Computational Design of Chemical Nanosensors: Metal Doped Carbon Nanotubes

TL;DR

This study employs computational screening to identify transition metal doped carbon nanotubes as effective chemical sensors, focusing on their binding energies and conductance changes for detecting gases like CO, NH3, and H2S.

Contribution

It introduces a systematic computational approach to screen and identify promising metal-doped carbon nanotubes for chemical gas sensing applications.

Findings

Ni-doped nanotubes are promising for CO detection under atmospheric conditions.

The study provides a method to estimate resistance changes based on gas concentration.

Identifies key dopants for specific target molecules.

Abstract

We use computational screening to systematically investigate the use of transition metal doped carbon nanotubes for chemical gas sensing. For a set of relevant target molecules (CO, NH3, H2S) and the main components of air (N2, O2, H2O), we calculate the binding energy and change in conductance upon adsorption on a metal atom occupying a vacancy of a (6,6) carbon nanotube. Based on these descriptors, we identify the most promising dopant candidates for detection of a given target molecule. From the fractional coverage of the metal sites in thermal equilibrium with air, we estimate the change in the nanotube resistance per doping site as a function of the target molecule concentration assuming charge transport in the diffusive regime. Our analysis points to Ni-doped nanotubes as candidates for CO sensors working under typical atmospheric conditions.