Selective carbon nanotube sensors

Oct. 26, 2006
Researchers from Arizona State University and Motorola Labs, the applied research arm of Motorola Inc. have developed sensors based on carbon nanotubes.

An atomic force microscopy image of a single wall nanotube device along with a schematic representation of the nanosensor (Photo credit: Erica Forzani, Arizona State University).

Early tests show the devices can detect heavy metal ions in water down to parts/trillion levels.

Specifically, the researchers developed a method for applying peptides to single-walled carbon nanotubes in field-effect transistors. "This is a fairly general sensor platform for all kinds of applications," said Nongjian Tao, an electrical engineering professor at ASU. "We tested heavy metal ions in water, but the platform can be applied to many other areas including biosensors for medical uses."

The sensors are based on the novel properties of carbon nanotubes and peptides made from 20 or more amino acids, ASUs Tao explains. "Peptides recognize and detect various chemical species with high sensitivity and selectivity." Changing the sequence of amino acids lets the researchers "tune" the peptides to recognize different compounds, Tao says.

The researchers developed peptides to individually detect nickel and copper. If only the nickel peptide were used, the sensor could only find nickel and would be "blind" to other heavy metal ions (copper, lead or zinc) passing over the carbon nanotubes.

Additionally, the research could lead to nanosensors able to detect biological and chemical agents at low concentrations. "Such devices may one day be a vital tool for public safety and homeland security," says Vida Ilderem, vice president of the Embedded Systems Research Labs at Motorola, Tempe, Ariz. Future work will also investigate sensors for detecting biological molecules such as RNA sequences.

The researchers report the advance in a paper, "Tuning the chemical selectivity of SWNT-FETs for detection of heavy metal ions," published in the journal Small.

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