Synergistic Corrosion Inhibition of Mild Steel in Acidic Media by a Benzimidazole–Thiophene Ligand and Its Metal Complexes: A Multi-Technique Electrochemical Approach
Bibliographic record
Abstract
This study investigates the corrosion inhibition efficiency of [2-(thiophen-2-yl)-1-(thiophen-2-ylmethyl)-1H-benzo[d]imidazole] and its Zn and Cu complexes for mild steel in 1.0 M HCl. The ligand was selected for its non-toxic profile and high electron density, favoring strong adsorption onto the metal surface. Electrochemical methods, including EIS, PDP, LPR, and CASP, were employed to evaluate the inhibitors’ performance. The results showed a significant decrease in corrosion current density and increased polarization resistance, with the Zn complex achieving the highest inhibition efficiency (93.8%). EIS fitting confirmed the formation of a protective film with high charge transfer and film resistance. Surface analyses by SEM and EDS revealed smoother steel morphology and inhibitor adsorption. XPS confirmed the presence of Fe3+, Zn2+and Cu2+ oxides, as well as all active inhibitor elements on the surface, supporting a mixed inhibition mechanism. The enhanced performance of the metal complexes is attributed to synergistic effects between the metal ions and the heterocyclic ligand, offering a promising strategy for the design of effective and environmentally friendly corrosion inhibitors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".