Carbon nanotubes: New capabilities through noncovalent functionalization and application toward biosensing

Noncovalent chemistry has been applied to the sidewalls of carbon nanotubes (CNTs) to impart novel functionalities and enable new capabilities such as conjugation to biomolecules and site-selective polymerization. The polyaromatic hydrocarbon pyrene---as part of the bifunctional molecule 1-pyrenebutanoic acid succinimidyl ester---is discovered to serve as an effective anchor on the sidewalls of CNTs for the immobilization of proteins in the aqueous phase. Additionally, a pyrene anchor is used to immobilize a ruthenium-based polymerization initiator selectively on the CNTs, after which polymerization of norbornene results in a uniform polymer coating directly on the nanotubes. Next, the feasibility of adapting CNT field effect transistors (FETs) to aqueous phase biomolecule detection is demonstrated with the immobilization of the enzyme glucose oxidase on CNT devices, affording selective sensing of glucose at sub-millimolar concentrations. Further investigation of CNT biosensors reveals the issue of non-specific binding of protein and its potential complication for future designs of such devices. As a way to prevent unwanted protein adsorption, various blocking agents are studied---with the surfactant Tween 20 eventually emerging as the most effective in imparting adsorption resistance to CNTs. A functionalization scheme is then devised to enable covalent attachment of receptor biomolecules to Tween 20, thereby allowing the detection of the binding of protein targets with specific affinity for the immobilized receptors. Further design optimization has resulted in a new generation of CNT biosensors---highly miniaturized, microarrayable and capable of being monitored in multiplex. Controlled adsorption studies with the new design affords significant elucidation into the mechanism by which protein adsorption in the aqueous phase is able to modulate the electrical conductance of CNT devices and yields considerable insight for the further optimization of new architectures.