Biomimetic bone-like composites fabricated through an automated alternate soaking process.

Hydroxyapatite-gelatin composites have been proposed as suitable scaffolds for bone and dentin tissue regeneration. There is considerable interest in producing these scaffolds using biomimetic methods due to their low energy costs and potential to create composites similar to the tissues they are intended to replace. Here an existing process used to coat a surface with hydroxyapatite under near physiological conditions, the alternate soaking process, is modified and automated using an inexpensive "off the shelf" robotics kit. The process is initially used to precipitate calcium phosphate coatings. Then, in contrast to previous utilizations of the alternate soaking process, gelatin was added directly to the solutions in order to co-precipitate hydroxyapatite-gelatin composites. Samples were investigated by Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive X-ray spectroscopy and nanoindentation. Calcium phosphate coatings formed by the alternate soaking process exhibited different calcium to phosphate ratios, with correspondingly distinct structural morphologies. The coatings demonstrated an interconnected structure with measurable mechanical properties, even though they were 95% porous. In contrast, hydroxyapatite-gelatin composite coatings over 2mm thick could be formed with little visible porosity. The hydroxyapatite-gelatin composites demonstrate a composition and mechanical properties similar to those of cortical bone.

Learning the dynamics of the external world: Brain inspired learning for robotic applications

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics. © 2006.