI-Robot.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) The schematics of Chlamydomonas motion and force analysis. (\u003cstrong\u003eB\u003c/strong\u003e) The measurement of friction moment (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003efC\u003c/em\u003e\u003c/sub\u003e), driving force (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e), and motion power (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e) of ten groups of Chlamydomonas. (\u003cstrong\u003eC\u003c/strong\u003e) The design of the driving force that produces the straight motion of CI-Robot. (\u003cstrong\u003eD\u003c/strong\u003e) Viscous stress distribution on CI-Robot by numerical simulation in straight motion. (\u003cstrong\u003eE\u003c/strong\u003e) Resistance and its pressure resistance component and viscous stress resistance component of CI-Robot at 100 mm/s straight speed under different immersion depths. (\u003cstrong\u003eF\u003c/strong\u003e) The straight motion of optical image of CI-Robot on water-air interface. (\u003cstrong\u003eG\u003c/strong\u003e) Fluid pressure distribution on CI-Robot by numerical simulation in straight motion. (\u003cstrong\u003eH\u003c/strong\u003e) Resistance and its pressure resistance component and viscous stress resistance component of CI-Robot at different straight speed under full immersion. (\u003cstrong\u003eI\u003c/strong\u003e) The design of the driving force that produces the rotational motion of CI-Robot. (\u003cstrong\u003eJ\u003c/strong\u003e) Viscous stress distribution on CI-Robot by numerical simulation in rotation motion. (\u003cstrong\u003eK\u003c/strong\u003e) Resistance moment and its pressure resistance moment component and viscous stress resistance moment component of CI-Robot at 10π rad/s rotational speed under different immersion depths. (\u003cstrong\u003eL\u003c/strong\u003e) The rotational motion of optical image of CI-Robot on water-air interface. (\u003cstrong\u003eM\u003c/strong\u003e) Fluid pressure distribution of CI-Robot by fluid dynamics simulation in rotational motion. (\u003cstrong\u003eN\u003c/strong\u003e) Resistance moment and its pressure resistance moment component and viscous stress resistance moment component of CI-Robot at different rotational speed under full immersion.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/5977a6bb9f7011500fb47acb.png"},{"id":82078006,"identity":"604a0008-f101-4b71-8553-aefbaa2ad0b7","added_by":"auto","created_at":"2025-05-06 14:07:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":461043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe different rotation modes of CI-Robot.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Mode-1 of rotational CI-Robot. (\u003cstrong\u003eB\u003c/strong\u003e) Motion sequence and fluid dynamics simulation of mode-1. Blue arrows represent the direction of flow in which the external solution enters, and ethanol exits in the channel inside the robot. Orange arrows represent the rotation angle and orientation of the CI-Robot at different times. The color map represents the velocity of flow near the CI-Robot ranging from 0 to 0.24 m/s. (\u003cstrong\u003eC\u003c/strong\u003e) The rotational angle of CI-Robot in mode-1 within three separate experiments. The upper left inset is the simple schematic of mode-1. The lower right inset is the movement trajectory of CI-Robot within 1 s. (\u003cstrong\u003eD\u003c/strong\u003e) The instantaneous angular velocity during the motion process of mode-1. (\u003cstrong\u003eE\u003c/strong\u003e) The fitting of angular acceleration and angular velocity during the motion process of mode-1. (\u003cstrong\u003eF\u003c/strong\u003e) The instantaneous driving force moment and resistance moment during the motion process of mode-1. (\u003cstrong\u003eG\u003c/strong\u003e) The ethanol diffusion and (\u003cstrong\u003eH\u003c/strong\u003e) flux simulation of CI-Robot in rotation movement. (\u003cstrong\u003eI\u003c/strong\u003e) The ethanol release volume of CI-Robot in mode-1. (\u003cstrong\u003eJ\u003c/strong\u003e) The rotational angle of CI-Robot in mode-2 within three separate experiments. The upper left inset is the simple schematic of mode-2. The lower right inset is the movement trajectory of CI-Robot within 1 s. (\u003cstrong\u003eK\u003c/strong\u003e) The rotational angle of CI-Robot in mode-3 within three separate experiments. The upper left inset is the simple schematics of mode-3. The lower right inset is the movement trajectory of CI-Robot within 1 s. (\u003cstrong\u003eL\u003c/strong\u003e) Comparison of the angular velocities of the three rotational modes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/788ffa8f8f6789422457d785.png"},{"id":82079550,"identity":"e1889432-563d-4b87-b6d8-ef7f467d4519","added_by":"auto","created_at":"2025-05-06 14:15:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":471493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe different straight modes of CI-Robot.\u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Mode-4 of rotational CI-Robot. (\u003cstrong\u003eB\u003c/strong\u003e) Motion sequence and fluid dynamics simulation of mode-4. Orange arrows represent the rotation angle and orientation of the CI-Robot at different times. The color map represents the velocity of flow near the CI-Robot, which scales bar from 0 to 0.08 m/s. (\u003cstrong\u003eC\u003c/strong\u003e) The displacement of CI-Robot in mode-4 within three separate experiments. The upper left inset is the simple schematics of mode-4. The lower right inset is the movement trajectory of CI-Robot within 0.5 s. (\u003cstrong\u003eD\u003c/strong\u003e) The instantaneous velocity during the motion process of mode-4. (\u003cstrong\u003eE\u003c/strong\u003e) The fitting of acceleration and velocity during the motion process of mode-4. (\u003cstrong\u003eF\u003c/strong\u003e) The instantaneous driving force and resistance during the motion process of mode-4. (\u003cstrong\u003eG\u003c/strong\u003e) The wake wave of CI-Robot in straight mode. (\u003cstrong\u003eH\u003c/strong\u003e) The height of the astern water datum. (\u003cstrong\u003eI\u003c/strong\u003e) The ethanol diffusion simulation of CI-Robot in straight movement. (\u003cstrong\u003eJ\u003c/strong\u003e) The displacement of CI-Robot in mode-5 within three separate experiments. The upper left inset is the simple schematics of mode-5. The lower right inset is the movement trajectory of CI-Robot within 4 s. (\u003cstrong\u003eK\u003c/strong\u003e) The displacement of CI-Robot in mode-6 within three separate experiments. The upper left inset is the simple schematics of mode-6. The lower right inset is the movement trajectory of CI-Robot within 4 s. (\u003cstrong\u003eL\u003c/strong\u003e) Comparison of the velocities of the three straight modes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/3914fa985ccb2b507625715f.png"},{"id":82078013,"identity":"dd45dd43-9cb4-46fb-8ac9-dc64bb9035a8","added_by":"auto","created_at":"2025-05-06 14:07:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":411830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe collective motion control of CI-Robot.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Force analysis of CI-Robot in collective rotational mode. (\u003cstrong\u003eB\u003c/strong\u003e) The simulation of fluid velocity at the mediate of separation distance in collective rotational mode. (\u003cstrong\u003eC\u003c/strong\u003e) The repulsive dimensionless force (\u003cem\u003eF/γB\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) caused by the Marangoni effect (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e) and fluid dynamics (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eu\u003c/em\u003e\u003c/sub\u003e) increases with D/R decreasing. Detailed formula derivation can be seen in Supplementary Materials. (\u003cstrong\u003eD\u003c/strong\u003e) Motion sequence of CI-Robot in collective rotational mode. (\u003cstrong\u003eE\u003c/strong\u003e) The velocity of CI-Robot in collective rotational mode at different separation distance to radius ratio (D/R). (\u003cstrong\u003eF\u003c/strong\u003e) Motion sequence of CI-Robot in collective straight mode. (\u003cstrong\u003eG\u003c/strong\u003e) The displacement of CI-Robot in collective straight mode with different array distance in 5 mm/s. (\u003cstrong\u003eH\u003c/strong\u003e) The motion sequence and dynamics simulation of CI-Robot in collective straight mode at flow velocity in 5 mm/s. (\u003cstrong\u003eI\u003c/strong\u003e) The displacement of CI-Robot in collective straight mode at different flow velocity. (\u003cstrong\u003eJ\u003c/strong\u003e) The resistance simulation of CI-Robot in collective straight mode at flow velocity in 5 mm/s.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/862f509b91549ee46413968b.png"},{"id":82078009,"identity":"d1a0c472-484c-48d4-a5fb-48eec212c1c3","added_by":"auto","created_at":"2025-05-06 14:07:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":395040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe multitasking executive of CI-Robot on confined water surface inside pipeline.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Schematic of CI-Robot for pipeline sampling, environment monitoring, and antibacterial treatment. (\u003cstrong\u003eB\u003c/strong\u003e) Motion sequence of CI-Robot in pipeline. (\u003cstrong\u003eC\u003c/strong\u003e) The microsphere absorbed on the channel gully of CI-Robot. (\u003cstrong\u003eD\u003c/strong\u003e) Flow analysis performed immediately after sampling. (\u003cstrong\u003eE\u003c/strong\u003e) Flow analysis performed after the sampled CI-Robot placing in ultrapure water for 0.5 h. (\u003cstrong\u003eF\u003c/strong\u003e) The pH monitoring application of CI-Robot. (\u003cstrong\u003eG\u003c/strong\u003e) Colorimetric quantitative analysis for the result of pH monitoring application. (\u003cstrong\u003eH\u003c/strong\u003e) The qPCR amplification curves of samples obtained by CI-Robot in different bacterial concentrations. (\u003cstrong\u003eI\u003c/strong\u003e) The linear fitting of Ct values of samplesobtained by CI-Robot in different bacterial concentrations. (\u003cstrong\u003eJ\u003c/strong\u003e) The qPCR amplification curve between immediate sample extraction in CI-Robot and delay (soaking for 30 min in PBS) sample extraction for detection. (\u003cstrong\u003eK\u003c/strong\u003e) The flat colony counting and (\u003cstrong\u003eL\u003c/strong\u003e) colony statistics of sample obtained by CI-Robot in different bacterial concentrations. (\u003cstrong\u003eM\u003c/strong\u003e) The antibacterial rate of CI-Robot with different driving solutions.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/c60f6d8deb5e4dfcac25fcb5.png"},{"id":96252908,"identity":"90768431-1e84-4d0b-86f8-2eeaded9d80e","added_by":"auto","created_at":"2025-11-19 07:41:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4026916,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/264f35df-000a-436a-85e3-005ad14ec2e2.pdf"},{"id":82078014,"identity":"0a8d5959-dec2-4fc0-b159-d3dc9f7a0606","added_by":"auto","created_at":"2025-05-06 14:07:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2613491,"visible":true,"origin":"","legend":"Supplementary Materials","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/8e2c40b795ab94a8140b4bcc.pdf"},{"id":82078030,"identity":"e492d474-6d45-44c8-aa1e-efd90048918c","added_by":"auto","created_at":"2025-05-06 14:07:02","extension":"mp4","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28304268,"visible":true,"origin":"","legend":"Movie S1","description":"","filename":"MovieS1.mp4","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/317e3dfc7e741cceb11176b1.mp4"},{"id":82079558,"identity":"be19a812-4ab0-4c08-b3af-53f96a8718e9","added_by":"auto","created_at":"2025-05-06 14:15:02","extension":"mp4","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":26893463,"visible":true,"origin":"","legend":"Movie S2","description":"","filename":"MovieS2.mp4","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/ad5b811dfd5069254b1bd34e.mp4"},{"id":82079560,"identity":"34914358-e1ab-438d-8a45-ac8bcac812c6","added_by":"auto","created_at":"2025-05-06 14:15:02","extension":"mp4","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":27949369,"visible":true,"origin":"","legend":"Movie S3","description":"","filename":"MovieS3.mp4","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/3a508a4010605f113951b766.mp4"},{"id":82078028,"identity":"c2deebfa-d7c4-4ef5-bec8-c3b601511e8b","added_by":"auto","created_at":"2025-05-06 14:07:02","extension":"mp4","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":29243230,"visible":true,"origin":"","legend":"\u003cp\u003eMovie S4\u003c/p\u003e","description":"","filename":"MovieS4.mp4","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/375fb5ea6a0427dda9baa43e.mp4"},{"id":82079563,"identity":"4cdcce9c-6891-4310-bda4-c71dc6b05470","added_by":"auto","created_at":"2025-05-06 14:15:03","extension":"mp4","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":28167082,"visible":true,"origin":"","legend":"Movie S5","description":"","filename":"MovieS5.mp4","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/780d9ccfbf5e5523b9307ae6.mp4"},{"id":82078029,"identity":"c103cea4-d51c-4158-9577-f1c8b02205a1","added_by":"auto","created_at":"2025-05-06 14:07:02","extension":"mp4","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":17236623,"visible":true,"origin":"","legend":"Movie S6","description":"","filename":"MovieS6.mp4","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/89b48d9e143d87c74fb3a8e6.mp4"},{"id":82078032,"identity":"1e4cafe2-edd9-4d5a-b587-485823ae369b","added_by":"auto","created_at":"2025-05-06 14:07:02","extension":"mp4","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":24432139,"visible":true,"origin":"","legend":"Movie S7","description":"","filename":"MovieS7.mp4","url":"https://assets-eu.researchsquare.com/files/rs-6560275/v1/8354d1df98b846005ac63bb9.mp4"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Chlamydomonas-Inspired Water-Air Interface Mini-Robot with Intricate Tectonics, Programmable Locomotion, and Multifunctional Execution","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater-air interface mini-robots\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e represent an emerging frontier in miniature robotics, evolving into a versatile technological platform capable of executing high-precision tasks at the interface between water and air, including water quality monitoring\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, maintenance operations\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, medical interventions\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, sample collection\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and cargo transportation\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Compared to conventional mechanical methods, non-mechanical mini-robots driven by external stimuli\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e are particularly well-suited for confined aquatic environments, as their structures facilitate miniaturization and eliminate the need for airtight enclosures required in electromechanical systems. Additionally, they enable more flexible and non-invasive manipulation of delicate biological cells\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, microparticle capture\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and soft material assembly\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOver the past decade, stimulus-responsive water-air interface mini-robots with diverse structures and functionalities have been developed, leveraging propulsion mechanisms such as magnetic\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, acoustic\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, optical\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, bio-actuation\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, chemical\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and Marangoni propulsion\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These mechanisms generate directional gradient fields or driving forces that guide robots along predictable motion trajectories\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, such robots are still constrained by low energy conversion efficiency (e.g., light-driven systems achieving\u0026thinsp;\u0026lt;\u0026thinsp;5%)\u003csup\u003e26\u003c/sup\u003e, environmental sensitivity (e.g., pH and ionic concentration dependencies) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, limited control precision, response latency, and other performance bottlenecks. Moreover, most existing designs\u0026mdash;such as particles\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, bars\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and thin films\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u0026mdash;feature simplistic structures, which hinder their ability to regulate motion speed or perform complex functional tasks. While simpler structures enhance control precision, task execution often necessitates more intricate designs, posing a fundamental challenge in balancing motion control and task performance\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn terms of locomotion, water-air interface mini-robots must counteract gravitational forces and resist disturbances from water currents while maintaining buoyancy\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Their buoyancy is typically governed by the hydrophobicity of their materials, external geometries, and immersion depths\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. However, existing studies have primarily focused on quantitative buoyancy analysis for mini devices with regular geometry, such as elongated rods\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, prisms\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, and spheres\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, while research on irregularly shaped robots remains scarce. Once stable flotation is achieved, optimizing motion speed and stability becomes a critical objective, as a robot's mobility is determined by the interplay between driving forces and hydrodynamic resistance, with the latter being positively correlated with the robot\u0026rsquo;s geometry and instantaneous velocity\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Under constant geometric conditions, increased propulsion results in higher transient acceleration, ultimately leading to an elevated steady-state velocity once force equilibrium is reached. In current studies on water-air interface mini-robots, the Marangoni effect\u0026mdash;driven by solute release\u0026mdash;is a primary mechanism for generating high propulsion forces\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. However, the hydrodynamic disturbances induced by Marangoni-driven flows can significantly disrupt the robot\u0026rsquo;s directional stability\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Therefore, achieving a balance between propulsion force, motion resistance, and hydrodynamic disturbances is a critical challenge in designing high-performance water-air interface mini-robots.\u003c/p\u003e \u003cp\u003eTo address these challenges, we present a Chlamydomonas-inspired miniature robot (CI-Robot) that integrates a non-mechanical stimulus-responsive system with a complex internal structure while enabling multimodal motion control (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This novel design allows precise and programmable execution of multiple tasks, significantly advancing the capabilities of non-mechanical mini-robots. The CI-Robot\u0026rsquo;s shape is inspired by Chlamydomonas who exemplifies nature\u0026rsquo;s solutions to micro-scale energy autonomy, motion control and environmental adaptation. The robot incorporates an internal buoyancy chamber, flagella fuel channels, a microporous barrier, and a rear fuel channel. By optimizing the shape and buoyancy cavity design, the load capacity of the CI-Robot is significantly enhanced. Moreover, various motion patterns\u0026mdash;including rotary motion (\u003cem\u003eJ\u003c/em\u003e\u003csub\u003e\u003cem\u003eFlagella\u003c/em\u003e\u003c/sub\u003e\u0026gt;0), variable rectilinear motion (\u003cem\u003eJ\u003c/em\u003e\u003csub\u003e\u003cem\u003eRear\u003c/em\u003e\u003c/sub\u003e\u0026gt;0), and static equilibrium (\u003cem\u003eJ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0)\u0026mdash;are achieved by regulating fuel flux (\u003cem\u003eJ\u003c/em\u003e) through different channel exits. To improve maneuverability and remote navigation, we integrate magnetic eye structures using a magnetic gel-based material. Additionally, we leverage the strong mass transfer effect induced by the Marangoni effect to develop interactive functionalities, such as controlled content release and environmental sampling. Notably, through the synergistic action of strong mass transfer and the capillary liquid retention effect, the CI-Robot successfully captures and retains both non-living microplastics and live bacteria, demonstrating its potential for advanced environmental and biomedical applications.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStructure and motion design of CI-Robot\u003c/h2\u003e \u003cp\u003eThe CI-Robot was inspired by the morphology and kinematic characteristics of Chlamydomonas\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The complex locomotion behavior of Chlamydomonas could be divided into rotational and straight mode (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). For rotational mode, the flagellum of Chlamydomonas beat with different phases and orientation. The unbalanced beating efficiency caused Chlamydomonas to produce a total tug force (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eCr\u003c/em\u003e\u003c/sub\u003e), which application point was away from the center of body. Since the Chlamydomonas was in a low Reynolds number fluid, there must be a viscous friction force (\u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eCr\u003c/em\u003e\u003c/sub\u003e) and a viscous friction moment (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003efC\u003c/em\u003e\u003c/sub\u003e) opposite to the direction of rotation under the equilibrium of forces. For straight mode, the flagellum of Chlamydomonas beat with synchronous phase and orientation. The symmetric beating efficiency also caused Chlamydomonas to produce a total tug force (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eCs\u003c/em\u003e\u003c/sub\u003e), but which application point is located in the center of body. Similarly, the fluid provides a viscous frictional force (\u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eCs\u003c/em\u003e\u003c/sub\u003e) in the opposite direction of straight. The detailed discussion about morphology (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and locomotion behavior (Fig. S2) of Chlamydomonas was shown in Supplementary Materials with corresponding text illustration. During the rotational and straight motion of Chlamydomonas, the total tug force (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e) and total power (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e) are similarly distributed in these two motion modes (5\u0026thinsp;~\u0026thinsp;15 pN, 0.5\u0026thinsp;~\u0026thinsp;1.5 fW), but the total friction moment (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003efC\u003c/em\u003e\u003c/sub\u003e) only has a large value in the rotation mode (40\u0026thinsp;~\u0026thinsp;90 aN\u0026middot;m) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). From the analysis data, we can conclude that Chlamydomonas switched their swimming modalities by adjusting the application point of total tug force. With the complex coupling of straight and rotation, the Chlamydomonas can exhibit abundance of modalities in swimming behavior that enables them to adapt to the complex physical environment (Movie S1). Considering the workspace of the actuator and the morphology of Chlamydomonas, the CI-Robot was designed as an ellipsoid whose length was 6 mm with two flagella (Fig. S3A). Moreover, we also designed magnetic eyes for navigation, and buoyancy chamber for reducing resistance (Fig. S3B). Inspired by the application point of total tug force of Chlamydomonas, we designed the structure and motion mode of CI-Robot that can rotate by an off-center driving force generating through the flagella channel or moving forward by an on-center driving force generating through the rear channel. The flagella channel was separated from the rear channel by a microporous barrier. By loading ethanol in different channel, the CI-Robot could produce different driving effects that was as same as the rotational and straight motion of Chlamydomonas. When the rear channel was loaded with ethanol, due to the capillary pressure of the microchannels in microporous barrier, ethanol was difficult to diffuse into the flagella channel and only be released by the rear channel exit. At this time, the released ethanol formed a concentration gradient at the rear channel exit, which generated a driving force (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eRs\u003c/em\u003e\u003c/sub\u003e) on the rear channel exit. As the robot moves, the fluid generates a friction force (\u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eRs\u003c/em\u003e\u003c/sub\u003e) in the opposite direction of the motion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Since the direction of \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eRs\u003c/em\u003e\u003c/sub\u003e passed through the CI-Robot center, the CI-Robot generated a straight motion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eFurther, we studied the influence of the flagella shape of CI-Robot on the fluid resistance through simulation calculation and optimized the morphology of the flagella design (Fig. S4). The numerical simulation of fluid dynamics models and computational formulas are shown in Supplementary Materials. Firstly, we explored the influence of flagella epitaxial angle (\u0026#120579;) on fluid resistance. With the increase of flagella epitaxial angle, the fluid pressure of CI-Robot was mainly distributed in the front face of the model and the position of flagella (Fig. S5A), and the viscous stress per unit area was mainly concentrated in the end of flagella (Fig. S5B). Additionally, the resistance of CI-Robot gradually decreases with the increase of the epitaxial angle and tends to be flat when it was greater than 5π/6 (Fig. S6). Then we explored the influence of flagella bending radius (\u003cem\u003eR\u003c/em\u003e) on fluid resistance. The maximum fluid pressure of CI-Robot was insensitive to the flagella bending radius (Fig. S7A). The distribution of viscous stress per unit area remained largely unchanged when the model body contour design parameters were fixed. However, the viscous stress at the flagellum's end decreases as the flagellar bending radius increases. (Fig. S7B). However, with the increase of the flagellar bending radius, the projected area of the flagella on the incoming flow increased, which leads to an increase of pressure resistance (Fig. S8). Finally, we explored the influence of flagella length (\u003cem\u003eL\u003c/em\u003e) on fluid resistance. Similarly, the maximum fluid pressure of CI-Robot was insensitive to the flagella length (Fig. S9A), but the viscous stress per unit area became more concentrated distribution on the end of flagella with the increase of the flagella length (Fig. S9B). This results in a linear increase in resistance as the flagella length increases, which was mainly due to the linear increase of the extension segment of the flagella (Fig. S10). By comparing the numerical solutions of the fluid resistance, the epitaxial angle and bending radius of the flagella mainly affect the fluid resistance, while the length of the flagella mainly affects the concentrated distribution of stress points. Based on the above studies, we proposed \u0026#120579;=5/6\u0026#120587;, \u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2 mm, and \u003cem\u003eL\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.8 mm as parameters for the shape design of CI-Robot. In this optimized design, the distribution of viscous stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) and pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG) of the CI-Robot in straight motion at 100 mm/s was mainly concentrated in the front of the flagella. The fluid resistance increased gradually with the immersion depth (\u003cem\u003eH\u003c/em\u003e) of the CI-Robot, and the fluid resistance increased slightly when the immersion depth exceeds half of the CI-Robot thickness (i.e. \u003cem\u003eH\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;2.5 mm) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Under full immersion, the fluid resistance of the CI-Robot increases as a power function with the increase of motion speed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). The change of fluid resistance conforms to the fitting of the hydrodynamic equation:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:f=0.01{v}^{1.64}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn addition, the fluid resistance of the CI-Robot in straight motion (\u003cem\u003ef\u003c/em\u003e) was mainly provided by the pressure resistance (\u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e), and the proportion of viscous resistance (\u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e) can be ignored.\u003c/p\u003e \u003cp\u003eWhen the flagella channel was loaded with ethanol, the unbalanced placement of flagella channel in the process of placing CI-Robot on water would generate a small gravitational potential energy difference, which induces the higher flagella channel exit to release ethanol while the lower flagella channel exit to indrawn environmental water due to the capillary action. At this time, the released ethanol formed a concentration gradient at the flagella channel exit, which generated a driving force (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eRr\u003c/em\u003e\u003c/sub\u003e) on the flagella channel exit. The friction force (\u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eRr\u003c/em\u003e\u003c/sub\u003e) and the friction moment (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003efR\u003c/em\u003e\u003c/sub\u003e) generated by the fluid during the rotation of robot change with the motion speed of the rotation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Since the direction of \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eRr\u003c/em\u003e\u003c/sub\u003e deviated from the CI-Robot center, the CI-Robot generated a rotational motion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL). In the rotational motion with 10π rad/s, the distribution of viscous stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ) and pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eM) of the CI-Robot was mainly concentrated at the end of the flagella. The fluid resistance moment increased significantly before the immersion depth reached half of the CI-Robot thickness (i.e. \u003cem\u003eH\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2.5 mm), and unchanged basically after the immersion depth reached half of the CI-Robot thickness (i.e. \u003cem\u003eH\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;2.5 mm) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK). Under full immersion, the fluid resistance moment of the CI-Robot increases as a power function with the increase of rotational speed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eN). The change of fluid resistance moment conforms to the fitting of the hydrodynamic equation:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{M}_{f}=0.31{\\omega\\:}^{1.64}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSimilarly, the fluid resistance moment of the CI-Robot in rotation motion (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e) was provided by the pressure resistance moment (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003efp\u003c/em\u003e\u003c/sub\u003e) rather than viscous resistance moment (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003efk\u003c/em\u003e\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eAccording to this design, we manufactured the front and back components of the CI-Robot using a high temperature resistant resin through 3D projection microstereolithography. The front component is composed of flagella fuel channel, magnetic eyes chamber, buoyancy chamber and fuel storage chamber in the front half, while the back component consists of rear fuel channel, microporous barrier, buoyancy chamber and fuel storage chamber in the back half. Under microscopic observation, the printing effect of each channel of the component is consistent with the design. Following the CI-Robot was microassembled to encapsulate the two components through light curing glue. The buoyancy chamber and fuel storage module of CI-Robot was assembled into closed compartments. Under the combined action of surface tension and buoyancy, the CI-Robot was able to float stably on water-air interface (Fig. S11).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe rotation mode of CI-Robot\u003c/h3\u003e\n\u003cp\u003eThe above analyses provided a decouple of the swimming performance of the CI-Robot, which was influenced by various kinematic parameters. Based on the rotational behavior generated by the fuel flux of flagella fuel channel, three preset channel programs were designed to generate the Chlamydomonas-inspired multimodal rotational swimming gait including mode-1, mode-2 and mode-3. During the mode-1 motion of the CI-Robot, all channels were open, where ethanol was outflows from the flagella channel on one side, and water in the environment inflows the fuel storage compartment from the other flagella channel and rear channel (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The CI-Robot in mode-1 rotates continuously (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and completes 30 laps within 20 s (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Movie S2). In this typical rotational motion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), after 0.08 s variable angular acceleration (T1 stage), the CI-Robot was transformed into high-speed uniform rotational movement (T2 stage) and then performed variable angular acceleration movement (T3 stage) and adjusted low speed uniform rotational movement (T4 stage). The driving moment (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eF\u003c/em\u003e\u003c/sub\u003e) of the CI-Robot during its rotational motion meets the following requirements:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{M}_{F}-{M}_{f}=I\\alpha\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eI\u003c/em\u003e is the inertia moment of CI-Robot, which measured by modeling software as 3.89\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e kg∙m\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eα\u003c/em\u003e is the instantaneous angular acceleration of the robot. According to Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the relationship between angular acceleration and angular velocity under rotational motion could be described as:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{\\omega\\:}^{1.64}=-\\frac{I\\alpha\\:}{0.31}+\\frac{{M}_{F}}{0.31}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTherefore, the instantaneous driving moment and resistance moment of CI-Robot for rotational motion could be obtained by iterated angular acceleration and angular velocity into Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F). According to the fitting results of each lap, the driving moment was much greater than the resistance moment before the ten turns, then became equal after the ten turns. That is to say, the driving moment and resistance moment of CI-Robot in rotational motion reach equilibrium in a very short time. Moreover, the rotation of CI-Robot can be started with a small driving moment (~\u0026thinsp;120 nN\u0026middot;m) and maintained the constant rotation with a smaller force moment (~\u0026thinsp;20 nN\u0026middot;m). Further, to reveal the reason of the rapid reduction of driving moment in the rotation process, the release of ethanol was simulated by finite element analysis. The simulation analysis results demonstrated that the ethanol concentration of solutions near the flagella channel outlet increases with the increase of the laps of CI-Robot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). This phenomenon was caused by the fact that the ethanol diffusion was uncoordinated with the robot rotation speed. After a quick rotation of the CI-Robot, the ethanol released in the initial position had not fully diffused, so the concentration gradient near the flagella channel outlet gradually decreases after each rotation. According to Fick's law (equation S1) and the Marangoni effect formulation (Eq.\u0026nbsp;\u003cspan refid=\"Equ8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Equ9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) (38, \u003cem\u003e41\u003c/em\u003e), the flux \u003cem\u003eJ\u003c/em\u003e and the surface tension gradient \u003cem\u003e∆γ\u003c/em\u003e are proportional to the concentration gradient. So as the concentration gradient decreases, \u003cem\u003eJ\u003c/em\u003e and \u003cem\u003ed\u003c/em\u003e decrease, \u003cem\u003ev\u003c/em\u003e decreases according to equation (S2). So as the concentration gradient decreases, \u003cem\u003eJ\u003c/em\u003e and \u003cem\u003e∆γ\u003c/em\u003e decreases which cause \u003cem\u003ev\u003c/em\u003e decreases according to \u003cem\u003eJ\u0026thinsp;=\u0026thinsp;ρ\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026middot;(L\u0026middot;∆γ-kv\u003c/em\u003e\u003csup\u003e\u003cem\u003en\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) dt/dm\u003c/em\u003e. After several laps, the concentration gradient became stable as the number of laps increased, and \u003cem\u003ev\u003c/em\u003e was also stabilized (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Therefore, in the rotation mode, the driving moment of the CI-Robot decreased rapidly first and then tended to be stable with the increase of the number of laps. This phenomenon is further proved by the coincidence of ethanol emission in experiment and simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003eFor mode-2, the bilateral flagella fuel channel was open and the rear fuel channels were closed (Fig. S12A). Thus, a continuous rotation with a slightly faster velocity like mode-1 is produced (Fig. S12B) and rotated 30 laps within 20 seconds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ, Movie S2). However, when only one of the flagella fuels channels was open in mode-3 (Fig. S13A), and the CI-Robot exhibited intermittent motion (Fig. S13B). Due to the long and vapidity deceleration rotation, the CI-Robot only rotated 1.5 laps in 20 seconds in mode-3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK, Movie S2). By comparing the angular velocities of the three rotational motions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL), the angular velocity of mode-1 and mode-2 increases from 0 rad/s to the maximum of 10π rad/s, then maintained for a period before 2.5 s, and finally maintained with a lower and stable angular velocity of 2π rad/s. In the intermittent motion in mode-3, the intervals were about 2 s, consisting of a rapid rotation of 0.4 s and a deceleration of 1.6 s. These results show that the rotation speed of CI-Robot could be controlled by controlling the channel exit mode. In addition, we also adjusted the rotation velocities of the CI-Robot by the control of different fuels, different concentrations ethanol and different viscosity solution (Fig. S14). Detailed discussion is provided in Supplementary Materials. Driven by 100% ethanol fuel, the average angular velocity of mode-1 CI-Robot in aqueous solution can reach 8.98π rad /s, which is 4.24 times the average angular velocity of Chlamydomonas (Fig. S15).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eThe straight mode of CI-Robot\u003c/h3\u003e\n\u003cp\u003eAdjusting the mode of channel exit also enables to control the straight mode of CI-Robot. In mode-4, ethanol was filled in the rear fuel channel of the CI-Robot, while the double-flagellar fuel channel remained empty (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Currently, the external solution was sucked in from the flagellar channel outlet, and ethanol fuel was released from the tail channel outlet. At this point, the CI-Robot started moving rapidly in a straight line (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Movie S3), covering approximately 20 mm in 0.5 s (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In this typical straight motion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), the CI-Robot transformed into high-speed uniform motion (T2 stage) after 0.08 s variable acceleration motion (T1 stage). However, since the driving force of Marangoni effect decreased rapidly in the early stage, which was insufficient to balance the fluid resistance, the CI-Robot further performed variable acceleration motion (T3 stage) and then adjusted the low-speed uniform motion (T4 stage). The driving force (\u003cem\u003eF\u003c/em\u003e) of the CI-Robot during its straight motion meet the following requirements:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:F-f=ma$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003em\u003c/em\u003e is the mass of CI-Robot, which measured by modeling software as 0.0385 g, \u003cem\u003ea\u003c/em\u003e is the instantaneous acceleration of the CI-Robot. According to Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the relationship between acceleration and velocity under straight motion could be described as:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{v}^{1.64}=-\\frac{ma}{0.01}+\\frac{F}{0.01}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTherefore, the instantaneous driving force and resistance of CI-Robot for straight motion could be obtained by iterated acceleration and angular velocity into Eq.\u0026nbsp;(\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F). The driving force was greater than the fluid resistance in the T1 stage when CI-Robot started, which corresponds to the rapid increase of the motion speed of the CI-Robot. At the T3 stage, the rapid reduction in driving force was insufficient to sustain the CI-Robot's resistance during high-speed movement at the T2 stage, resulting in variable acceleration and deceleration. From the T2 to T4 stage, the driving force was almost in a flat state with fluid resistance, which corresponds to the relatively uniform movement of the CI-Robot. Similarly to rotational motion, the driving force and resistance of CI-Robot in straight motion reach equilibrium in a very short time.\u003c/p\u003e \u003cp\u003eIn addition, the measurement of the surface height when the CI-Robot moves indicated that the CI-Robot presents a unique form of undulation propulsion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). The wake flow pattern induced by the CI-Robot is consistent with the Chlamydomonas. The surface wave at 2 mm from the rear outlet has an amplitude of 0.3 mm and a period of 6 ms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). Based on this, the ethanol release during the CI-Robot's straight motion was simulated. The analysis showed that the released ethanol diffuses along the CI-Robot's trajectory \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). From the trajectory of the ethanol concentration distribution, the ethanol concentration increases with decreasing distance at the outlet, specially producing a sharp increase within 10 mm. The large ethanol concentration gradient near the outlet is the reason for the fast movement of the CI-Robot. In addition, when the CI-Robot started, the flux at the outlet of rear fuel channel dropped rapidly and remain unchanged, indicating that the CI-Robot quickly switches to approximately uniform motion after a brief variable acceleration movement. Therefore, the force at the rear was almost constant during motion in mode-4, and the dynamics of the CI-Robot was completely determined by the flux at the rear fuel channel outlet.\u003c/p\u003e \u003cp\u003eFor mode-5, the flagella channel closed and the rear channel open (Fig. S16A), the CI-Robot exhibits periodic straight motion with a period of about 2.4 s, consisting of rapid in-line movement of 0.4 s and inertial drift with an interval of 2 s (Fig. S16B). Different from mode-4, in this single-opening design, the CI-Robot trajectory fails to maintain a linear trajectory but has a certain lateral deviation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ, Movie S3). This phenomenon occurs because there is no driving force during the inertial drift stage, and the lateral resistance from the fluid boundary effect becomes the dominant factor influencing the motion trajectory. In the mode-6 design of CI-Robot without the flagella channel (Fig. S17A), a similar motion of mode-5 the CI-Robot was exhibited (Fig. S17B). However, due to the lack of flagella orientation, the lateral resistance generated by the fluid changes more and the CI-Robot cannot maintain the stability of the forward direction, thus showing a planar circular motion on the trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK, Movie S3). By comparing the velocities of the three straight motions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eL), the velocity of mode-4 increased from 0 mm/s to the maximum of 120 mm/s, then maintained with a lower and stable angular velocity of 75 mm/s. In the intermittent motion in mode-5 and mode 6, the peak velocity was roughly the same at about 15 mm/s. These results show that the straight speed of CI-Robot could also be controlled by controlling the channel exit mode. Moreover, we also verified that the straight speed of the CI-Robot can be regulated by different fuels, different ethanol concentrations and different viscosity solutions (Fig. S18), which has detailed discussed in Supplementary Materials. Driven by 100% concentration ethanol fuel, the average displacement speed of mode-4 CI-Robot in aqueous solution can reach 11.43 body/s, which is 1.37 times the displacement speed of Chlamydomonas (Fig. S19).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eThe collective motion of CI-Robot\u003c/h2\u003e \u003cp\u003eIn nature, Chlamydomonas often exhibit collective movement\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, demonstrating synergistic performance than individual cells swimming alone. This inspired us to design collective motion of CI-Robot to improve work efficiency. On this basis, we investigate the collective motion of CI-Robot approaching each other under rotating motion. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, two CI-Robots with radius \u003cem\u003eR\u003c/em\u003e respectively perform synchronous anti-directional rotation when the distance is \u003cem\u003eD\u003c/em\u003e. For example, Robot-1 has a driving force \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e11\u003c/em\u003e\u003c/sub\u003e at the exit of its own flagella, while Robot-2 has a reverse driving force \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e21\u003c/em\u003e\u003c/sub\u003e. The angle formed by the two driving directions is related to the phase angle of the CI-Robot at this time. In addition, the CI-Robot itself generates a corresponding resistance torque due to its movement. The force of robot-2 is symmetric with robot-1. From this mechanical state, when two CI-Robots are close to each other, their rotational speed will be restricted by each other. To avoid the influence of motion state between multiple CI-Robots, it is necessary to study the safe working distance between two CI-Robots without mutual restriction.\u003c/p\u003e \u003cp\u003eBased on this, we numerically study the release of ethanol fuel when two CI-Robots approach each other. The results show that when D/R is less than 2.5, the ethanol concentration in the middle water area of the two robots will be significantly higher than that in the outer water area (Fig. S20A), which will lead to the reduction of surface tension in the middle water area (Fig. S20B). In addition, the flow velocity of the intermediate waters is close to rest when D/R is greater than 4, and the flow velocity gradually increases with the decrease of D/R (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The repulsive force against the rotational motion of the two CI-Robots is composed of the Marangoni effect (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e) and the hydrodynamic force (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eu\u003c/em\u003e\u003c/sub\u003e). For comparison, we divide these two forces by the total tension of the CI-Robot in water (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) to obtain a dimensionless force, and the detailed calculation formula shows in the supporting materials (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). As the D/R value decreases, the repulsion force between the two CI-Robots gradually increases. In addition, the increase of \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e is smaller than that of \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eu\u003c/em\u003e\u003c/sub\u003e. This indicates that the hydrodynamic force is the main factor that interferes with the velocity of two CI-Robots when they are near each other. When D/R\u0026thinsp;\u0026gt;\u0026thinsp;4, the repulsive force obstructing the rotational motion is close to 0, that is, the two CI-Robots do not interfere with each other. The results show that the safe working distance between the two CI-Robots is D/R greater than 4. The experimental results also prove this conclusion. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-E (Movie S4), when two CI-Robots run separately at D/R\u0026thinsp;=\u0026thinsp;2, the flagella are close to each other, the rotation speed of both is significantly reduced. The slower moving CI-Robot will further slow down due to the reaction force of the faster CI-Robots. However, when the two CI-Robots run separately at D/R\u0026thinsp;=\u0026thinsp;4, the motion state between them does not affect each other. When the two CI-Robots are close to each other, there is no change in their rotational speed.\u003c/p\u003e \u003cp\u003eIn addition, we also studied the motion and stress state of linear array arranged CI-Robot in fluid through simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). The robot achieves orderly navigation by constructing its own magnetic eye and an external navigation system (Fig. S21). When the fluid velocity is 5 mm/s, the offset distance of CI-Robot decreases with the increase of array spacing (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). When the array spacing reaches 2 times the body length of CI-Robot, the offset distance of CI-Robot does not change, which indicates that the non-influence distance of CI-Robot in linear array arrangement is 6 mm. Under this arrangement of column spacing, we compare the state of CI-Robot in simulation and real experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH, Movie S5). In the real experiment, the arrangement of CI-Robot does not maintain the direction of head to tail as in the simulation, but there is no obvious difference in the displacement distance when the fluid velocity is 5 mm/s. In addition, as the fluid flow rate increases from 0 mm/s to 5 mm/s, the deviation distance of CI-Robot basically maintains a small change (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). In the array arrangement of five CI-Robots, the CI-Robot at the front of the oncoming position is subjected to the greatest resistance, followed by the CI-Robot at the end (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ). These results show that there is no congestion when the CI-Robot is running in the pipeline with an array spacing of 6 mm, which provides control parameters for controlling the efficient movement of the CI-Robot.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProgramed locomotion and multifunctional execution of CI-Robot in complex scenes\u003c/h3\u003e\n\u003cp\u003eThe small size, rapid movement, and complex coupled swimming capabilities make the CI-Robot highly promising for a variety of applications, particularly in programmable complex path planning and cargo handling. We have realized the path planning of the CI-Robot under the programming of the magnetic field. For example, the CI-Robot could move in a straight line and rotates at the specified position. Then finally returns to the starting position according to the planned path (Fig. S22, Movie S6). Besides the magnetic navigation system, the autonomous movement of the CI-Robot in the maze was realized by utilizing the fluid reaction force generated by the boundary conditions (Fig. S23). In order to further verify the obstacle avoidance ability of CI-Robot in complex water scenes, we carried out the obstacle avoidance operation of the robot in the narrow water area of the array cylinder (Fig. S24, Movie S6). Furthermore, we have achieved the fixed-point cargo handling capabilities of CI-Robot (Fig. S25, Movie S6). The detailed operation mode and related discussion of programmatic movement and multifunctional execution of ci robots in these complex scenarios are shown in Supplementary Materials.\u003c/p\u003e\n\u003ch3\u003eUnique applications for narrow water surface in pipelines with CI-Robot\u003c/h3\u003e\n\u003cp\u003eMiniature robots oriented to work in narrow pipes play an important role in pipe monitoring with sophisticated instruments (such as circulating pipe systems in spacecraft), complex and long pipe maintenance (such as industrial pipes), sample collection of pipe water quality (such as water supply pipes), and biomedical procedures (such as therapeutic cargo carrying and release in the intestinal tract) \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. However, moving in narrow pipe environments is always a big challenge for miniature robots, because narrow gaps, curved paths and complex pipe interfaces often occur on navigation paths that prevent miniature robots from achieving exploration goals and operational tasks. Therefore, after successfully controlling the movement behavior control of various complex paths of the CI-Robot, we further explored the application of the CI-Robot in the pipeline for microplastics and bacteria sampling, environmental monitoring and antibacterial (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Movie S7). With the help of the structural design of the CI-Robot's capillary channel, a mass transfer effect occurred when ethanol was released, causing water to be absorbed from the flagella into the CI-Robot's fuel chamber. After the ethanol was released, the water inside the channel was well retained due to the capillary's retention ability. Utilizing this feature, we demonstrated the sampling of microplastics in a curved pipe. In a pipeline model, the pipeline was filled with microsphere solutions of different concentrations, sizes, and materials as a microplastic model. After loading fuel, the CI-Robot was magnetically guided into the curved pipe to perform the sampling operation. Once the sampling was completed, the CI-Robot was removed, and the obtained sample was extracted from its capillary structure for flow counting analysis. Additionally, after drying the sampled CI-Robot, the capillary structure at its antennae was dissected, and the interior was characterized by SEM to observe the presence of the obtained microplastics. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, under the guidance of magnetic navigation, the CI-Robot can move smoothly in the U-shaped acrylic pipe with an inner diameter of φ\u0026thinsp;=\u0026thinsp;15mm and carry out rotating sampling operations at the turning point. Here we use polystyrene and silica microspheres with different sizes as microplastic models. From SEM images, CI-Robot can sample the microspheres into its flagellar channels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The experimental results show that CI-Robot has a stronger ability to sample larger diameter microspheres under the same material, and that to larger density microspheres under the same size (Fig. S26A). Moreover, the sampling operation of CI-Robot was mainly concentrated in the first 20 seconds, after which the number of microspheres sampled remained basically unchanged (Fig. S26B). Additionally, we compared the impact of the three operating modes on sampling. The CI-Robot achieved the fastest movement and maximum sampling capacity when all passageways were open. When only one passageway was opened, the CI-Robot's fuel mainly diffused and released, without generating significant fluid mass transfer, resulting in poor sampling ability. When the CI-Robot was not filled with fuel, mass transfer was absent, and diffusion solely relied on the free Brownian motion of the microspheres, leading to an almost nonexistent sampling capability. (Fig. S26C). Further, we discuss the sampling capability of CI-Robot in solution environments with different concentrations of microspheres. As the concentration of microspheres in the environment increased, the number of samples taken by the CI-Robot increased linearly, and its minimum sampling limit was 10\u003csup\u003e3\u003c/sup\u003e number/mL (Fig. S26D). After the CI-Robot performs sampling, it is important to consider whether the captured sample may leak due to the robot's need to operate in the pipeline for an extended period. To address this concern, we designed an experiment to compare immediate sampling analysis with delayed sampling analysis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E, the proportion of microspheres in both immediate and delayed sampling analyses remains essentially unchanged. Numerical analysis clearly indicates that the PS-L microspheres captured by the CI-Robot experience only a minor, non-significant reduction during delayed sampling, with no substantial leakage observed in the solution (Fig. S26E). The slight variation may be attributed to minor handling errors during the transfer of the CI-Robot. These results demonstrate that the CI-Robot effectively captures and retains samples in the sample area, with no significant changes over time or in varying environmental conditions.\u003c/p\u003e \u003cp\u003eMoreover, we evaluated the ability of CI-Robot to transport small molecule compounds or drugs in pipes. Taking environmental pH monitoring as an example, the fuel containing phenolphthalein indicator was loaded inside the CI-Robot, and the photoresponsive gel was used to plug each fuel channel outlet of the CI-Robot. When magnetically driven to a specified tank, a near-infrared laser was used to illuminate the gel closure of the CI-Robot. At this time, the fuel outlet was opened, and phenolphthalein dissolved in ethanol fuel was released into the tank with the rotating motion of the CI-Robot, and the diffusion process of phenolphthalein in the tank was accelerated due to the self-driven rotation of the CI-Robot. In this process, when the solution was alkaline, it would produce a color reaction, and then the acid and alkali of the environmental water could be quantitatively analyzed according to the colorimetric method. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, the CI-Robot can run into the specified tank to release phenolphthalein at a fixed point. At the same time, the accompanying rotating motion can play a similar role of agitation, so as to display uniform different color states according to the pH of the solution. By comparing the gray value of the image (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), the color change of the region can be qualitatively obtained, and the acid-alkalinity of the environment can be monitored at a fixed point. The results demonstrate that the CI-Robot can release small molecule compounds and enhance the uniform diffusion of these compounds in the region through its self-driving capabilities during the release process.\u003c/p\u003e \u003cp\u003eWe further investigated the collecting and eliminate live bacteria ability of CI-Robot within pipelines. Using Escherichia coli (\u003cem\u003eE. coli\u003c/em\u003e) as the model bacteria, the CI-Robot sampled bacterial solutions of varying concentrations, with subsequent analysis via qPCR. As the bacterial concentration increased, the amplification curve of CI-Robot samples shifted significantly to the right, while parallel experimental groups showed only slight changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). The Ct values exhibited a clear linear relationship with bacterial concentration, with a detection limit of 100 CFU/mL (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). Additionally, samples taken by CI-Robot immediately after sampling and samples taken after soaking for 30 min had consistent results in the amplification curve characterization of qPCR, indicating that the CI-Robot could successfully capture live microorganisms and lock in sample without leakage (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ). CI-Robots loaded with ethanol fuel showed significant bacterial capture due to fluid mass transfer, whereas those without fuel captured few bacteria relying only on microorganism movement (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK-L). When loaded with Kana antibiotic solutions, the CI-Robot exhibited self-driving behavior through the Marangoni effect, enhancing antibiotic diffusion and distribution. In contrast, the CI-Robot with pure water did not generate self-drive and had no mass transfer effect. In both low- and high-concentration bacterial solutions, the self-driven CI-Robot loaded with antibiotic fuel showed rapid antibacterial action (Fig. S27). Conversely, the non-driven CI-Robot did not release antibiotics effectively, failing to exhibit significant antibacterial effects. Quantification of antimicrobial properties revealed that the self-driven CI-Robot had an antibacterial rate of over 95%, compared to less than 50% for the undriven CI-Robot, indicating a substantial difference in performance. These results demonstrate that the self-driven CI-Robot, loaded with antibiotic fuel, can effectively release and accelerate the diffusion of antibiotics, while the non-driven version does not achieve effective bacterial killing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn summary, we have designed self-propelled water-air interface mini-robots using Chlamydomonas as a prototype, focusing on shape design and motion bionics. Through comprehensive design optimization, motion simulation, and control of motion direction and speed, we have reconstructed the complex, long-duration, and multi-modal movement of these mini-robots, enabling their multi-functional application in various narrow water areas and pipeline operations. The design principles of the mini-robots we propose are primarily based on the drag-reducing shape characteristics of Chlamydomonas, optimized through fluid-structure coupling simulations. Unlike most existing sheet-like or thin-film water-air interface mini-robots, our design incorporates a complex three-dimensional structure. The combination of the buoyancy chamber and the buoyancy enhancement effect, stemming from the shape design, significantly improves the load capacity of the mini-robots, reaching up to 2.968×10\u003csup\u003e− 3\u003c/sup\u003e N. Building on this, we designed a Y-shaped capillary fuel release system driven by the Marangoni effect, inspired by the driving force direction and application points observed in different Chlamydomonas motion modes. This system facilitates the reproduction of both linear and rotational motion behaviors by adjusting the form, size, and direction of the driving force generated by the fuel release. The mini-robots achieve a linear speed of 11.43 body/s and a rotational speed of 8.98π rad/s, surpassing the linear speed of 5.25 body/s and rotational speed of 1.8π rad/s of Chlamydomonas. These results demonstrate that the designed water-air interface mini-robots possess efficient driving capabilities.\u003c/p\u003e \u003cp\u003eIn response to the lack of systematic research on the fluid state at the gas-liquid interface in current water-air interface robot research, we developed a dynamic simulation model based on kinematic theory and calculated the magnitude of the driving force for both straight and rotational motion of the mini-robots. The results indicate that the driving force required for the mini-robots to start straight motion from rest is at least on the order of 10\u003csup\u003e− 3\u003c/sup\u003e N, while maintaining straight motion after reaching a specified linear speed requires only a stable driving force on the order of 10\u003csup\u003e− 5\u003c/sup\u003e N. Initiating rotational motion requires a driving torque on the order of 10\u003csup\u003e− 6\u003c/sup\u003e N·m, while maintaining stable rotation after reaching the specified rotational speed requires a driving torque on the order of 10\u003csup\u003e− 9\u003c/sup\u003e N·m. The time-dependent variation in driving force closely aligns with the temporal changes in tension generated by the Marangoni effect, enabling rapid startup and stable operation through Marangoni-effect-driven propulsion. Additionally, by simulating fuel diffusion and release distributions under various motion states of the driving system, we observed that fuel diffusion and release decrease with increasing motion speed. This leads to a negative feedback regulation of motion speed, allowing the mini-robot to continue operating at a relatively stable speed.\u003c/p\u003e \u003cp\u003eAddressing the inherent challenges of water-air interface robots driven by the Marangoni effect, such as rapid fluid diffusion caused by surface tension gradients and significant effects of boundary conditions on flow direction and path, we propose a control strategy to adjust the propulsion mode, speed, lifespan, and directionality of the mini-robots. This is achieved by designing fuel channels, controlling the ethanol flow rate, and applying long-range magnetic navigation. By adjusting the fuel channel outlet status and ethanol flow rate, we demonstrate various straight and rotational motion behaviors of the mini-robots. Remote navigation based on magnetic fields allows for programmable trajectory motion. The results show that the designed water-air interface robots can accomplish tasks such as obstacle avoidance, complex path planning, and fixed-point transportation of small goods in complex water bodies, such as narrow channels, array column channels, and curved pipelines. These findings indicate that the mini-robots exhibit strong motion performance and high environmental adaptability. Furthermore, in response to the potential interference during the operation of mini-robot collectives, we have determined through experimental and simulation calculations that the distance at which mini-robot collectives do not interfere with each other during operation is 2/3 of their body length.\u003c/p\u003e \u003cp\u003eCurrently, water-air interface robots often require external functional modules to perform specific tasks, which limit their practical application in aquatic environments. To address this limitation, we propose integrating the functional actuator and driving mechanism into a single unit by utilizing the strong mass transfer effect of the Marangoni effect and the liquid retention capability of the capillary tube. This integration enables applications in environmental detection, antibacterial agent transport and release, and pollutant sampling. The results show that the designed capillary-based drive system can release small-molecule compounds dissolved in the fuel during operation, enabling environmental detection and antibacterial applications. In pipeline environments, the capillary-based system demonstrates the ability to capture non-active microplastics and active bacteria under strong mass transfer and liquid retention capabilities. It also ensures that samples can be retained for extended periods (\u0026gt; 30 min) without loss after sampling. These results indicate that the mini-robots performs well in pipeline environments, both in operation and in sampling analysis. We expect this study to help decouple the complex gaits generated by Chlamydomonas, leading to a deeper understanding of their underlying mechanisms. Additionally, this robot can be functionalized with various responsive hydrogels and functional materials, with potential applications in environmental remediation and drug delivery. Moreover, it could inform the design and operation of future bionic robots with programmed swimming gaits, opening new avenues for control, sensing, and driving mechanisms.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eMaterials\u003c/h2\u003e\u003cp\u003eEthanol, methanol, acetone, N, N-dimethylformamide (DMF), N, N-dimethylaniline (DMA), glycerinum, 2,4,6-trimethylbenzoyl phenylphosphinate acid ethyl ester (TPO-L), and hydrochloric acid (HCl) were purchased from Xilong Scientific Co.,Ltd. (Shanghai, China). Ferric chloride anhydrous (FeCl\u003csub\u003e3\u003c/sub\u003e), ferrous chloride tetrahydrate (FeCl\u003csub\u003e2\u003c/sub\u003e·4H\u003csub\u003e2\u003c/sub\u003eO), trisodium citrate dihydrate (C\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e5\u003c/sub\u003eNa\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e·2H\u003csub\u003e2\u003c/sub\u003eO), sodium hydroxide (NaOH), acrylamide (AAm), bis-acrylamide (BIS), polyvinylpyrrolidone (PVP), sodium borohydride (NaBH\u003csub\u003e4\u003c/sub\u003e), hexadecyl trimethyl ammonium bromide (CTAB), sodium oleate (NaOA), silver nitrate (AgNO\u003csub\u003e3\u003c/sub\u003e), N-isopropyl acrylamide (NIPAm), L-ascorbic acid, and phenolphthalein were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Chloroauric acid (HAuCl\u003csub\u003e4\u003c/sub\u003e) was purchased from Shanghai Bojing Chemical Co.,Ltd. (Shanghai, China). Polystyrene microsphere and silicon dioxide microsphere were obtained from Beijing HumaDX Tech Co., Ltd. (Beijing, China). LB agar was purchased from BBI Life Sciences Co., Ltd. (Shanghai, China). Kanamycin (Kana) was purchased from Beijing Solarbio Science\u0026amp;Technology Co.,Ltd. (Beijing, China). The qPCR reagent kit was purchased from TransGen Biotech Co., Ltd. (Beijing, China). Ultrapure water (Millipore system, 18.2 MΩ cm) was used to prepare aqueous solutions throughout the experiments. All other reagents were of analytical grade and used as received.\u003c/p\u003e\u003ch2\u003eFabrication of CI-Robot\u003c/h2\u003e\u003cp\u003eThe CI-Robot was inspired and designed by Chlamydomonas, and all the parts of CI-Robot were fabricated by a projection micro stereolithography 3D printing machine (nanoArch S140, BMF Material Technology Inc., Shenzhen, China) with high temperature resistant resin (HTL, BMF Material Technology Inc., Shenzhen, China). The spatial resolution for the 3D printing fabrication was 10 µm and the layer thickness was 100 µm. A 405 nm 55.4 mW/cm\u003csup\u003e2\u003c/sup\u003e ultraviolet light source was employed to cure the resin material, and the exposure time during the printing was 1 s. To facilitate the removal of support materials for 3D printing, the CI-Robot was divided into the front part, microporous barrier part and back part in the middle. After removing excess support material, the CI-Robot was bonded from the front part, microporous barrier part and back part using photo-curing glue in a clean room. Each CI-Robot weighs 0.0381 g after assembly and was soaked overnight in ultrapure water to test the tightness of the bonding.\u003c/p\u003e\u003ch2\u003ePreparation of magnetic navigation system\u003c/h2\u003e\u003cp\u003eThe magnetic navigation system consisted of a magnetic gel installed on the CI-Robot and an external magnetic field control device. The magnetic gel was synthesized by dispersing ferric oxide nanoparticles (Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003eNPs) in polyacrylamide gel. Briefly, 0.162 g FeCl\u003csub\u003e3\u003c/sub\u003e, 0.994 g FeCl\u003csub\u003e2\u003c/sub\u003e·4H\u003csub\u003e2\u003c/sub\u003eO, and 1.47 g C\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e5\u003c/sub\u003eNa\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e·2H\u003csub\u003e2\u003c/sub\u003eO were dissolved in 40 mL ultrapure water and heated to 90 ℃ with 800 rpm. Following, 10 mL 0.24 g/mL NaOH solution were added and maintain the reaction temperature at 90 ℃ for 2 h. The product was purified three times by centrifugation with ultrapure water at 10000 rpm for 20 min and redissolved in 50 mL ultrapure water as Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003eNPs solution for future use. 7 g AAm, 0.3 g BIS, and 3 g PVP were dissolved in 30 mL the prepared Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003eNPs solution and stirred at 500 rpm for 10 min. Subsequently, this precast gel was stored in 4 ℃ for 24 h to complete cross-linking. Finally, 5 mg prepared magnetic gel was filled in mounting hole of the CI-Robot and dry at 60 ℃ for 2 h to make sure magnetic gel adhere. The external magnetic field control device was composed of a three-dimensional moving platform with stepper motors. A magnetic pole array with a diameter of 5 mm was arranged in this platform. Therefore, the magnetic navigation field could be constructed by controlling the movement of the 3D mobile platform.\u003c/p\u003e\u003ch2\u003ePreparation of photoresponse channel switch\u003c/h2\u003e\u003cp\u003eThe photoresponse channel switch, which was synthesized by dispersing gold nanorods (AuNRs) in polyisopropylacrylamide gel, was used for remote controlling the drive mode of the CI-Robot. Briefly, 0.3645 g CTAB and 2.3 mg of NaBH\u003csub\u003e4\u003c/sub\u003e were dissolved in 10 mL 0.25 mM HAuCl\u003csub\u003e4\u003c/sub\u003e with 1500 rpm for 2 min and stood for 0.5 h as seed solution. 1.4 g CTAB, 0.2468 g NaOA and 3.3 mg of AgNO\u003csub\u003e3\u003c/sub\u003e were dissolved in 100 mL 0.5 mM HAuCl\u003csub\u003e4\u003c/sub\u003e with 500 rpm for 1.75 h, then 0.3 mL HCl was added with 500 rpm for another 0.25 h. Subsequently, 0.25 mL 64 mM L-ascorbic acid solution and 0.2 mL seed solution were added with 1500 rpm for 1 min, and the mix solution was stood in a 30 ℃ water bath for 12 h. The product was purified three times by centrifugation with ultrapure water at 10000 rpm for 20 min and redissolved in 50 mL ultrapure water as AuNRs solution for future use. 10 g NIPAm, 0.7 g AAm, 0.3 g BIS, 3 g PVP and 0.6 mL 10wt% TPO-L ethanol solution were dissolved in 30 mL the prepared AuNRs solution with dark stirring 500 rpm for 10 min. Following, this precast photoresponse gel was stored in 4 ℃ with dark for future use. After the fuel was loaded in the CI-Robot, 0.2 µL the precast photoresponse gel was used to block the outlet of channel of the CI-Robot and then complete the preparation of photoresponse channel switch after 30 s of ultraviolet light irradiation.\u003c/p\u003e\u003ch2\u003eFabrication of flume and pipeline model\u003c/h2\u003e\u003cp\u003eThe annular pipe flume, cylinder array flume and circular labyrinth flume were designed by AutoCAD, and all the flumes were fabricated by a melt deposition 3D printing machine (Neptune 3 Pro, ELEGOO intelligent technology Inc., Shenzhen, China) with polylactic acid wire (ELEGOO PLA wire, ELEGOO intelligent technology Inc., Shenzhen, China). The temperature of sprinkler head was 215 ℃ and baseplate was 65 ℃. The complex pipeline model was fabricated by assembling transparent acrylic pipe with 15 mm inner diameter and plate with thickness 2.5 mm using photo-curing glue.\u003c/p\u003e\u003ch2\u003eCulture of Chlamydomonas\u003c/h2\u003e\u003cp\u003eChlamydomonas were inoculated into a conical bottle containing 100 mL sterile SE medium in a super-clean table and placed in a light incubator for expansion culture. The culture conditions were as follows: temperature 25℃, light intensity 2000 Lux, light-dark cycle ratio 16 h: 8 h, hand shaking 6 times a day and randomly changing the position of the conical bottle.\u003c/p\u003e\u003ch2\u003eMovement analysis of CI-Robot\u003c/h2\u003e\u003cp\u003eThe movement behaviors of the CI-Robot were filmed with NIKON D780 camera in various flumes and pipeline model. Ethanol was encapsulated in the CI-Robot before the experiment and using photoresponse channel switch to block the outlet according to the design of motion mode. Programmable trajectories were designed by magnetic navigation system, which has been reported in our previous study. In short, a three-dimensional robotic arm carrying a strong magnetic magnet was moved to build the desired field gradient by preset trajectory. The magnetic field strength was determined by the distance between the magnet and the driving plane. All experiments were carried out at room temperature, and the flume was filled with tap water for propulsion experiments. The movement videos were analyzed by Tracker.jar to record the corresponding position coordinates frame by frame in the Open-Source Physics Java framework. The data acquired were used to calculate displacement, velocity, acceleration, rotation angle, angular velocity and angular acceleration.\u003c/p\u003e\u003ch2\u003eCargo delivery experiments of CI-Robot\u003c/h2\u003e\u003cp\u003eThe cargo delivery experiments of CI-Robot were demonstrated by transporting small goods, phenolphthalein indicator and antibiotics. In a small goods transportation experiment, a black disc as the goods model was bonded to the flagellar channel outlet of the CI-Robot through photoresponse channel switch. When the CI-Robot moved to the designated area under magnetic navigation, the goods were released by the near-infrared laser (2.5 W/cm\u003csup\u003e2\u003c/sup\u003e) illuminated switch. In the phenolphthalein indicator transportation experiment, 3 µL 0.01g/mL phenolphthalein ethanol solution was loaded in the CI-Robot and using photoresponse channel switch to block all outlet of the CI-Robot. After the CI-Robot was run through the pipeline to the pool with different pH environments, the indicator was released by the near-infrared laser (2.5 W/cm\u003csup\u003e2\u003c/sup\u003e) illuminated switch. The color changes in the pool were recorded by NIKON D780 camera and quantified the gray value by Image J software. This experiment was repeated 3 times for each group. In antibiotics transportation experiment, 3 µL 50 mg/mL Kana ethanol solution and 3 µL 50 mg/mL Kana water solution was loaded in the CI-Robot respectively. The \u003cem\u003eE. coli\u003c/em\u003e solution with 10\u003csup\u003e2\u003c/sup\u003e CFU/mL and 10\u003csup\u003e4\u003c/sup\u003e CFU/mL were used to construct a pipeline model. After the CI-Robot released antibiotics for 5 minutes, 5 mL of water was taken from the pipeline and incubated for 6 hours, then evenly spread on LB agar and allowed to grow for 24 hours. Finally, the obtained bacteria trays were photographed by the gel imager and counted the colonies by Image J software. Each experiment was repeated three times. The antibacterial rate was calculated by the following formula:\u003c/p\u003e\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:Antibacterial\\:rate\\:\\left(\\%\\right)=\\left(1-\\frac{{A}_{t}}{{A}_{0}}\\right)\\times\\:100\\%$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the bacterial colony of the blank group, and \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e is the bacterial colony of the experimental group or control group.\u003c/p\u003e\u003ch2\u003eSampling experiments of CI-Robot\u003c/h2\u003e\u003cp\u003eThe pipeline sampling experiment of CI-Robot was demonstrated by microplastics sampling and microbial sampling. Firstly, polystyrene microspheres and silica microspheres with different sizes as microplastic models, and ampicillin resistant \u003cem\u003eE. coli\u003c/em\u003e as bacterial models were pre-placed in the pipeline respectively. The CI-Robot runs in the pipeline and collects samples through strong fluid mass transfer of the Marangoni effect. Subsequently, the CI-Robot, which had completed the sampling task, was removed and carefully wiped the residual water on the surface. The collected samples were obtained from the channel of CI-Robot with 100 µL eluent. In microplastics sampling experiment, the CI-Robot after finishing microsphere sampling task was observed in situ by scanning electron microscope. As another appraisal, the collected samples in CI-Robot were eluted with ultrapure water and counted by flow cytometry. To verify the accuracy of microsphere sampling, a comparison test between immediate sampling analysis and delayed sampling analysis was conducted. Simply, 10\u003csup\u003e5\u003c/sup\u003e number/mL polystyrene microspheres with low scattering light (PS-L) as the sample solution and 10\u003csup\u003e3\u003c/sup\u003e number/mL polystyrene microspheres with high scattering light (PS-H) as the eluent solution were carried in this experiment. For immediate sampling analysis, after finishing microsphere sampling task the CI-Robot was taken out soon and eluted the samples for flow cytometry. For delayed sampling analysis, after finishing microsphere sampling task the CI-Robot was placed in ultrapure water for 30 min and then eluted the samples for flow cytometry. Since the concentration of PS-H is identical, the leakage degree of PS-L which sampled by the CI-Robot in delayed process can be reflected by the proportion of the two gates in flow cytometry pattern. This experiment was repeated eight times. In microbial sampling experiment, the collected samples in CI-Robot were eluted with pH = 7.5 PBS solution and characterized by real-time fluorescence quantitative polymerase chain reaction (qPCR) assay and standard plate count. In qPCR assay, the forward primer sequence of ampicillin resistance gene (Amp-F) was TTACCAATGCTTAATCAGTGAGGCAC, and the reverse primer sequence of ampicillin resistance gene (Amp-R) was ATGAGTATTCAACATTTCCGTGTCGC. For delayed sampling analysis, the CI-Robot which finishes bacterial sampling task was placed in pH = 7.5 PBS solution for 30 min and then eluted the samples for qPCR assay. In standard plate count, 100 µL collected samples were evenly coated on LB agar and incubated with 37.5 ℃ for 24 h. Finally, the obtained bacteria trays were photographed by the gel imager and counted the colonies by Image J software. This experiment was repeated five times.\u003c/p\u003e\u003ch2\u003eNumerical simulation of fluid dynamics\u003c/h2\u003e\u003cp\u003eIn the research of the CI-Robot motion mechanism, the 2D fluid dynamics of the CI-Robot under different driving forces were established, meshed, and simulated by COMSOL Multiphysics 6.1. The multi-physics simulation of fluid-structure interaction includes k-ε turbulent flow module, solid mechanics module and moving mesh deforming domain module. Where the calculation domain of the straight motion was 100 mm × 20 mm rectangle region, and the calculation domain of the rotational motion was Φ50 mm round region. The wall around calculation domain were open boundaries and set 0 Pa press point constraint at the corner of the wall.\u003c/p\u003e\u003ch2\u003eNumerical simulation of ethanol diffusion flux\u003c/h2\u003e\u003cp\u003eIn the research of the ethanol diffusion flux, the motion parameters of CI-Robot as the boundary conditions were used to simulate the ethanol concentration distribution. Transport of diluted species modules and moving mesh modules were used in this simulation. Where the calculation domain was same as the fluid dynamics simulation, the initial value of ethanol was 17.2 M, and the velocity of the model was defined by the moving mesh module. In numerical solution, the driving force generated by the Marangoni effect (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e) was obtained by integrating the surface tension (γ, Fig. S28):\u003c/p\u003e\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\:{F}_{m}={\\oint\\:}_{L}^{\\:}{\\gamma\\:}_{2}\\:dL-{\\oint\\:}_{L}^{\\:}{\\gamma\\:}_{1}\\:dL$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$\\:\\gamma\\:=0.07202-0.01236{ln}\\left(1+0.5842{C}_{Ethanol}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere \u003cem\u003eL\u003c/em\u003e is the contact line between drive system and fluid, \u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e is the surface tension at the \u003cem\u003eL, γ\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e is the surface tension at parallel to the \u003cem\u003eL\u003c/em\u003e with 1 mm, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eEthanol\u003c/em\u003e\u003c/sub\u003e is the concentration of ethanol.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll Data were presented as mean ± standard deviation unless otherwise noted. Differences between groups were compared by analysis of one-way analysis of variance using Origin software (OriginLab, Northampton, MA, USA). Results were considered statistically significant when P \u0026lt; 0.05. The sample numbers for each statistical analysis were listed in the methods section and figure legends.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by the National Natural Science Foundation of China (Grant No. 52273305 and 32271469), National Natural Science Foundation of China under the Basic Science Center Program for \u0026quot;Space Robot Intelligent Manipulation\u0026quot; (Grant No. T2388101), Natural Science Foundation of Xiamen, China (Grant No. 3502Z20227010), Natural Science Foundation of Fujian Province of China (Grant No. 2023J05012), Fundamental Research Funds for the Central Universities (Grant No. 20720230037), State Key Laboratory of Vaccines for Infectious Diseases, and Xiang An Biomedicine Laboratory (Grant No. 2023XAKJ0103071).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.L., M.W., and\u0026nbsp;L.R. contributed to the conception of the work;\u0026nbsp;L.L., G.W., and\u0026nbsp;H.S. conceived the experimental method;\u0026nbsp;L.L., W.L.,\u0026nbsp;and\u0026nbsp;M.W. performed the experiments;\u0026nbsp;L.L. and L.H. contributed to data processing and analysis;\u0026nbsp;M.W. and L.R.\u0026nbsp;administrated the project;\u0026nbsp;H.S., M.W., and\u0026nbsp;L.R. supervised the study;\u0026nbsp;L.L. and L.H. discussed the results and contributed to the writing of the original draft;\u0026nbsp;L.L. and M.W. contributed to the writing,\u0026nbsp;review \u0026amp; editing\u0026nbsp; of the original manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are available in the manuscript or the supplementary materials.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e The online version contains supplementary material available at xxx.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e and requests for materials should be addressed to Miao Wang, Hao Sun or Lei Ren.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePeer review information\u003c/strong\u003e Nature Communications thanks all reviewers for their contribution to the peer review of this work. A peer reviewer file is available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReprints and permission information\u003c/strong\u003e is available at http://www.nature.com/reprints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher's note\u003c/strong\u003e Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen, Y.\u003cem\u003e et al.\u003c/em\u003e A biologically inspired, flapping-wing, hybrid aerial-aquatic microrobot. \u003cem\u003eSci. Robot.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 5619 (2017).\u003c/li\u003e\n\u003cli\u003eWang, D.\u003cem\u003e et al.\u003c/em\u003e Vibration-induced-flow mechanism and its application in water surface robot. \u003cem\u003eResearch\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 0449 (2024).\u003c/li\u003e\n\u003cli\u003eLi, L.\u003cem\u003e et al.\u003c/em\u003e Aerial-aquatic robots capable of crossing the air-water boundary and hitchhiking on surfaces. \u003cem\u003eSci. Robot.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 6695 (2022).\u003c/li\u003e\n\u003cli\u003eHartmann, F.\u003cem\u003e et al.\u003c/em\u003e Highly agile flat swimming robot. \u003cem\u003eSci. Robot.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 0721 (2025).\u003c/li\u003e\n\u003cli\u003eZhang, J.\u003cem\u003e et al.\u003c/em\u003e Self-propelled nanocellulose aerogel eco-robots for self-powered aquatic environment perception. \u003cem\u003eACS Energy Lett.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 4852-4863 (2024).\u003c/li\u003e\n\u003cli\u003eHong, C.\u003cem\u003e et al.\u003c/em\u003e Wireless flow-powered miniature robot capable of traversing tubular structures. \u003cem\u003eSci. Robot.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 5155 (2024).\u003c/li\u003e\n\u003cli\u003eChen, B. W.\u003cem\u003e et al.\u003c/em\u003e Cooperative magnetic interfacial microrobot couple for versatile non-contact biomedical applications. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 2417416 (2025).\u003c/li\u003e\n\u003cli\u003eVaquero, T. S.\u003cem\u003e et al.\u003c/em\u003e EELS: Autonomous snake-like robot with task and motion planning capabilities for ice world exploration. \u003cem\u003eSci. Robot.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 8332 (2024).\u003c/li\u003e\n\u003cli\u003eLv, X., Wang, W., Clancy, A. J. \u0026amp; Yu, H. High-speed, heavy-load, and direction-controllable photothermal pneumatic floating robot. \u003cem\u003eACS Appl. Mater. Interfaces\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 23030-23037 (2021).\u003c/li\u003e\n\u003cli\u003ePi\u0026ntilde;an Basualdo, F. N., Bolopion, A., Gauthier, M. \u0026amp; Lambert, P. A microrobotic platform actuated by thermocapillary flows for manipulation at the air-water interface. \u003cem\u003eSci. Robot.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 3557 (2021).\u003c/li\u003e\n\u003cli\u003eLi, Z. W., Myung, N. V. \u0026amp; Yin, Y. D. Light-powered soft steam engines for self-adaptive oscillation and biomimetic swimming. \u003cem\u003eSci. Robot.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 4523 (2021).\u003c/li\u003e\n\u003cli\u003ePark, S.-J.\u003cem\u003e et al.\u003c/em\u003e Phototactic guidance of a tissue-engineered soft-robotic ray. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e353\u003c/strong\u003e, 158-162 (2016).\u003c/li\u003e\n\u003cli\u003eMa, Y.\u003cem\u003e et al.\u003c/em\u003e Bioinspired high-power-density strong contractile hydrogel by programmable elastic recoil. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 2520 (2020).\u003c/li\u003e\n\u003cli\u003eTang, S.\u003cem\u003e et al.\u003c/em\u003e Enzyme-powered Janus platelet cell robots for active and targeted drug delivery. \u003cem\u003eSci. Robot.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 6137 (2020).\u003c/li\u003e\n\u003cli\u003eRan, H.\u003cem\u003e et al.\u003c/em\u003e Programmable ultrasound-mediated swarms manipulation of bacteria\u0026ndash;red blood cell microrobots for tumor-specific thrombosis and robust photothermal therapy. \u003cem\u003eTrends Biotechnol.\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 868-892 (2024).\u003c/li\u003e\n\u003cli\u003eLi, S.\u003cem\u003e et al.\u003c/em\u003e Particle robotics based on statistical mechanics of loosely coupled components. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e567\u003c/strong\u003e, 361-365 (2019).\u003c/li\u003e\n\u003cli\u003eLi, G.\u003cem\u003e et al.\u003c/em\u003e Self-powered soft robot in the Mariana Trench. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e591\u003c/strong\u003e, 66-71 (2021).\u003c/li\u003e\n\u003cli\u003eGardi, G., Ceron, S., Wang, W., Petersen, K. \u0026amp; Sitti, M. Microrobot collectives with reconfigurable morphologies, behaviors, and functions. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 2239 (2022).\u003c/li\u003e\n\u003cli\u003eWang, W.\u003cem\u003e et al.\u003c/em\u003e Order and information in the patterns of spinning magnetic micro-disks at the air-water interface. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 0685 (2022).\u003c/li\u003e\n\u003cli\u003eHan, H.\u003cem\u003e et al.\u003c/em\u003e Imaging-guided bioresorbable acoustic hydrogel microrobots. \u003cem\u003eSci. Robot.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 3593 (2024).\u003c/li\u003e\n\u003cli\u003eLi, S.\u003cem\u003e et al.\u003c/em\u003e Jellyfish-inspired visual and sensory bubbling robots with automatic 3D morphable films for underwater environmental interactions. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 20694-20705 (2024).\u003c/li\u003e\n\u003cli\u003eLi, Z. X.\u003cem\u003e et al.\u003c/em\u003e Inhalable biohybrid microrobots: a non-invasive approach for lung treatment. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 666 (2025).\u003c/li\u003e\n\u003cli\u003eHuang, Y., Guo, J. H., Li, Y. F., Li, H. Z. \u0026amp; Fan, D. E. 2D-material-integrated micromachines: competing propulsion strategy and enhanced bacterial disinfection. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2203082 (2022).\u003c/li\u003e\n\u003cli\u003eXiong, W.\u003cem\u003e et al.\u003c/em\u003e Marangoni-driven deterministic formation of softer, hollow microstructures for sensitivity-enhanced tactile system. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 5596 (2024).\u003c/li\u003e\n\u003cli\u003eKe, X., Yong, H., Xu, F., Ding, H. \u0026amp; Wu, Z. Stenus-inspired, swift, and agile untethered insect-scale soft propulsors. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1491 (2024).\u003c/li\u003e\n\u003cli\u003eDong, R. F., Cai, Y. P., Yang, Y. R., Gao, W. \u0026amp; Ren, B. Y. Photocatalytic micro/nanomotors: from construction to applications. \u003cem\u003eAcc. Chem. Res.\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 1940-1947 (2018).\u003c/li\u003e\n\u003cli\u003eNguyen, V. D.\u003cem\u003e et al.\u003c/em\u003e pH-sensitive magnetic nanoparticle-mediated natural-killer-cell-based microrobots for dual-targeted delivery and induction of pro-inflammatory macrophage polarization. \u003cem\u003eSmall Struct.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 2400149 (2024).\u003c/li\u003e\n\u003cli\u003eZheng, Z.\u003cem\u003e et al.\u003c/em\u003e Ionic shape-morphing microrobotic end-effectors for environmentally adaptive targeting, releasing, and sampling. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 411 (2021).\u003c/li\u003e\n\u003cli\u003eWang, Y.\u003cem\u003e et al.\u003c/em\u003e Meniscus-climbing system Inspired 3D printed fully soft robotics with highly flexible three-dimensional locomotion at the liquid\u0026ndash;air interface. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 19393-19402 (2022).\u003c/li\u003e\n\u003cli\u003eZheng, Z.\u003cem\u003e et al.\u003c/em\u003e Programmable aniso-electrodeposited modular hydrogel microrobots. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 6135 (2022).\u003c/li\u003e\n\u003cli\u003eWang, X.\u003cem\u003e et al.\u003c/em\u003e Multistimuli-responsive hydroplaning superhydrophobic microrobots with programmable motion and multifunctional applications. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 14895-14906 (2022).\u003c/li\u003e\n\u003cli\u003eMao, G.\u003cem\u003e et al.\u003c/em\u003e Ultrafast small-scale soft electromagnetic robots. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 4456 (2022).\u003c/li\u003e\n\u003cli\u003eChen, Y., Doshi, N., Goldberg, B., Wang, H. \u0026amp; Wood, R. J. Controllable water surface to underwater transition through electrowetting in a hybrid terrestrial-aquatic microrobot. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 2495 (2018).\u003c/li\u003e\n\u003cli\u003eQing, H.\u003cem\u003e et al.\u003c/em\u003e Spontaneous snapping-induced jet flows for fast, maneuverable surface and underwater soft flapping swimmer. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 4222 (2024).\u003c/li\u003e\n\u003cli\u003eWang, Q.\u003cem\u003e et al.\u003c/em\u003e Enhanced buoyancy and propulsion in 3D printed swimming micro-robots based on a hydrophobic nano-fibrillated cellulose aerogel and porous lead-free piezoelectric ceramics. \u003cem\u003eNano Energy\u003c/em\u003e \u003cstrong\u003e131\u003c/strong\u003e, 110254 (2024).\u003c/li\u003e\n\u003cli\u003ePeng, X., Urso, M., Ussia, M. \u0026amp; Pumera, M. Shape-controlled self-assembly of light-Powered microrobots into ordered microchains for cells transport and water remediation. \u003cem\u003eACS Nano\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 7615-7625 (2022).\u003c/li\u003e\n\u003cli\u003eXu, Z.\u003cem\u003e et al.\u003c/em\u003e Bubble-inspired multifunctional magnetic microrobots for integrated multidimensional targeted biosensing. \u003cem\u003eNano Lett.\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 13945-13954 (2024).\u003c/li\u003e\n\u003cli\u003eMao, J. W., Han, D. D., Zhou, H., Sun, H. B. \u0026amp; Zhang, Y. L. Bioinspired superhydrophobic swimming robots with embedded microfluidic networks and photothermal switch for controllable marangoni propulsion. \u003cem\u003eAdv. Funct. Mater.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 2208677 (2023).\u003c/li\u003e\n\u003cli\u003eSun, M.\u003cem\u003e et al.\u003c/em\u003e Individual and collective manipulation of multifunctional bimodal droplets in three dimensions. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1439 (2024).\u003c/li\u003e\n\u003cli\u003eWang, X. D., Dai, L. G., Jiao, N. D., Tung, S. \u0026amp; Liu, L. Q. Superhydrophobic photothermal graphene composites and their functional applications in microrobots swimming at the air/water interface. \u003cem\u003eChem. Eng. J.\u003c/em\u003e \u003cstrong\u003e422\u003c/strong\u003e, 129394 (2021).\u003c/li\u003e\n\u003cli\u003eLyu, L. X.\u003cem\u003e et al.\u003c/em\u003e Bio-inspired untethered fully soft robots in liquid actuated by induced energy gradients. \u003cem\u003eNatil. Sci. Rev.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 970-981 (2019).\u003c/li\u003e\n\u003cli\u003eGinger, M. L., Portman, N. \u0026amp; McKean, P. G. Swimming with protists: perception, motility and flagellum assembly. \u003cem\u003eNat. Rev. Microbiol.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 838-850 (2008).\u003c/li\u003e\n\u003cli\u003eNelson, G.\u003cem\u003e et al.\u003c/em\u003e Cells collectively migrate during ammonium chemotaxis in Chlamydomonas reinhardtii. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 10781 (2023).\u003c/li\u003e\n\u003cli\u003eLi, Q. W.\u003cem\u003e et al.\u003c/em\u003e Magnetically actuated soft microrobot with environmental adaptative multimodal locomotion towards targeted delivery. \u003cem\u003eAdv. Sci.\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 2406600 (2024). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bionic, Mini-Robot, Self-propel, Motion control, Delivery, Sampling","lastPublishedDoi":"10.21203/rs.3.rs-6560275/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6560275/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rapid development of micro-robotics, non-mechanical stimulus-responsive water-air interface mini-robots have become a prominent focus in intelligent materials and environmentally responsive systems. However, their versatile application is challenged by a fundamental trade-off: simpler structures enable precise motion control, while complex configurations are often required for task execution, making it difficult to balance controllable locomotion with functional complexity. Inspired by Chlamydomonas, we have designed a water-air interface mini-robot with a sophisticated multifunctional architecture (CI-Robot), enabling both programmable motion and multifunctional execution, which demonstrated tremendous potential for application in confined aquatic environments and complex pipelines. The robot can achieve ultra-fast linear and rotational speeds (11.43 body/s, 8.98π rad/s), exceeding biological counterparts by 1.37- and 4.24-fold, \u003cem\u003evia \u003c/em\u003esynergistic surface tension gradients and flagellar capillary mechanisms. The fluid-solid coupling simulation reveals the motion mechanism of CI-Robot in the transitional Reynolds regimes, in which the inertial force stabilizes the propulsion force, and the driving torque rapidly decreases to equilibrium (~15.21 μN, ~10⁻⁹ N·m), providing a theoretical basis for the analysis and regulation of the robot's motion behavior. The safe separation distance (~2/3 body length) without interference is determined by collective motion analysis, which guides the reasonable arrangement of CI-Robot group operation. Integrating propulsion and functional modules, the CI-Robot excels in obstacle avoidance, complex path planning, microplastic collection (up to 10\u003csup\u003e2\u003c/sup\u003e particles/mL), bacterial sampling (up to 100 CFU/mL) and site-specific molecular release, retaining samples for \u0026gt;30 minutes. This innovative mini-robot combining unparalleled speed, adaptability, and multifunctionality, will pave the way for transformative applications in cargo delivery, environmental monitoring, microplastic collection, and site-specific sampling in confined space.\u003c/p\u003e","manuscriptTitle":"Chlamydomonas-Inspired Water-Air Interface Mini-Robot with Intricate Tectonics, Programmable Locomotion, and Multifunctional Execution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 14:06:57","doi":"10.21203/rs.3.rs-6560275/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa0271b0-e11e-4fe6-a26e-c5988635278a","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48080540,"name":"Physical sciences/Engineering"},{"id":48080541,"name":"Physical sciences/Physics/Fluid dynamics"},{"id":48080542,"name":"Physical sciences/Materials science"}],"tags":[],"updatedAt":"2025-11-18T13:10:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 14:06:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6560275","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6560275","identity":"rs-6560275","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}