Effect of Porosity and Pore Shape on the Mechanical and Biological Properties of Additively Manufactured Bone Scaffolds.

This study investigates the effect of porosity and pore shape on the biological and mechanical behavior of additively manufactured scaffolds for bone tissue engineering (BTE). Polylactic acid scaffolds with varying porosity levels (15-78%) and pore shapes, including regular (rectangular pores), gyroid, and diamond (triply periodic minimal surfaces) structures, are fabricated by fused filament fabrication. Murine-derived macrophages and human bone marrow-derived mesenchymal stromal cells (hBMSCs) are seeded onto the scaffolds. The compressive behavior and surface morphology of the scaffolds are characterized. The results show that scaffolds with 15%, 30%, and 45% porosity display the highest rate of macrophage and hBMSC growth. Gyroid and diamond scaffolds exhibit a higher rate of macrophage proliferation, while diamond scaffolds exhibit a higher rate of hBMSC proliferation. Additionally, gyroid and diamond scaffolds exhibit better compressive behavior compared to regular scaffolds. Of particular note, diamond scaffolds have the highest compressive modulus and strength. Surface morphology characterization indicates that the surface roughness of diamond and gyroid scaffolds is greater than that of regular scaffolds at the same porosity level, which is beneficial for cell attachment and proliferation. This study provides valuable insights into porosity and pore shape selection for additively manufactured scaffolds in BTE.

Predictive modelling of drop ejection from damped, dampened wings by machine learning.

The high frequency, low amplitude wing motion that mosquitoes employ to dry their wings inspires the study of drop release from millimetric, forced cantilevers. Our mimicking system, a 10-mm polytetrafluoroethylene cantilever driven through ±1 mm base amplitude at 85 Hz, displaces drops via three principal ejection modes: normal-to-cantilever ejection, sliding and pinch-off. The selection of system variables such as cantilever stiffness, drop location, drop size and wetting properties modulates the appearance of a particular ejection mode. However, the large number of system features complicate the prediction of modal occurrence, and the transition between complete and partial liquid removal. In this study, we build two predictive models based on ensemble learning that predict the ejection mode, a classification problem, and minimum inertial force required to eject a drop from the cantilever, a regression problem. For ejection mode prediction, we achieve an accuracy of 85% using a bagging classifier. For inertial force prediction, the lowest root mean squared error achieved is 0.037 using an ensemble learning regression model. Results also show that ejection time and cantilever wetting properties are the dominant features for predicting both ejection mode and the minimum inertial force required to eject a drop.