Adsorption of SO2 Molecule on Pristine, N, Ga-Doped and -Ga-N- co-Doped Graphene: A DFT Study

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Computation Pub Date : 2023-11-22 DOI:10.3390/computation11120235
Dinara Akhmetsadyk, Arkady Ilyin, Nazim Guseinov, Gary Beall
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Abstract

SO2 (sulfur dioxide) is a toxic substance emitted into the environment due to burning sulfur-containing fossil fuels in cars, factories, power plants, and homes. This issue is of grave concern because of its negative effects on the environment and human health. Therefore, the search for a material capable of interacting to detect SO2 and the research on developing effective materials for gas detection holds significant importance in the realm of environmental and health applications. It is well known that one of the effective methods for predicting the structure and electronic properties of systems capable of interacting with a molecule is a method based on quantum mechanical approaches. In this work, the DFT (Density Functional Theory) program DMol3 in Materials Studio was used to study the interactions between the SO2 molecule and four systems. The adsorption energy, bond lengths, bond angle, charge transfer, and density of states of SO2 molecule on pristine graphene, N-doped graphene, Ga-doped graphene, and -Ga-N- co-doped graphene were investigated using DFT calculations. The obtained data indicate that the bonding between the SO2 molecule and pristine graphene is relatively weak, with a binding energy of −0.32 eV and a bond length of 3.06 Å, indicating physical adsorption. Next, the adsorption of the molecule on an N-doped graphene system was considered. The adsorption of SO2 molecules on N-doped graphene is negligible; generally, the interaction of SO2 molecules with this system does not significantly change the electronic properties. However, the adsorption energy of the gas molecule on Ga-doped graphene relative to pristine graphene increased significantly. The evidence of chemisorption is increased adsorption energy and decreased adsorption distance between SO2 and Ga-doped graphene. In addition, our results show that introducing -Ga-N- co-dopants of an “ortho” configuration into pristine graphene significantly affects the adsorption between the gas molecule and graphene. Thus, this approach is significantly practical in the adsorption of SO2 molecules.
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二氧化硫分子在原始石墨烯、氮石墨烯、掺镓石墨烯和掺镓氮共掺石墨烯上的吸附:DFT 研究
SO2(二氧化硫)是一种有毒物质,由于汽车、工厂、发电厂和家庭燃烧含硫化石燃料而排放到环境中。由于其对环境和人类健康的负面影响,这一问题备受关注。因此,在环境和健康应用领域,寻找一种能够相互作用检测二氧化硫的材料以及研究开发有效的气体检测材料具有重要意义。众所周知,预测能与分子相互作用的系统的结构和电子特性的有效方法之一是基于量子力学方法的方法。在这项研究中,我们使用了 Materials Studio 中的 DFT(密度泛函理论)程序 DMol3 来研究二氧化硫分子与四个系统之间的相互作用。利用 DFT 计算研究了 SO2 分子在原始石墨烯、N-掺杂石墨烯、Ga-掺杂石墨烯和 -Ga-N- 共掺杂石墨烯上的吸附能、键长、键角、电荷转移和状态密度。得到的数据表明,二氧化硫分子与原始石墨烯之间的结合相对较弱,结合能为-0.32 eV,键长为 3.06 Å,表明存在物理吸附。接下来,我们考虑了分子在掺杂 N 的石墨烯体系上的吸附情况。二氧化硫分子在 N 掺杂石墨烯上的吸附可以忽略不计;一般来说,二氧化硫分子与该体系的相互作用不会显著改变电子特性。然而,相对于原始石墨烯,气体分子在掺杂 Ga 的石墨烯上的吸附能显著增加。二氧化硫与掺镓石墨烯之间吸附能的增加和吸附距离的减小就是化学吸附的证据。此外,我们的研究结果表明,在原始石墨烯中引入 "正交 "构型的 -Ga-N- 共掺杂剂会明显影响气体分子与石墨烯之间的吸附。因此,这种方法在二氧化硫分子的吸附方面非常实用。
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来源期刊
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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