Characterization of three‐dimensional printed thermal‐stimulus polylactic acid‐hydroxyapatite‐based shape memory scaffolds

Abstract

The shape memory polymers have attained huge attention as smart materials owing to their enormous benefits in the context of the common class of thermoplastics. In this study, novel thermal‐stimulus‐based hydroxyapatite (HA) reinforced polylactic acid (PLA) scaffolds have been developed through three‐dimensional (3D) printing technology. Initially, the effect of various processing parameters, such as the proportion of HA in PLA and infill density, and level of stimulating temperature, on tensile, flexural, and compression strength of the developed composite scaffolds have been studied to understand their effects, through statistically assisted single and multiobjective optimization techniques. The shape recovery factor of the prestrained composite scaffolds has been assessed by providing thermal‐stimulus. Finally, the biological performance of the developed scaffolds has been evaluated through in vitro cell culture and wettability analysis. The morphological study of the composite scaffolds, at different stages, has been performed for understanding the effect of processing parameters on the mechanical, shape recovery effect, and biological responses. Overall, the results of the study highlighted that the 3D printed scaffolds possessed nearly 95.77% shape memory effect along with desirable mechanical, in vitro biocompatibility, and hydrophilic morphology suitable to be a potential candidate of fabricating customized biomedical devices with activated shape recovery abilities.

Microstructural analysis and multi‐response optimization of mechanical properties of bulk molding compound

“…The R 2 (pred)-value is calculated by systematically removing each observation from the data set, estimating the regression equation, and determining how well the model predicts the removed observation. [34] In addition, the high values of R 2 (adj) (diverse between 91.59% and 98.90%) and values of R 2 (pred) (diverse between 86.90% and 98.28%) reported that the predictability of all models was well.…”

Multiple-Criteria Decision-Making and Sensitivity Analysis for Selection of Materials for Knee Implant Femoral Component

“…cost, density, modulus of elasticity, tensile strength, elongation, corrosion resistance, wear resistance, and osseointegration. ij is an element of the decision matrix ‘ ’ [ ij ; = 1, 2, …, a number of alternatives (n), = 1, 2, …, number of attribute/criteria (m)], which are inputs [ 46 , 47 ]. …”

“…The weighted normalized matrix (ŴŻ ij ) is obtained by multiplying the columns of M ij with their particular allocated weight, j . Subsequently, ŴŻ ij is attained by Equation (26) [ 47 , 48 ]. …”

3D printing of shape memory polymers

“…54,55 Singh et al added different amounts of HA powder (4-8 wt%) to PLA granules, milled the mixture and prepared a filament in a twin-screw extruder. 56 Different infill percentages, numbers of outer perimeters and post-heating steps to improve the mechanical strength were compared. By this, the authors found recovery rates between approx.…”