Comparative study of corrosion inhibition by three anionic surfactants in an acidic environment
Bibliographic record
Abstract
Abstract Corrosion in carbon steel units of chemical, petrochemical and oil and gas plants poses safety and economic concerns. The goal of our study is to investigate the corrosion inhibition effectiveness of an environmentally benign surfactant, namely sodium lauroyl lactylate (SLL), in comparison to sodium cocoyl glutamate (SCG) and sodium dodecyl sulfate (SDS). The corrosion of carbon steel in 1 M HCl was markedly inhibited by 0.05 and 0.1 M of the anionic surfactant SLL, as determined from weight loss over 96 h, at ambient conditions. X‐ray photoelectron spectroscopy (XPS) showed that SLL adsorbed at the carbon steel surface, forming a protective film that decreased corrosion. Scanning electron microscopy (SEM) showed that carbon steel surfaces immersed in 1 M HCl for 96 h had an etched appearance without SLL, whereas they retained their smoothness with 0.1 M SLL. Electrochemical impedance spectroscopy (EIS) measurements confirmed that SLL passivated carbon steel surfaces, markedly increasing the polarization resistance Rp from ≈95 to ≈20,694 Ω cm2 over a 12 h period. In contrast, without SLL, Rp decreased from ≈92 to ≈12 Ω cm2. These results demonstrate for the first time that the environmentally friendly surfactant SLL is an efficient corrosion inhibitor in extreme environments such as 1 M HCl solutions. Dissimilar to SLL, SCG and SDS were not effective in inhibiting corrosion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".