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Inadequate oxygen and nutrient diffusion in a porous scaffold often resulted in insufficient formation of branched vasculatures, which hindered bone regeneration. In this study, interconnected porous β-tricalcium phosphate (β-TCP) scaffolds with different geometric designs of channels were fabricated and compared to discover the functionality of structure on facilitating nutrient diffusion for angiogenesis. In vitro fluid transportation and degradation of the scaffolds were performed. Cell infiltration, migration, and proliferation of human umbilical vein endothelial cells (HUVECs) on the scaffolds were carried out under both static and dynamic culture conditions. A computational simulation model and a series of immunofluorescent staining were implemented to understand the mechanism of cell behavior in response to different types of scaffolds. Results showed that geometry with multiple channels significantly accelerated the release of Ca 2+ and increased the fluid diffusion efficiency. Moreover, multiple channels promoted HUVECs' infiltration and migration in vitro. The ex vivo implantation results showed that the channels promoted cells from the rats' calvarial bone explants to infiltrate into the implanted scaffold. Multiple channels also stimulated HUVECs' proliferation prominently at both static and dynamic culturing conditions. The expression of both cell migration-related protein α5 and angiogenesis-related protein CD31 on multiplechanneled scaffolds was upregulated compared to that on the other two types of scaffolds, implying that multiple channels reinforced cell migration and angiogenesis. All the findings suggested that the geometric design of multiple channels in the porous β-TCP scaffold has promising potential to promote cell infiltration, migration, and further vascularization when implanted in vivo.

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Strategies to help reconstruct and restore haptic perception are essential for control of prosthetic limbs and robotic manipulation.

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Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.