Published November 2019 | Version v1
Journal article

Adsorption characteristics of formaldehyde on nitrogen doped graphene-based single atom adsorbents: A DFT study

  • 1. School of Energy and Power Engineering, North China Electric Power University, Baoding, 071003 (China)

Description

Formaldehyde (CH2O) is the most common volatile organic pollutant in the indoor environment, which can directly cause respiratory system diseases of humans. As a new type of material, graphene-based single atom adsorbent has the advantages of large specific surface and high adsorption activity, which can provide a new direction for formaldehyde removal. In this paper, the adsorption characteristics of formaldehyde on graphene-based single atom adsorbents were investigated through density functional theory (DFT) calculations. According to the analysis of geometric structure, electron transfer and density of states (DOS), the adsorption of formaldehyde belongs to a stable chemisorption and the largest adsorption energy of formaldehyde is −2.27 eV, which is a combined action of electron transfer and hybridization effect. Formaldehyde will be preferentially adsorbed on single atom adsorbent in side-on model. The effect of N doping and desorption temperature were studied through searching for linear relationship and Gibbs free energy calculation, respectively. Doping N atoms into graphene plane can significantly improve the adsorption strength of formaldehyde through modulating the charge of metal atoms. The charge of metal atoms can be an efficient descriptor for adsorption energy of formaldehyde. CH2O plays a dominant role in competitive adsorption between O2, N2 and CH2O. We expect that this theoretical study can lay a foundation for the experimental investigations.

Additional details

Identifiers

DOI
10.1016/j.apsusc.2019.07.114;
PII
S0169433219321506;

Publishing Information

Journal Title
Applied Surface Science
Journal Volume
493
Journal Page Range
p. 1260-1267
ISSN
0169-4332
CODEN
ASUSEE

Optional Information

Copyright
Copyright (c) 2019 Elsevier B.V. All rights reserved.