Research Article
Microwave-assisted Synthesis, Characterization and Antibacterial Study of Schiff Base Metal (II) Complexes Derived from 2-Aminophenol and P-chlorobenzaldehyde
Issue:
Volume 14, Issue 4, December 2026
Pages:
109-115
Received:
12 September 2026
Accepted:
24 September 2026
Published:
29 October 2026
Abstract: The growing resistance of microorganisms to existing antibiotics has increased the need for new and more effective antimicrobial agents. In this study, Schiff base ligand was synthesized from 2-aminophenol and p-chlorobenzaldehyde, and its cobalt (II) and lead (II) complexes. The compounds were characterized using melting point determination, solubility tests, Ultraviolet–visible (UV-Vis), Fourier Transform Infrared spectroscopy (FTIR), Elemental analysis, and Magnetic susceptibility measurements. The FTIR spectrum of the ligand showed a characteristic band at 1610 cm-1 due to the formation of azomethine (C=N) group, confirming Schiff base formation. In the metal (II) complexes, this band shifted to lower frequencies, 1638 and 1612 cm-1 indicating coordination of the azomethine nitrogen to the metal ions. The O–H stretching band at 3429 cm-1 for ligand shifted to 3257 and 3047 cm-1 upon complexation, suggesting involvement of the phenolic oxygen atom. UV–visible and magnetic data indicated an octahedral geometry around Pb(II) and tetrahedral around Co(II). Elemental analysis revealed the expected molecular compositions of the prepared Schiff base ligand. Antibacterial studies using Staphylococcus aureus and Escherichia coli showed that the metal complexes displayed higher inhibitory activity against the tested bacterial strains compared with the free ligand. This enhancement suggests that coordination with metal ions increases the biological effectiveness of the Schiff base. Overall, the results indicated formation and characterization of the Schiff base ligand and its cobalt (II) and lead (II) complexes, demonstrating their potential as promising antimicrobial agents.
Abstract: The growing resistance of microorganisms to existing antibiotics has increased the need for new and more effective antimicrobial agents. In this study, Schiff base ligand was synthesized from 2-aminophenol and p-chlorobenzaldehyde, and its cobalt (II) and lead (II) complexes. The compounds were characterized using melting point determination, solub...
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Research Article
Theoreticaland Computational Studies of Corrosion Inhibition: Recent Developments
Issue:
Volume 14, Issue 4, December 2026
Pages:
116-119
Received:
8 June 2026
Accepted:
25 June 2026
Published:
3 November 2026
DOI:
10.11648/j.mc.20261404.12
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Abstract: Corrosion remains a major challenge in industrial systems, leading to significant economic losses, material degradation, and safety concerns. Corrosion inhibitors are widely employed to mitigate metal deterioration; however, the discovery and optimization of efficient, environmentally friendly inhibitors through conventional experimental methods are often costly and time-consuming. Consequently, theoretical and computational approaches have become indispensable tools for understanding corrosion inhibition mechanisms and accelerating inhibitor development. This review aims to provide a comprehensive overview of the theoretical and computational methods applied in corrosion inhibition studies between 2015 and 2025, with particular emphasis on Density Functional Theory (DFT), Molecular Dynamics (MD) simulations, Quantitative Structure-Activity Relationship (QSAR) modeling, and emerging Machine Learning (ML) techniques. Relevant literature was critically analyzed to evaluate recent advances in predicting inhibitor efficiency, adsorption behavior, and structure-activity relationships for metallic systems, particularly aluminium and steel.The review demonstrates that DFT remains a powerful approach for identifying key electronic descriptors, including HOMO energy, LUMO energy, and energy gap, which strongly influence inhibitor performance. Molecular dynamics simulations provide detailed insights into adsorption mechanisms and inhibitor orientation at metal surfaces, while QSAR models establish quantitative relationships between molecular descriptors and inhibition efficiency. Recent integration of machine learning algorithms with DFT and QSAR methodologies has significantly improved predictive accuracy, enabled rapid screening of large molecular libraries, and facilitated the design of eco-friendly corrosion inhibitors. Furthermore, hybrid computational workflows combining DFT, MD, QSAR, and ML have enhanced mechanistic understanding while reducing experimental costs and development time. Theoretical and computational techniques have transformed corrosion inhibitor research by providing reliable predictive frameworks for inhibitor design. Future progress will depend on the integration of advanced artificial intelligence tools, standardized datasets, and rigorous experimental validation to support sustainable and industrially applicable corrosion protection strategies.
Abstract: Corrosion remains a major challenge in industrial systems, leading to significant economic losses, material degradation, and safety concerns. Corrosion inhibitors are widely employed to mitigate metal deterioration; however, the discovery and optimization of efficient, environmentally friendly inhibitors through conventional experimental methods ar...
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Barizomdu Tina Pii
