Amphiphilic Janus Graphene Oxide Acts as a Corrosion Inhibitor to Mitigate the Corrosion Caused by a 1 M HCl Solution on Mild Steel
Bibliographic record
Abstract
Great aqueous dispersibility, a large specific surface area, and high impermeability make graphene oxide (GO) the ideal candidate for a high-performance corrosion inhibitor. Numerous symmetrical modification methods have been reported to enhance the adsorption of GO on metal surfaces in various corrosive media. This work aims to investigate the enhancement and mechanism of unilateral hydrophobic modification on the corrosion inhibition performance of GO. In this study, amphiphilic Janus GO (JGO) was prepared by grafting hydrophobic alkyl chains on one side of GO, and its anticorrosion performance was evaluated via weight loss experiments and electrochemical tests. The results revealed that the corrosion inhibition efficiency for Q235 mild steel (MS) in a 1 M HCl aqueous solution of 25 ppm JGO (81.08%) was much higher than that of GO at the same concentration (22.12%). Furthermore, the Langmuir adsorption isotherm and computational study demonstrated that the synergistic effect of physical adsorption and chemical adsorption promoted the hydrophilic side of JGO close to the surface of the metal, and the dense protective layer was formed by the hydrophobic chains toward the corrosive medium, which effectively hindered the corrosion of MS by the acidic liquid. This study emphasizes the significant role of asymmetrically modified hydrophobic alkyl chains in improving the corrosion prevention performance of GO and provides a perspective for the structural design of GO-based 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) | 0.000 | 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".