Soft micromachines with programmable motility and morphology

Abstract

Nature provides a wide range of inspiration for building mobile micromachines that can navigate through confined heterogenous environments and perform minimally invasive environmental and biomedical operations. For example, microstructures fabricated in the form of bacterial or eukaryotic flagella can act as artificial microswimmers. Due to limitations in their design and material properties, these simple micromachines lack multifunctionality, effective addressability and manoeuvrability in complex environments. Here we develop an origami-inspired rapid prototyping process for building self-folding, magnetically powered micromachines with complex body plans, reconfigurable shape and controllable motility. Selective reprogramming of the mechanical design and magnetic anisotropy of body parts dynamically modulates the swimming characteristics of the micromachines. We find that tail and body morphologies together determine swimming efficiency and, unlike for rigid swimmers, the choice of magnetic field can subtly change the motility of soft microswimmers.

Figure 1. Reconfigurable body plans for soft micromachines inspired from microorganisms.

The versatile body plans of microorganisms composed of (a) a variety of body design and propeller mechanisms and (b) materials and architectures that can be utilized for morphological adaptation. (c) Schematic of the batch fabrication of biomimetic soft micromachines. This process enables photopatterning of microstructures with various shapes, as well as the ability to fix the magnetic nanoparticles (MNPs) in the structure. Flagellated soft micromachines with a bilayer head and monolayer tail are fabricated by sequential photopatterning of magnetic hydrogel nanocomposites. First, a mixture of photocurable non-swelling hydrogel and MNPs is injected into a microfabricated chamber (constituting the supporting layer) and a uniform magnetic field is applied in direction 1. Second, a mixture of photocurable swelling thermo-responsive hydrogel layer is patterned on top of the supporting layer and a uniform magnetic field is applied in direction 2. Finally, a monolayer tail embedded with aligned nanoparticles in direction 3 is attached to the previous bilayer structure using the same process. Every layer has its own fixed magnetic axis denoted by magnetic axis 1 (MA1), magnetic axis 2 (MA2) and magnetic axis 3 (MA3). (d) Anisotropic swelling behaviour controlled by the alignment of MNPs along prescribed 3D pathways and selective patterning of supporting layers results in 3D functional micromachines. The folding axis 1 and folding axis 2 denote the direction of folding for each compartment. The micromachine possesses multiple different magnetic axes, which determine the motility when the magnetic field is applied. The flagellated micromachine, which contains self-assembled MNPs, performs controllable swimming in a 3D space under a homogeneous rotating magnetic field . (e) The soft micromachine can be programmed to transform its shape and perform a different propulsion mechanism when exposed to external near-infrared (NIR) heating. (f–h) Optical images of flagellated soft micromachines with complex body plans. MA1 and MA3 denote the magnetic axis in the head and the tail, respectively. Scale bars, 500 μm.

Figure 2. Programming the morphology of the flagelled soft micromachines.

(a) Formation of compound micromachines with a bilayer head and monolayer tail. Due to lack of particle alignment, the folding axis of the head is solely determined by the edge effect. Finite element modelling (FEM) simulations visualize internal stress distribution. (b) By reinforcing the magnetic nanoparticles (MNPs) inside the supporting layer, the folding of the head can be decoupled from the geometric effects and directly controlled by the particle alignment. Scale bars, 500 μm. The final 3D shape of the helical structures after autonomous folding of hydrogel (c) bilayers and (d) monolayers is controlled by the alignment of embedded MNPs. The orientation of MNPs alignment (denoted by α) generates a helical angle (denoted by θ) given by θ=90°−α in bilayer structures and θ=α in monolayer structures. Scale bars, 1 mm.

Figure 3. Programmable transformation of morphology and magnetic anisotropy.

(a) Optical images of the bilayer tube and monolayer helix (with a helical angle of 45°) at different ambient temperatures. (b) The measured and predicted folding radius of the monolayer and bilayer structures at different temperatures. Each dot represents the average radius for three different machines with identical cylindrical shapes ±s.e.m. (standard error of the mean). (c) Programming the magnetization of micromachines generated after shape transformation. The alignment of the magnetic nanoparticles (MNPs) embedded inside the responsive layer determines the magnetic axis of soft micromachines in their transformed state. While magnetization generated with planar alignment of MNPs can be reconfigured during the refolding process, the machines with magnetization in the out-of-plane direction display the same magnetic axis. A static uniform magnetic field is applied to identify the magnetic anisotropy. Scale bars, 1 mm.

Figure 4. Programming the motility of flagellated soft micromachines.

(a) Time lapse optical images of two different types of compound micromachines driven by rotating uniform magnetic fields. Scale bars, 2 mm. (b) The forward velocity of the compound machines for micromachines with planar and helical flagella with respect to the frequency of rotating magnetic field. The 3D trajectories of the flagellated soft machines with (c) planar flagellum and (d) helical flagellum recorded by two camearas visualizing the workspace from the top and side view. (e) A reconfigurable compound soft micromachine switching shapes between a long slender form and a compact stumpy form. (f) The forward speed of the micromachine at different morphological states driven with the same strength and frequency of rotating magnetic field. All bar graphs represent average ±s.e.m. (N=6 measurements per micromachine, three different micromachines).