Research Article
Designing Novel Quinoline Derivative as Corrosion Inhibitorfor Aluminium in Hydrochloric Acid Solutions
Issue:
Volume 10, Issue 1, June 2026
Pages:
1-14
Received:
19 December 2025
Accepted:
7 January 2026
Published:
23 January 2026
Abstract: In this research, novel quinoline derivative as aluminium corrosion inhibitor was designed by utilizing twenty three (23) molecules of quinoline derivatives tested each as corrosion inhibitors for the aluminium in HCl solution; experimentally through weight loss method, and theoretical investigations using quantitative structure activity relationship (QSAR). The inhibition efficiencies of the quinoline derrivatives obtained from the weight lossshows that some quinoline derivatives such as 5-MeQ, 5-ClQ, 8-TMeQ, 6-ACQ and 7-OHQ inhibits the corrosion better than others as indicated by percentage inhibition efficiency (IE). Quantum chemical calculation indicated that the most popular parameters which play a prominent role are the eigenvalues of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), the HOMO-LUMO gap (ΔE), chemical hardness and softness and the number of electrons transferred from inhibitor molecule to the metal surface. Base on the several physicochemical descriptors and investigation of the adsorption of these molecules on the aluminium surface by the QSAR study of twenty three quiniline derivatives with the aid of material studio, a model was developed and validated. On the basis of the physicochemical parameters, predicted inhibition efficiency of 97.7% obtained using experimental inhibition efficiencies at 303K in 0.4MHCl and 0.2g/mol inhibitor concentration, and the correlation matrix from the QSAR study; 5-chloro,7-hydroxy-quinoline (5-Cl,7-OH-C9H5N) was designed and accepted as new efficient and effective quinoline derivative inhibitor for aluminium corrosion in HCl acid solution.
Abstract: In this research, novel quinoline derivative as aluminium corrosion inhibitor was designed by utilizing twenty three (23) molecules of quinoline derivatives tested each as corrosion inhibitors for the aluminium in HCl solution; experimentally through weight loss method, and theoretical investigations using quantitative structure activity relationsh...
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Research Article
Integrating Quantum Chemistry and Machine Learning for Accurate Modelling of Aromaticity, Hydrogen Bonding, and Metal Co-Factors
Ravuri Hema Krishna*
Issue:
Volume 10, Issue 1, June 2026
Pages:
15-23
Received:
23 January 2026
Accepted:
3 February 2026
Published:
21 February 2026
DOI:
10.11648/j.ajqcms.20261001.12
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Abstract: Aromaticity, hydrogen bonding, and metal cofactors are fundamental interactions governing the structure, stability, and function of biomolecular and catalytic systems. Their accurate computational representation remains a major challenge due to the combined influence of electron delocalization, polarization effects, and complex quantum mechanical behavior, particularly in transition-metal environments. Classical molecular mechanics force fields, while computationally efficient, fail to capture these phenomena reliably, motivating the development of quantum mechanical (QM), hybrid QM/MM, and machine-learning (ML) enhanced approaches. This article systematically reviews recent advances in the modelling of aromatic stabilization, hydrogen-bonding dynamics, and metal–ligand coordination using density functional theory (DFT), multi-scale QM/MM simulations, and modern ML potentials. Benchmark systems including aromatic hydrocarbons, hydrogen-bonded clusters, peptide fragments, and biologically relevant metal complexes were analyzed using dispersion-corrected DFT functionals and ML-based force fields trained on high-level QM datasets. Validation metrics such as interaction energies, geometric parameters, aromaticity indices, hydrogen-bond lifetimes, and metal-coordination stability were employed to assess predictive performance. The results demonstrate that modern DFT methods accurately reproduce electronic delocalization and interaction energetics, while QM/MM techniques effectively capture environmental effects in large biomolecular systems. Machine-learning potentials achieve near-QM accuracy at substantially reduced computational cost, showing strong performance for aromatic systems and hydrogen-bond networks, though challenges remain for redox-active metal centers and multi-reference electronic states. Overall, the study highlights that no single modelling strategy is universally optimal. Instead, integrated hybrid frameworks combining QM accuracy, ML efficiency, and classical scalability offer the most promising pathway toward predictive and interpretable simulations. Future progress will depend on metal-inclusive training datasets, physics-informed ML architectures, and improved treatment of polarization and electronic correlation to enable robust modeling across complex chemical space.
Abstract: Aromaticity, hydrogen bonding, and metal cofactors are fundamental interactions governing the structure, stability, and function of biomolecular and catalytic systems. Their accurate computational representation remains a major challenge due to the combined influence of electron delocalization, polarization effects, and complex quantum mechanical b...
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