Green and nano-based strategies for controlling mild steel corrosion in acidic media: a systematic review
Source abstract
Abstract
Corrosion of mild steel in acidic media remains a major constraint in industrial processes, necessitating effective and sustainable protection strategies. This systematic review synthesizes evidence from 45 rigorously selected studies (2018–2025), identified through a PRISMA-guided screening of 676 records, to evaluate recent developments in green and nano-enabled corrosion inhibitors for acidic environments. A complementary bibliometric analysis using VOSviewer reveals four dominant research clusters focused on plant-based inhibitors, nanomaterials, adsorption mechanisms, and electrochemical evaluation techniques, highlighting an increasingly interconnected research landscape. Green inhibitors offer environmentally benign and low-toxicity corrosion mitigation but often suffer from thermal instability, compositional variability, and inconsistent performance. Nano-based inhibitors exhibit superior durability, high surface reactivity, and strong interfacial interactions, yet raise concerns regarding synthesis cost, potential toxicity, and long-term environmental impact. Hybrid green–nano systems show synergistic behaviour, enhanced film formation, and consistently high inhibition efficiencies, positioning them as strong candidates for advanced protection solutions. Despite these advances, important gaps persist, including limited industrial validation, inadequate environmental risk assessment, and the absence of standardized testing protocols. This review consolidates current knowledge and outlines key research priorities to guide the design of next-generation, sustainable corrosion inhibitors capable of delivering reliable performance under harsh acidic conditions.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Corrosion Behavior and Inhibition · Concrete Corrosion and Durability · Electrodeposition and Electroless Coatings
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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