Bioinspired Engineering: From Medical Devices to e-Nose Sensors

Abstract

Nature rarely solves one problem at a time. It designs systems—delicate yet resilient, selective yet responsive—that operate under multiple constraints in complex environments. This dissertation draws from that ethos, presenting a body of work that bridges implantable medical devices and electronic chemical sensors, unified by a central theme: bioinspired engineering at the intersection of materials science, fluid dynamics, and computation. In the first half of this work, I tackle long-standing challenges in biomedical implants—devices that must remain open yet resist fouling, deliver therapeutics yet prevent contamination, function reliably yet disrupt the body as little as possible. Using tympanostomy tubes as a case study, I develop subcapillary-scale conduits with curved geometries derived from capillary transport models, paired with liquid-infused surfaces that mimic the self-cleaning skins of pitcher plants. These tubes selectively transport desired fluids (e.g., therapeutics) while passively rejecting undesired ones (e.g., water, pathogens), with performance validated in vitro and in vivo. I extend these concepts to hydrocephalus catheters, where shear-driven self-cleaning and geometry-guided antifouling design prevent occlusion from cellular debris—a leading cause of implant failure. In both systems, I combine computational fluid modeling, materials engineering, and biological analogy to build devices that work with the body, not against it. The second half of the thesis turns to the invisible world of chemical vapors, where biological olfaction offers a blueprint for high-dimensional, real-time sensing. Inspired by the sniffing behavior of mammals and the diverse receptor arrays in biological noses, I construct cross-reactive metal oxide sensor arrays integrated with machine learning models capable of classifying complex vapor mixtures. These e-nose platforms are deployed across domains—from food spoilage and air quality to breath-based diagnostics—achieving high specificity and adaptability by incorporating temporal signal processing, fluid-guided delivery, and adaptive sampling schemes. I postulate how feedback from flow simulations and physics-informed neural networks can guide sensor calibration and improve robustness, mimicking how natural systems sense dynamically and respond in real time. While these domains may seem disparate—an ear tube and a chemical sensor—their design challenges and solutions are strikingly parallel. Both demand selectivity without complexity, resilience without rigidity, and the capacity to navigate the messy gradients of biology and the environment. By pairing natural design principles with modern fabrication, modeling, and data tools, this dissertation offers a cohesive framework for engineering systems that are not only inspired by nature, but perform with nature’s elegance and efficiency. Ultimately, this work illustrates that the future of biomedical and environmental technologies will not come from any single field—but from the harmonious convergence of materials, computation, and mechanics, guided by the silent logic of biology. From implant to e-nose, this is a vision of devices that sense, respond, and endure—because they are designed like life itself.