OAK RIDGE, Tenn., April 22, 2010 -- Mimicking the human nervous system for bionic applications could become a reality with the help of a method developed at Oak Ridge National Laboratory to process carbon nanotubes.
While these nanostructures have electrical and other properties that make them attractive to use as artificial neural bundles in prosthetic devices, the challenge has been to make bundles with enough fibers to match that of a real neuron bundle. With current technology, the weight alone of wires required to match the density of receptors at even the fingertips would make it impossible to accommodate. Now, by adapting conventional glass fiber drawing technology to process carbon nanotubes into multichannel assemblies, researchers believe they are on a path that could lead to a breakthrough.
"Our goal is to use our discovery to mimic nature's design using artificial sensors to effectively restore a person's ability to sense objects and temperatures," said Ilia Ivanov, a researcher in the Center for Nanophase Materials Sciences Division. Ivanov and colleagues at ORNL recently published a paper in Nanotechnology that outlines the method of processing loose carbon nanotubes into a bundle with nearly 20,000 individual channels.
Ultimately, the goal is to duplicate the function of a living system by combining the existing technology of glass fiber drawing with the multi-functionality of sub-micron (0.4 micron) scale carbon nanotubes, according to Ivanov, who described the process.
"We make this material in a way similar to what you may have done in high school when making a glass capillary over a Bunsen burner," Ivanov said. "There, you would take the glass tube, heat it up and pull, or draw, as soon as the glass became soft."
Ivanov and John Simpson of the Measurement Science and Systems Engineering Division are doing something similar except they use thousands of glass tubes filled with carbon nanotube
'/>"/>
| Contact: Ron Walli wallira@ornl.gov 865-576-0226 DOE/Oak Ridge National Laboratory Source:Eurekalert |