Abstract
About this talk
Literature has established that; Metallic corrosion remains one of the most persistent challenges confronting modern infrastructure, energy systems, transportation, chemical processing, oil and gas production, construction and marine technologies. To cube the challenges, chemical scientists have being struggling to synthesize a reliable method for suppressing a metal from its corrosive environment by which the trend passes through different activities ranging from protective coating, cathodic and anodic protection, and now to inhibitor. Recently, corrosion inhibitors remain among the most practical approaches for reducing metal dissolution and extending service life, particularly in acidic cleaning, pickling, oil-well acidification and other aggressive environments. However, the research direction has changed considerably over the last ten years. The field has moved beyond the conventional search for highly efficient molecules toward the rational discovery of inhibitors that simultaneously provide high protection, low toxicity, sustainability, durability, affordability and predictable performance under realistic operating conditions. This review examines major developments in metallic corrosion-inhibitor research from 2015 to 2026, with emphasis on organic inhibitors, heterocyclic compounds, ionic liquids, plant-derived and other bio-based inhibitors, polymers, nanomaterials, synergistic inhibitor systems, computational chemistry, molecular simulations, high-throughput experimentation and artificial intelligence/machine learning. Recent literature demonstrates increasing use of density functional theory (DFT), molecular dynamics (MD), Monte Carlo simulation and quantitative structure activity/property relationships to understand molecular adsorption and guide inhibitor design. Presently, machine learning is emerging as a potentially transformative tool for screening large chemical libraries and predicting inhibition performance before synthesis. The review argues that the next generation of corrosion inhibitors will not be defined simply by a high inhibition efficiency measured under laboratory conditions. Instead, successful inhibitors should be mechanistically understood, computationally predictable, environmentally responsible, experimentally reproducible and industrially deployable. The integration of green chemistry, interfacial science, electrochemistry, molecular modelling, high-throughput experimentation and AI/ML therefore represents a promising pathway toward accelerated discovery of smart and sustainable corrosion-control technologies.
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