journal article·2017-01

Powerful greenhouse gas nitrous oxide adsorption onto intrinsic and Pd doped Single walled carbon nanotube

Mehdi Yoosefian

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Abstract

Abstract Density functional studies on the adsorption behavior of nitrous oxide (N2O) onto intrinsic carbon nanotube (CNT) and Pd-doped (5,5) single-walled carbon nanotube (Pd-CNT) have been reported. Introduction of Pd dopant facilitates in adsorption of N2O on the otherwise inert nanotube as observed from the adsorption energies and global reactivity descriptor values. Among three adsorption features of N2O onto CNT, the horizontal adsorption with Eads = −0.16 eV exhibits higher adsorption energy. On the other hand the Pd-CNT exhibit strong affinity toward gas molecule and would cause a huge increase in N2O adsorption energies. Chemical and electronic properties of CNT and Pd-CNT in the absence and presence of N2O were investigated. Adsorption of N2O gas molecule would affect the electronic conductance of Pd-CNT that can serve as a signal of gas sensors and the increased energy gaps demonstrate the formation of more stable systems. The atoms in molecules (AIM) theory and the natural bond orbital (NBO) calculations were performed to get more details about the nature and charge transfers in intermolecular interactions within adsorption process. As a final point, the density of states (DOSs) calculations was achieved to confirm previous results. According to our results, intrinsic CNT cannot act as a suitable adsorbent while Pd-CNT can be introduced as novel detectable complex for designing high sensitive, fast response and high efficient carbon nanotube based gas sensor to detect N2O gas as an air pollutant. Our results could provide helpful information for the design and fabrication of the N2O sensors.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Gas Sensing Nanomaterials and Sensors · Carbon Nanotubes in Composites · Graphene research and applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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