Department of Chemistry, Faculty of Science, Al Azhar University-Gaza, P.O. Box 1277, Gaza, Palestine.
Chemistry Department, Faculty of Science, King Abdulaziz University, P.O. Box 42805, Jeddah, 21589, Saudi Arabia.
Sci Rep. 2022 Oct 22;12(1):17798. doi: 10.1038/s41598-022-22900-y.
Geometries of the 3-methyl-1-phenyl-4-(phenyldiazenyl)-1H-pyrazol-5-amine azo-dye compound and its tautomer were optimized using B3LYP and M06-2X functionals in coupling with TZVP and 6-311 + G(d,p) basis sets. The H- and C-NMR chemical shifts of all species were predicted using 13 density functional theory (DFT) approaches in coupling with TZVP and 6-311 + G(d,p) basis sets at the different optimized geometries by applying the using GIAO method using the eight geometries. The selected functionals are characterized by having different amount of Hartree-Fock exchange. The selected DFT methods were B3LYP, M06-2X, BP86, B97XD, TPSSTPSS, PBE1PBE, CAM-B3LYP, wB97XD, LSDA, HSEH1PBE, PW91PW91, LC-WPBE, and B3PW91. The results obtained were compared with the available experimental data using different statistical descriptors such as root mean square error (RMSE) and maximum absolute error (MAE). Results revealed that the prediction of the H-NMR chemical shifts has more significant dependence on the applied geometry than that of the prediction of the C-NMR chemical shifts. Among all the examined functionals, B97D and TPSSTPSS functionals were found to be the most accurate ones, while the M06-2X functional is the least accurate one. Results also revealed that the prediction of NMR chemical shifts using TZVP basis sets results is more accurate results than 6-311 + G(2d,p) basis set.
3-甲基-1-苯基-4-(苯偶氮基)-1H-吡唑-5-胺偶氮染料化合物及其互变异构体的几何形状采用 B3LYP 和 M06-2X 泛函与 TZVP 和 6-311 + G(d,p)基组进行了优化。使用 GIAO 方法,通过应用八种几何形状,在不同优化的几何形状下,使用 TZVP 和 6-311 + G(d,p)基组,通过 13 种密度泛函理论 (DFT) 方法预测了所有物种的 H 和 C-NMR 化学位移。所选功能具有不同数量的 Hartree-Fock 交换。选择的 DFT 方法有 B3LYP、M06-2X、BP86、B97XD、TPSSTPSS、PBE1PBE、CAM-B3LYP、wB97XD、LSDA、HSEH1PBE、PW91PW91、LC-WPBE 和 B3PW91。使用不同的统计描述符(例如均方根误差 (RMSE) 和最大绝对误差 (MAE))将获得的结果与可用的实验数据进行了比较。结果表明,H-NMR 化学位移的预测比 C-NMR 化学位移的预测更依赖于应用的几何形状。在所检查的功能中,B97D 和 TPSSTPSS 功能被发现是最准确的,而 M06-2X 功能是最不准确的。结果还表明,使用 TZVP 基组的 NMR 化学位移预测结果比 6-311 + G(2d,p)基组更准确。