Towards Smart Textiles Produced from Electrospun PVDF-HFP and its Composites

Abstract

As exploring new energy harvesting methods is gaining momentum as a solution to the current environmental crisis, conversion from mechanical energy to electrical power in a green, sustainable fashion has been comprehensively assessed. Piezoelectric materials offer a scalable mechanism for energy conversion via intrinsic polarization properties. Advancements in the manufacturing of nanoscale materials have enabled the production of piezoelectric materials with high flexibility, enhanced piezoelectric performance, and increased versatility in applications. Polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) is one such piezoelectric material with immense potential for sensing and energy harvesting applications, such as smart textiles, pressure sensors, or biomedical sensors. The incorporation of cellulose nanocrystals (CNC) in PVDF-HFP presents a promising route to improve its piezoelectric properties. This thesis investigates the enhancement of the piezoelectric properties of PVDF-HFP through CNC reinforcement, explores applications in sensing and drug delivery, and models the manufacturing process for better tailorability of the piezoelectric properties. The thesis starts with a literature review accompanied by machine learning to predict the piezoelectric output of electrospun polyvinylidene fluoride (PVDF) mats with various fillers under mechanical stress or strain. PVDF was selected for its similar piezoelectric properties to PVDF-HFP and the large quantity of related research. This work provided a predictive framework that could be extended to PVDF-HFP, demonstrating that enhancing the β-phase content was the primary strategy for improving piezoelectric performance. Specifically, it highlighted that incorporating nanofillers could increase the voltage output of electrospun PVDF-based polymer fibers with a classification model employed to categorize output voltage ranges based on practical electronic applications. From the literature review and machine learning model, nanofillers were found effective in improving the piezoelectric properties of PVDF-based polymers. Thus, the next study focused on electrospinning of PVDF-HFP nanofiber mats reinforced with CNC. The incorporation of CNC significantly enhanced the β-phase content of PVDF-HFP and its piezoelectric output. The study systematically varied CNC concentrations and other experimental parameters to determine the optimal conditions for β-phase formation and mechanical reinforcement. The application of the PVDF-HFP/CNC mat as a pressure sensing device that could be employed as a sensor for detecting carpal tunnel syndrome for pianists was achieved. Based on the optimized conditions established for random electrospinning, the investigation extended to yarn electrospinning of PVDF-HFP/CNC. Yarn electrospinning yielded twisted continuous yarns. PVDF-HFP yarns offered great uniformity and mechanical properties, as well as significantly enhanced piezoelectric performance compared to randomly oriented nanofiber mats. One application explored in this thesis was the use of these yarns in touchscreen gloves, providing real-time motion-sensing capabilities. This innovation can advance human-device interaction while offering health monitoring benefits. The application of PVDF-HFP/CNC was explored beyond its piezoelectric properties in the thesis by investigating its potential for drug delivery. PVDF-HFP/CNC yarns exhibited pH-responsive drug release performance with sustained structural stability at pH and temperature environments suitable for wound healing. Their drug release behaviors conformed to the Ritger-Peppas model, suggesting Fickian diffusion dominated the release kinetics. After loading with levofloxacin, PVDF-HFP/CNC yarns exhibited strong antimicrobial activity and cytocompatibility, emphasizing their potential for advancing antibacterial wound healing in suture applications. To further understand how the manufacturing method affected the properties of yarn electrospun products, computational fluid dynamics (CFD) models were constructed for the random and yarn electrospinning processes. Taylor cone formation and jet trajectories in stable and unstable regions were simulated to understand yarn formation mechanisms. This study provided critical insights into how the simultaneous presence of two electric fields promoted jet collision during electrospinning, which was never investigated in the literature before. The CFD model, subjected to future refinements, can serve as a predictive tool for manufacturing. This study was the first to utilize machine learning for predicting the piezoelectric performance of electrospun PVDF-baed polymers and their composites. Yarn electrospinning of PVDF-HFP/CNC was introduced for the first time, with novel applications explored in motion-sensing touchscreen gloves and drug-loaded sutures. The theoretical simulation and analysis revealed the underlying mechanisms of random and yarn electrospinning, providing a deeper understanding of the process. The findings offer valuable insights into developing next-generation smart textiles and medical devices, highlighting the versatile and impactful role of PVDF-HFP/CNC composites in emerging technologies.