Rolling of soft microbots with tunable traction

Abstract

Microbot (μbot)-based targeted drug delivery has attracted increasing attention due to its potential for avoiding side effects associated with systemic delivery. To date, most μbots are rigid. When rolling on surfaces, they exhibit substantial slip due to the liquid lubrication layer. Here, we introduce magnetically controlled soft rollers based on Pickering emulsions that, because of their intrinsic deformability, fundamentally change the nature of the lubrication layer and roll like deflated tires. With a large contact area between μbot and wall, soft μbots exhibit tractions higher than their rigid counterparts, results that we support with both theory and simulation. Upon changing the external field, surface particles can be reconfigured, strongly influencing both the translation speed and traction. These μbots can also be destabilized upon pH changes and used to deliver their contents to a desired location, overcoming the limitations of low translation efficiency and drug loading capacity associated with rigid structures.

Fig. 1.. The rolling of deformable soft droplets.

(A) Pickering decane-in-water emulsion droplets are partially or fully covered with modified 1-μm polystyrene-Fe₃O₄ composite beads. (B) The contact area radius a of the droplets depends on their radius R. Fit is based on Eq. 1. Inset: The image of a deformed droplet. Rolling of a (C) soft Pickering droplet, (D) a rigid Pickering sphere, and (E) a soft ferrofluid droplet over 8.5 s under a rotating magnetic field (H₀ = 2.5 mT and f = 20 Hz) along the x-z plane.

Fig. 2.. The velocity and traction of soft and rigid μbots.

(A) Angular velocities of rigid and soft Pickering droplets under identical magnetic fields (H₀ = 2.5 mT and f = 20 Hz) with the slopes of the fitted lines indicated. (B) Tractions of rigid and soft Pickering droplets. The error bars in (A) and (B) around each data point are determined from five different measurements. Schematic of a (C) rigid sphere and (D) soft droplet rolling under an AC magnetic field. T_H and T_M are the hydrodynamic and magnetic torques. Surface particles in both cases are not shown for simplicity.

Fig. 3.. Numerical simulation on rolling of droplets with different softness and size.

(A) Position of larger droplets with R = 9.6 μm at times t = 0 s and t = 2 s. The softer green droplet ($\overline{a}=2.43$) rolls 3.5× faster than the blue rigid droplet ($\overline{a}=1.09$; movie S3). Particles around the droplet equator are darkened to provide a visual indication of the droplet orientation. (B) Droplet traction γ versus dimensionless contact radius $\overline{a}$. The solid black curve shows the effect of varying $\overline{a}$ for a fixed droplet size R = 4.61 μm, where markers (also of fixed size) are colored from rigid droplets in indigo to soft droplets in yellow. The dashed black curve shows the effect of varying $\overline{a}$ by varying droplet size R, where marker sizes vary according to droplet size. Inset: The positions of six representative droplets (top) with varying $\overline{a}$ at times t = 0 s and t = 2 s. Grid lines on the substrate surface are spaced every 10 μm with the red arrow indicating the rolling direction. (C) Droplet side view equilibrated over 0.25 s with no applied magnetic forces. All droplets have the same radius R = 4.61 μm. (D) Droplets with varying R and contact radius $\overline{a}$ vary accordingly.

Fig. 4.. Reconfiguration of surface particles tunable by external fields.

(A) Superimposed photos showing the reconfiguration of surface particles and the difference in velocities under different magnetic fields at a particle surface coverage of 40%. The AC field is 2.5 mT and 20 Hz, and the superimposed DC field is 2.5 mT. (B) Illustration of the induced dipoles between neighboring spheres located at the equator and the pole. Red arrows are dipoles induced by the rotating field, while blue arrows are dipoles induced by the DC field. (C) Calculated energies per particle for different magnetic fields and surface particle configurations. (D) Traction enhancement for the equatorial configuration. Inset: Measured velocities at 30% surface coverage. The error bars around each data point are SDs determined from five different measurements.

Fig. 5.. Destabilization of soft Pickering μbots.

(A) Concentrated Pickering emulsion droplets at pH 7 and (B) at pH 12, where (C) particles leave the droplet interface forming long comet-like tails, eventually causing the droplets to (D) burst.