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Active matter is differentiated from conventional passive matter due to its capability to convert chemical energy into mechanical work. A material that possesses such capability can consist of living or non-living entities such as bacteria, insects, colloids, grains, and cytoskeletal filaments1,2,3,4,5,6,7,8,9,10. These material entities interact with their neighbors. At a larger scale, they self-organize into either turbulent-like vortices (active turbulence) or material flows11,12,13,14,15,16,17,18,19,20. An understanding of self-organization of active matter has led to various applications in molecular shuttles, optical devices, and parallel computation21,22,23. To bring applications to the next level requires control beyond self-organization. For example, Palacci et al. developed a hematite-encapsulated colloid that self-propelled only when exposed to manually controlled blue light, which led to the emergence of living crystals24. Morin et al. established the control of rolling Quincke colloids by using a tunable external electric field, resulting in colloidal flocking in a racetrack-like channel25. These previous works demonstrate the role of local control in applications and advance the knowledge base of active matter.
In this article, we focus on the controllability of kinesin-driven, microtubule (MT)-based 3D active fluids. The fluids consist of three main components: MTs, kinesin molecular motors, and depletants. The depletants induce a depletion force to bundle the MTs, which are later bridged by motor clusters. These motors walk along the MTstoward the plus end. When a pair of bridged MTsis antiparallel, the corresponding motors walk in opposite directions. However, the motors are bound in a cluster and are unable to walk apart, so they cooperatively slide apart pairs of MTs (interfilament sliding, Figure 1A). These sliding dynamics accumulate, causing bundles of MTsto extend until reaching their buckling instability point and break (extensile bundles, Figure 1B)26. The broken bundles are annealed by the depletion force, which subsequently extends again, and the dynamics repeat. During the process of the repeating dynamics, the bundle movements stir the nearby liquid, inducing flows that can be visualized by doping with micron-scale tracers (Figure 1C). Sanchez et al. and Henkin et al. have characterized the mean speeds of tracers, finding that the speeds were tunable by varying the concentrations of adenosine triphosphate (ATP), depletants, motor clusters, and MTs19,27. However, such tunability existed only prior to active fluid synthesis. After synthesis, the tunability was lost, and the fluids self-organized in their own way. To control active fluid activity after synthesis, Ross.et al. reported a method using the light-activated dimerization of motor proteins, allowing fluid activity to be tuned on and off using light28. While light control is convenient in terms of locally activating the fluids, the method requires redesigning the structures of motor proteins, along with modifying the optical paths in a microscope. Here, we provide an easy-to-use method for locally controlling fluid flows without microscope modification while keeping the motor structure intact.
Our method of locally tuning active fluid flow is based on the Arrhenius law because the kinesin-MT reaction has been reported to increase with temperature29,30,31,32. Our previous studies showed that the temperature dependence of the mean speed of an active fluid flow followed the Arrhenius equation: v = A exp(-Ea/RT), where A is a pre-exponential factor, R is the gas constant, Ea is the activation energy, and T is the system temperature33. Therefore, fluid activity is sensitive to the temperature environment, and the system temperature needs to be consistent to stabilize the motor performance, and consequently, the fluid flow speed34. In this article, we demonstrate the use of the motor's temperature dependence to continuously tune the flow speeds of active fluids by adjusting the system temperature. We also demonstrate the preparation of an active fluid sample, followed by mounting the sample on a microscope stage whose temperature is controlled via computer software. Increasing the temperature from 16 °C to 36 °C speeds up the mean flow speeds from 4 to 8 µm/s. Additionally, the tunability is reversible: repeatedly increasing and decreasing the temperature sequentially accelerates and decelerates the flow. The demonstrated method is applicable to a wide range of systems where the main reactions obey the Arrhenius law, such as the MT gliding assay29,30,31,32.