A Transport-Driven Conceptual Framework for the Biodegradation of FFF-Printed PLA/PHB Structures: Integrating...

Biodegradation of fused filament fabrication (FFF)-printed poly(lactic acid)/polyhydroxybutyrate (PLA/PHB) structures is governed by complex interactions between material composition, structural design, and degradation environment. Although these factors have been extensively investigated, they are typically evaluated independently, limiting a comprehensive understanding of their combined influence on degradation behaviour. This study synthesizes experimental evidence from a series of complementary studies on FFF-printed PLA/PHB systems to develop a transport-driven conceptual framework for interpreting biodegradation mechanisms. The integrated findings show that structural porosity increases fluid uptake (approximately 10% in dense structures versus 20% in porous structures) and promotes greater mass loss (25-40% after 45 days), while material modifications, including plasticizers and ceramic additives, influence local pH evolution and mechanical stability during degradation. The degradation response was also strongly dependent on the surrounding medium, with PBS maintaining relatively stable pH, saline and Hank's solutions promoting acidification, and urea-based media leading to alkalization. Collectively, these observations indicate that fluid absorption and diffusion consistently mediate the interactions between material composition, scaffold architecture, and degradation environment, thereby governing the progression of biodegradation. Based on these experimentally supported relationships, a transport-driven conceptual framework is proposed to provide a unified qualitative interpretation of biodegradation in FFF-printed PLA/PHB structures. Rather than presenting new experimental data, this work offers an integrative perspective that may facilitate the interpretation of degradation behaviour, support the rational design of biodegradable scaffolds, and provide a foundation for future quantitative and predictive models.