Mechanistic, Thermodynamic, Kinetic, and Electronic Insights into the Formation of Benzothiazole-Based Thiourea and Urea Derivatives: A DFT Study
نویسندگان
1 Department of Chemistry, YI.C., Islamic Azad University, Tehran, I.R. IRAN
2 Department of Chemistry, YI.C., Islamic Azad University, Tehran, I.R. IRAN
3 Department of Chemistry, YI.C., Islamic Azad University, Tehran, I.R. IRAN
4 Research Center for New Technologies in Chemistry and Related Sciences, YI.C., Islamic Azad University, Tehran, I.R. IRAN
5 Research Center for New Technologies in Chemistry and Related Sciences, YI.C., Islamic Azad University, Tehran, I.R. IRAN
doi
10.30492/ijcce.2025.2070820.7332چکیده
Benzothiazole-based thiourea and urea derivatives are heterocycles of significant biological and pharmaceutical relevance. Understanding their formation pathways is essential for predicting reactivity and stability. In this work, Density Functional Theory (DFT) calculations were employed to investigate the thermodynamic and kinetic aspects of two competing condensations: 2-aminobenzothiazole with phenyl isothiocyanate (thiourea derivative) and with phenyl isocyanate (urea derivative). Geometrical, electronic, and energetic parameters were evaluated using the B3LYP functional with the 6-31G(d,p) and 6-311++G(d,p) basis sets in the gas phase, acetonitrile (ACN), and the ionic liquid ethyl pyridinium iodide ([EPy]I). The results indicate that Reaction II (urea formation) is both kinetically and thermodynamically favorable in all environments, whereas Reaction I becomes nearly thermoneutral or slightly endergonic with the larger basis set, especially in [EPy]I. IRC analysis further reveals a concerted addition–proton-transfer mechanism for both pathways, providing direct mechanistic insight. Solvent effects, particularly in acetonitrile, further enhance charge polarization and stabilize the corresponding transition states, strengthening the preference for the urea pathway. These findings provide mechanistic insights into benzothiazole reactivity and may guide future synthetic optimization.