Biomimetic porous high-density polyethylene/polyethylene- grafted-maleic anhydride scaffold with improved in vitro cytocompatibility

Design, Modeling, and Experimental Validation of an Active Microcatheter Driven by Shape Memory Effects

Microcatheters capable of active guidance have been proven to be effective and efficient solutions to interventional surgeries for cardiovascular and cerebrovascular diseases. Herein, a novel microcatheter made of two biocompatible materials, shape memory alloy (SMA) and polyethylene (PE), is proposed. It consists of a reconfigurable distal actuator and a separate polyethylene catheter. The distal actuator is created via embedding U-shape SMA wires into the PE base, and its reconfigurability is mainly dominated by the shape memory effect (SME) of SMA wires, as well as the effect of thermal mismatch between the SMA and PE base. A mathematical model was established to predict the distal actuator's deformation, and the analytical solutions show great agreement with the finite element results. Structural optimization of such microcatheters was carried out using the verified analytical model, followed by fabrication of some typical prototypes. Experimental testing of their mechanical behaviors demonstrates the feasibility of the structural designs, and the reliability and accuracy of the mathematical model. The active microcatheter, together with the prediction model, will lay a solid foundation for rapid development and optimization of active navigation strategies for vascular interventions.

Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO₂ and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO₂ and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.