Thesis

Bio-inspired sensors : from hair-like sensilla of arachnids and insects to 3d-printed acoustic and airflow sensors

Creator
Rights statement
Awarding institution
  • University of Strathclyde
Date of award
  • 2026
Thesis identifier
  • T18032
Person Identifier (Local)
  • 202268508
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • Leonardo da Vinci observed that “Human subtlety will never devise an invention more beautiful, more simple or more direct than does nature because in her inventions nothing is lacking, and nothing is superfluous.” This statement remains profoundly relevant, as natural systems continue to inspire technological innovation. Among these, the hair mechanoreceptors of insects and arachnids represent highly efficient biological sensors capable of detecting airflows and low-frequency near-field sounds. Variants of these structures are hypothesized to sense acceleration, direct contact, and even thermal or chemical stimuli, suggesting an evolutionary link to olfactory and infrared sensilla. Their multifunctionality has motivated extensive research into bioinspired sensing technologies. Historically, such sensors have been fabricated using micro-electromechanical systems (MEMS), a technology originating in the mid-20th century and widely adopted by the 1990s. MEMS enabled miniaturization and integration of sensors but remains constrained by complex, costly manufacturing processes. In contrast, additive manufacturing techniques, particularly 3D printing, offer simplified workflows, reduced costs, and greater design flexibility. Digital Light Processing (DLP), a photopolymer-based 3D-printing method, is particularly promising for producing sensor arrays and enabling batch fabrication, thereby reducing production time and improving scalability. This thesis explores the design and fabrication of bioinspired sensors using DLP-based 3D printing. Two prototypes are presented: an acoustic sensor responsive to specific frequency bands and an airflow sensor capable of converting wind speed variations into electrical signals. While the acoustic sensor demonstrated mechanical functionality, electrical transduction remains a challenge; its successful implementation could enable frequency-selective audio acquisition, reducing computational demands in applications such as speech recognition. The airflow sensor achieved reliable electrical output and demonstrates potential for large-scale deployment in arrays, with applications spanning fluid dynamics, autonomous navigation, biomedical monitoring, and meteorology. These findings underscore the potential of DLP-based additive manufacturing to advance bioinspired sensing through cost-effective, scalable, and versatile solutions. The PhD research work showed that it is possible to 3D-print structures bio-inspired by nature that can react to different acoustic frequency bands, and that it is possible to repurpose these structures to sense other physical phenomena, for example airflow velocity.
Advisor / supervisor
  • Windmill, James
  • Reid, Andrew Baxter
Resource Type
DOI
Funder

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