Date of Award

2026-05-01

Degree Name

Doctor of Philosophy

Department

Mechanical Engineering

Advisor(s)

Anabel A. Marquez

Abstract

The rapid demand for piezoelectric sensor fabrication requires the integration of material design, optimization strategies, and predictive modeling. Hence, this work focuses on developing, characterizing, and optimizing next-generation piezoelectric composites through additive manufacturing, experimental testing, and machine-learning-based predictive modeling. Through the combination of fabrication, testing, and data-driven modeling, this research established a framework for accelerating the development of enhanced piezoelectric sensors for aerospace, biomedical, structural monitoring, and energy applications. The first study investigates the fabrication of flexible piezoelectric polyvinylidene fluoride (PVDF) composites through solvent-evaporation-assisted direct ink writing (SE-DIW). PVDF inks reinforced with varying concentrations of barium titanate (BTO) and multiwalled carbon nanotubes (MWCNTs) were developed to enhance the electroactive β-phase and improve piezoelectric performance. The combined effects of nozzle-induced shear during printing and contact poling under an electric field of 4 kV at 90 °C promoted polar phase formation. The fabricated composites maintained flexibility while exhibiting approximately 10% shrinkage after processing. Among the compositions investigated, PVDF containing 20 wt% BTO achieved the highest piezoelectric coefficient (d33) of 53.25 pC/N while maintaining a Young's modulus of 4.87 MPa, demonstrating the potential of SE-DIW for flexible piezoelectric device fabrication. The second study investigates PVDF composites enhanced with BTO and MWCNTs fabricated through phase-immersion direct ink writing (PI-DIW). A Gaussian process (GP) model was implemented to optimize the electroactive β-phase content in PVDF under varying additive loadings and processing conditions. The predictive framework achieved an accuracy of 97.6% with quantified uncertainty and experimental validation, demonstrating the effectiveness of machine-learning-assisted optimization for piezoelectric material design with limited experimental datasets. The third study develops a polydimethylsiloxane (PDMS)/BTO/cellulose nanocrystal (CNC) composite for wearable sensing applications, integrating the flexibility of PDMS, the piezoelectric response of BTO, and the reinforcing electromechanical behavior of CNC. The composite was fabricated as a sandwich structure with PDMS/MWCNT electrodes and experimentally evaluated under vibration and physiological sensing conditions. Under cyclic compression loading of 250 lb, the composite containing 40 wt% BTO exhibited a maximum voltage output of 1.7 V. To further enhance sensor performance, H-tree-inspired fractal geometries were fabricated and evaluated in the fourth study to investigate the influence of structural design on piezoelectric response. Finite element analysis (FEA) simulations were implemented to optimize geometric configurations and demonstrated strong agreement with experimental results, achieving approximately 92% accuracy. This approach enabled systematic evaluation of geometric design variations for improved electromechanical performance and demonstrated the potential of simulation-driven optimization for complex piezoelectric architectures. Together, these studies demonstrate a comprehensive strategy for advancing piezoelectric polymer composites, spanning material formulation, additive manufacturing, predictive modeling, and device-level design optimization. By coupling experimental characterization with computational and machine-learning-based approaches, this work establishes a pathway toward scalable, multifunctional piezoelectric systems tailored for aerospace, biomedical, wearable, and structural health monitoring applications.

Language

en

Provenance

Received from ProQuest

File Size

130 p.

File Format

application/pdf

Rights Holder

Aaron A. Rodriguez

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Available for download on Wednesday, June 16, 2027

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