The Effects of Self-Polymerized Polydopamine Coating on Mechanical Properties of Polylactic Acid (PLA)-Kenaf Fiber (KF) in Fused Deposition Modeling (FDM).

This research examines the impact of self-polymerized polydopamine (PDA) coating on the mechanical properties and microstructural behavior of polylactic acid (PLA)/kenaf fiber (KF) composites in fused deposition modeling (FDM). A biodegradable FDM model of natural fiber-reinforced composite (NFRC) filaments, coated with dopamine and reinforced with 5 to 20 wt.% bast kenaf fibers, was developed for 3D printing applications. Tensile, compression, and flexural test specimens were 3D printed, and the influence of kenaf fiber content on their mechanical properties was assessed. A comprehensive characterization of the blended pellets and printed composite materials was performed, encompassing chemical, physical, and microscopic analyses. The results demonstrate that the self-polymerized polydopamine coating acted as a coupling agent, enhancing the interfacial adhesion between kenaf fibers and the PLA matrix and leading to improved mechanical properties. An increase in density and porosity was observed in the FDM specimens of the PLA-PDA-KF composites, proportional to their kenaf fiber content. The enhanced bonding between kenaf fiber particles and the PLA matrix contributed to an increase of up to 13.4% for tensile and 15.3% for flexural in the Young's modulus of PLA-PDA-KF composites and an increase of up to 30% in compressive stress. The incorporation of polydopamine as a coupling agent in the FDM filament composite led to an improvement in tensile, compressive, and flexural stresses and strain at break, surpassing that of pure PLA, while the reinforcement provided by kenaf fibers was enhanced more by delayed crack growth, resulting in a higher strain at break. The self-polymerized polydopamine coatings exhibit remarkable mechanical properties, suggesting their potential as a sustainable material for diverse applications in FDM.

Analytical and computational sliding wear prediction in a novel knee implant: a case study.

Osteoarthritis (OA) is a commonly occurring cartilage degenerative disease. The end stage treatment is Total Knee Arthroplasty (TKA), which can be costly in terms of initial surgery, but also in terms of revision knee arthroplasty, which is quite often required. A novel conceptual knee implant has been proposed to function as a reducer of stress across the joint surface, to extend the period of time before TKA becomes necessary. The objective of this paper is to develop a computational model which can be used to assess the wear arising at the implant articulating surfaces. Experimental wear coefficients were determined from physical testing, the results of which were verified using a semi-analytical model. Experimental results were incorporated into an anatomically correct computational model of the knee and implant. The wear-rate predicted for the implant was 27.74 mm3 per million cycles (MC) and the wear depth predicted was 1.085 mm/MC. Whereas the wear-rate is comparable to that seen in conventional knee implants, the wear depth is significantly higher than for conventional knee prostheses, and indicates that, in order to be viable, wear-rates should be reduced in some way, perhaps by using low-wear polymers.