Combination of dopamine and tyrosinase as a green corrosion inhibitor for carbon steel
Bibliographic record
Abstract
This study investigates the combination of dopamine (DA) and tyrosinase (TYR) for corrosion protection of carbon steel in acidic conditions, focusing on corrosion protection behavior and film formation mechanisms. Confocal Raman microscopy analysis demonstrated that DA forms a thin protective film on carbon steel through complexation with Fe ions, preferably at defect sites, thereby transforming mixed Fe oxides to Fe(catechol) 3 , while TYR promotes DA oxidation and enhances the complexation. Surface coverage of Fe(catechol) 3 increases from 37 % at 10 min to 89 % at 60 min of exposure in the DA/TYR solution. Inductively coupled plasma-optical emission spectroscopy (ICP-OES) measurements showed a 25 % reduction in Fe release after 48 h in the DA/TYR solution. X-ray photoelectron spectroscopy (XPS) analysis revealed that TYR promotes oxidation from Fe 2+ to Fe 3+ at the surface, resulting in a thinner yet more protective DA-Fe complexation film. The DA/TYR system increased corrosion resistance by 47 % after 24 h, primarily attributed to the rapid and extensive formation of Fe(catechol) 3 complexes between DA and Fe ions released from the substrate, further strengthened by TYR. This bio-inspired and green corrosion inhibitor strategy, combining DA’s metal-binding affinity with TYR’s enzymatic oxidation capability, provides a scalable and non-toxic strategy for effective corrosion protection. • Dopamine (DA) and Tyrosinase (TYR) form a DA-Fe complexation film providing corrosion protection of carbon steel. • TYR catalyzes Fe 2+ to Fe 3+ oxidation and makes the film thinner and more protective. • Surface coverage of Fe(catechol) 3 increases from 37 % (10 min) to 89 % (60 min) in the DA/TYR solution. • DA/TYR-formed film reduces Fe dissolution by 25 % after 48 h in the DA/TYR solution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".