Sensor design gets systematic

Cantor set
To prove their case mathematically, the researchers modeled both sensors. However, the jumble-of-nanowires was impossible to model accurately with traditional techniques, so the researchers invented a Cantor transformation to simplify the process.

The Cantor set, invented by mathematician Georg Cantor, averages results by iteratively removing the middle one-third of a data set—that is, after the middle third is removed, then the middle third of the two remaining thirds is removed, and the process repeats until you get down to individual observations.

By transforming their modeling data into a Cantor set, performing the simulation, then transforming the results back, they were able to simplify the simulation enough for it to be run on the nanoHub—an Internet-based parallel processor that is part of Purdue's Network for Computational Nanotechnology.

"We showed that the Cantor set has the same fractal dimension as the pick-up-stick sensors, so that any problem you want to solve about that sensor might as well as solved on the Cantor set, and the results will be the same," said Alam.

The EEs also investigated nanodot sensors because their spherical shape would appear to enable even more sensitivity than a cylindrical sensor, since molecules can collide and become attached to a sphere from even more directions than a cylinder. However, Alam and Nair's model showed that there was no great advantage to spherical nanodot sensors over cylindrical nanowires or nanotubes.

"We found that both the cylindrical and spherical sensors have comparable sensitivity—there is not as much difference as you would think," said Alam.

Currently, the researchers are using their model to discover a sensor architecture that can detect DNA sequences electronically, so that genome sequencing can be more easily automated.

"Today, genome sequencing depends on chemical detection of molecules, which is slow and cumbersome," said Alam. "What we are trying to do is invent a sensor that can electrically detect molecules types, for faster and more efficient genome sequencing."

This research was funded by the National Science Foundation and the National Institute of Health, as well as by Purdue's Network for Computational Nanotechnology and its Birck Nanotechnology Center.