INDUSTRY NEWS

A research model developed at Purdue University explains why biosensors using carbon nanotubes as a sensing element work better than other sensor types.

Biosensors integrate electronic circuitry with natural molecules, such as antibodies or DNA, which enable the devices to capture target molecules. In efforts to design more-sensitive devices, engineers have created sensors with various geometries: some capture the biomolecules on a flat surface, others use a single cylindrical nanotube as a sensing element, and still others use several nanotubes, arranged in a crisscross pattern. Researchers have known for several years that small sensors have greater sensitivity than larger ones; the most sensitive devices are those built at the nanometer scale such as hollow carbon nanotubes. “Everyone presumes that nanometer-scale sensors are simply better because they are closer to the size of the target molecules,” explains Ashraf Alam, a Purdue professor of electrical and computer engineering who led the research. “What we found, however, was not that smaller sensors are better at detecting target molecules, but that they are better at capturing target molecules.” A sensor using a single nanotube captures molecules effectively because it eliminates diffusion slowdown, a phenomenon that decreases the rate of molecule capture over time. As a result, target molecules move faster toward single nanotubes than other structures.

“Many universities and companies are conducting experiments in biosensors,” Alam says. “The problem is that, until now, there was no way to consistently interpret the wealth of data available to the research community.” According to Alam, the new model “provides a completely different perspective on analyzing and interpreting [biosensor] data.”

The researchers expect their work to further lab-on-a-chip technology and to benefit fields such as medical diagnostics and environmental monitoring.

One obstacle to learning why smaller sensors work better is that the analysis is too computationally difficult to perform using conventional approaches. “You could not effectively analyze the physics behind these biosensors using brute force with massive computing resources,” explains Purdue graduate student Pradaeep Nair. The researchers solved this problem by using a mathematical technique called Cantor transformation to simplify the computations needed for the analysis.

For more information, contact Purdue University, West Lafayette, IN 47907, USA; phone: +1 765 4949035; e-mail: alam@purdue.edu; Internet: http://engineering.purdue.edu.