Fractal patterns in the parameter space of a bistable Duffing oscillator.

We study the dissipative bistable Duffing oscillator with equal energy wells and observe fractal patterns in the parameter space of driving frequency, forcing amplitude, and damping ratio. Our numerical investigation reveals the Hausdorff fractal dimension of the boundaries that separate the oscillator's intrawell and interwell behaviors. Furthermore, we categorize the interwell behaviors as three steady-state types: switching, reverting, and vacillating. While fractal patterns in the phase space are well known and heavily studied, our results point to another research direction about fractal patterns in the parameter space. Another implication of this study is that the vibration of a continuous bistable system modeled using a single-mode approximation also manifests fractal patterns in the parameter space. In addition, our findings can guide the design of next-generation bistable and multistable mechanical metamaterials.

Ingestible Functional Magnetic Robot with Localized Flexibility (MR-LF).

The integration of an ingestible dosage form with sensing, actuation, and drug delivery capabilities can enable a broad range of surgical-free diagnostic and treatment strategies. However, the gastrointestinal (GI) tract is a highly constrained and complex luminal construct that fundamentally limits the size of an ingestible system. Recent advancements in mesoscale magnetic crawlers have demonstrated the ability to effectively traverse complex and confined systems by leveraging magnetic fields to induce contraction and bending-based locomotion. However, the integration of functional components (e.g., electronics) in the proposed ingestible system remains fundamentally challenging. Herein, the creation of a centralized compartment in a magnetic robot by imparting localized flexibility (MR-LF) is demonstrated. The centralized compartment enables MR-LF to be readily integrated with modular functional components and payloads, such as commercial off-the-shelf electronics and medication, while preserving its bidirectionality in an ingestible form factor. The ability of MR-LF to incorporate electronics, perform drug delivery, guide continuum devices such as catheters, and navigate air-water environments in confined lumens is demonstrated. The MR-LF enables functional integration to create a highly-integrated ingestible system that can ultimately address a broad range of unmet clinical needs.

Embedded 3D printing of multi-layer, self-oscillating vocal fold models.

The biomechanics of human voice production are commonly studied using benchtop silicone vocal fold models that mimic the vibration of their in vivo counterparts. These models often have multiple layers of differing stiffness that represent human vocal fold tissue layers and are fabricated using a multi-step casting process. The purpose of the present study is to introduce and demonstrate a process for fabricating functional multi-layer vocal fold models using an alternative approach, termed embedded 3D printing, that is a hybrid of casting and 3D printing. In this paper the fabrication process is described. Analysis of the resulting geometric and stiffness characteristics of the layers, including layer elastic modulus values ranging from less than 1 kPa to approximately 40 kPa, is presented. The results of tests demonstrating that the models are capable of sustained phonomimetic vibration are given. Capabilities and limitations of the embedded 3D printing process are discussed. It is concluded that the process has the potential to contribute to voice biomechanics research by facilitating prospective improvements in the fabrication, design, and functionality of multi-layer vocal fold models.

3D Printing Low-Stiffness Silicone Within a Curable Support Matrix.

Embedded 3D printing processes involve extruding ink within a support matrix that supports the ink throughout printing and curing. In once class of embedded 3D printing, which we refer to as "removable embedded 3D printing," curable inks are printed, cured, then removed from the uncured support matrix. Removable embedded 3D printing is advantageous because low-viscosity inks can be patterned in freeform geometries which may not be feasible to create via casting and other printing processes. When printing solid-infill geometries, however, uncured support matrix becomes trapped within the prints, which may be undesirable. This study builds on previous work by formulating a support matrix for removable embedded 3D printing that cures when mixed with the printed silicone ink to solve the problem of trapped, uncured support matrix within solid-infill prints. Printed specimens are shown to have a nearly isotropic elastic modulus in directions perpendicular and parallel to the printed layers, and a decreased modulus and increased elongation at break compared to specimens cast from the ink. The rheological properties of the support matrix are reported. The capabilities of the printer and support matrix are demonstrated by printing a variety of geometries from four UV and addition-cure silicone inks. Shapes printed with these inks range by nearly two orders of magnitude in stiffness and have failure strains between approximately 50 and 250%, suggesting a wide range of potential applications for this printing process.

A 3D-Printed Self-Learning Three-Linked-Sphere Robot for Autonomous Confined-Space Navigation.

Reinforcement learning control methods can impart robots with the ability to discover effective behavior, reducing their modeling and sensing requirements, and enabling their ability to adapt to environmental changes. However, it remains challenging for a robot to achieve navigation in confined and dynamic environments, which are characteristic of a broad range of biomedical applications, such as endoscopy with ingestible electronics. Herein, a compact, 3D-printed three-linked-sphere robot synergistically integrated with a reinforcement learning algorithm that can perform adaptable, autonomous crawling in a confined channel is demonstrated. The scalable robot consists of three equally sized spheres that are linearly coupled, in which the extension and contraction in specific sequences dictate its navigation. The ability to achieve bidirectional locomotion across frictional surfaces in open and confined spaces without prior knowledge of the environment is also demonstrated. The synergistic integration of a highly scalable robotic apparatus and the model-free reinforcement learning control strategy can enable autonomous navigation in a broad range of dynamic and confined environments. This capability can enable sensing, imaging, and surgical processes in previously inaccessible confined environments in the human body.