Increased carbon nanotube area density after catalyst generation from cobalt disilicide using a cyclic reactive ion etching approach

We used a cyclic reactive ion etching (RIE) process to increase the Co catalyst density on a cobalt disilicide (CoSi2) substrate for carbon nanotube (CNT) growth. Each cycle of catalyst formation consists of a room temperature RIE step and an annealing step at 450 °C. The RIE step transfers the top-surface of CoSi2 into cobalt fluoride; while the annealing reduces the fluoride into metallic Co nanoparticles. We have optimized this cyclic RIE process and determined that the catalyst density can be doubled in three cycles, resulting in a final CNT shell density of 6.6 × 10 11 walls·cm-2. This work demonstrates a very effective approach to increase the CNT density grown directly on silicides. © 2014 AIP Publishing LLC.

Self-organization of reflexive behavior from spontaneous motor activity.

In mammals, the development of reflexes is often regarded as an innate process. However, recent findings show that fetuses are endowed with favorable conditions for ontogenetic development. In this article, we hypothesize that the circuitry of at least some mammalian reflexes can be self-organized from the sensory and motor interactions brought forth in a musculoskeletal system. We focus mainly on three reflexes: the myotatic reflex, the reciprocal inhibition reflex, and the reverse myotatic reflex. To test our hypothesis, we conducted a set of experiments on a simulated musculoskeletal system using pairs of agonist and antagonist muscles. The reflex connectivity is obtained by producing spontaneous motor activity in each muscle and by correlating the resulting sensor and motor signals. Our results show that, under biologically plausible conditions, the reflex circuitry thus obtained is consistent with that identified in relation to the analogous mammalian reflexes. In addition, they show that the reflex connectivity obtained depends on the morphology of the musculoskeletal system as well as on the environment that it is embedded in.

Soft robotics: The next generation of intelligent machines

There has been an increasing interest in applying biological principles to the design and control of robots. Unlike industrial robots that are programmed to execute a rather limited number of tasks, the new generation of bio-inspired robots is expected to display a wide range of behaviours in unpredictable environments, as well as to interact safely and smoothly with human co-workers. In this article, we put forward some of the properties that will characterize these new robots: soft materials, flexible and stretchable sensors, modular and efficient actuators, self-organization and distributed control. We introduce a number of design principles; in particular, we try to comprehend the novel design space that now includes soft materials and requires a completely different way of thinking about control. We also introduce a recent case study of developing a complex humanoid robot, discuss the lessons learned and speculate about future challenges and perspectives.