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Effect of nanostructuring of ZnO for gas sensing of nitrogen dioxide

journal contribution
posted on 2024-11-02, 03:33 authored by Shannyn Oberegger, Oliver JonesOliver Jones, Michelle SpencerMichelle Spencer
Nitrogen dioxide (NO2) is a toxic gas that contributes to photochemical pollution and can cause adverse effects to human and environmental health. Monitoring this gas is therefore important to warn of potential exposure. Nanostructures of zinc oxide (ZnO) have been studied for the sensing of NO2 gas, however, the effect of nanostructure morphology on the gas-sensor reaction mechanism is not understood. We examine the NO2 sensing mechanism for three different ZnO nanostructures, namely a nanowire, a facetted nanotube and a single walled (8,0) nanotube using density functional theory (DFT) calculations and ab initio molecular dynamics (AIMD) simulations. The effect of gas concentration and oxygen vacancies is also determined. It was shown that NO2 adsorbs on all nanostructures in multiple configurations, with the bonding being strongest on the nanowire and weakest on the single walled nanotube. NO2 behaves as a charge acceptor, consistent with adsorption on the thin film and desorbs intact from the nanostructures at simulation temperatures of 300 K and 700 K, providing new surface sites for detection of other molecules. The presence of surface oxygen vacancies was found to enhance the binding, with NO2 chemisorbing on all 3 nanostructures via two bonds to the surface. The AIMD simulations showed that at a simulation temperature of 500 K, NO2 dissociates on the nanowire and facetted nanotube defect surfaces resulting in a surface bound oxygen atom which fills the defect site, with the remaining NO desorbing from the surface. The single walled nanotube was shown to be unstable at these simulation temperatures. These findings provide important information for the experimental development of ZnO nanostructure-based gas sensors.

History

Related Materials

  1. 1.
    DOI - Is published in 10.1016/j.commatsci.2017.02.019
  2. 2.
    ISSN - Is published in 09270256

Journal

Computational Materials Science

Volume

132

Start page

104

End page

115

Total pages

12

Publisher

Elsevier

Place published

Netherlands

Language

English

Copyright

© 2017 Elsevier B.V. All rights reserved

Former Identifier

2006072487

Esploro creation date

2020-06-22

Fedora creation date

2017-04-06

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