journal article·2023-01-31

Theoretical study of the interaction of trimethylamine with aluminium nitride nanotube and gallium-doped aluminium nitride nanotube

Mohsen Doust Mohammadi, Hitler Louis, Hadi Afaridoon, Ernest C Agwamba

Bulletin of Materials Science· Volume 46· Issue 1
Publisher page

Abstract

A high concentration of trimethylamine (TMA) in the body can be converted to trimethylamine N-oxide (TMAO), which is a well-known proatherogenic substance capable of accelerating atherosclerosis disease in humans. Hence, there is a pressing need for the development of fast, accurate and reliable nanomaterials for the detection of TMA. In this study, the interaction of TMA with aluminium nitride nanotube (AlNNT) and gallium-doped aluminium nitride nanotube (AlNGaNT) was investigated using density functional theory at the M06-2X, B3LYP, ωB97XD at 6-311G(d)/6-31G(d) levels of theory. The results of the interaction energy between AlNGaNT and TMA for the 6-31G(d) increased remarkable in the order of M06-2X: −1.57 < B3LYP-D3:1.58 < ωB97XD: 1.89 kcal mol−1, while the 6-311G(d) showed higher interaction energy in the order of B3LYP-D3: −1.96 < M06-2X: −1.99 < ωB97XD: −2.03 kcal mol−1. Generally, the interaction of TMA with AlNNT and AlNGaNT increases the global reactivity parameter. From topological, a strong interaction was observed from −0.010 to −0.050 a.u. for signλ2(r)ρ(r) function and 0.000 to 0.00 to 0.400 for aRDG in TMA/AlNGaNT, where at signλ2(r)ρ(r) more scattered plot was observed between −0.050 to −0.020 a.u. While the small scattered plot was observed for TMA/AlNGaNT at 0.00 to −0.010 a.u. for signλ2(r)ρ(r) and aRDG of 0.00 to 0.400. Therefore, it was proposed that an electrostatic interaction is the mechanism between TMA and the AlNNC, and the strength of the interaction increase with the addition of Ga-atom as in AlNGaNT.
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Research context

OpenAlex topics: Nonlinear Optical Materials Research · Conducting polymers and applications · Pharmacological Effects and Assays

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

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