Experimental and computational chemistry studies on the inhibition of aluminium and mild steel in 0.1 M HCl by 3-nitrobenzoic acid
Bibliographic record
Abstract
The effectiveness of 3-nitrobenzoic acid towards the inhibition of the corrosion of mild steel and aluminium in solution of HCl was investigated using theoretical and experimental methods (weight loss, thermometric, polarization, FTIR and SEM techniques). Inhibition efficiency of 3-nitrobenzoic acid, evaluated from weight loss technique ranged from 71% to 90% and from 71% to 82% for mild steel and aluminium, respectively. Results from linear polarization and potentiodynamic studies were comparable to weight loss results. Calculated kinetic (activation energy), thermodynamic (changes in entropy and enthalpy) and adsorption parameters indicated that the adsorption of the inhibitor on the surface of the respective metal is accompanied by molecular association and is endothermic, spontaneous and favoured the mechanism of physical adsorption. Best-fitted adsorption isotherms were Langmuir and Frumkin models, which gave evidences for the existence of interaction, characterized by attractive behaviour of the inhibitor on both mild steel and aluminium surfaces. Scanning electron micrographs of the metal before and after inhibition clearly revealed that the inhibitor prevented crevice and pitting corrosion by forming adsorbed protective layer on the respective metal surface. FTIR spectra of the inhibitor and the corrosion products indicated the formation of new bond, existence of interaction between the inhibitor molecules and the involvement of some functional groups in the adsorption and inhibition processes. Quantum chemical study revealed that the inhibitor is adsorbed on the metal surface through the nitro functional group in the ring. Calculated semi empirical parameters were comparable to those reported for excellent 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".