Spontaneously self‐healing silicone polyurea coating for metal corrosion protection
Bibliographic record
Abstract
Abstract Organic anticorrosion coatings (OACs) are the most effective, economical, and environmentally friendly protection strategy for metals. However, the traditional OACs usually inevitably suffer mechanical cracks during their service life. If not repaired in time, the resultant cracks in OACs will result in irreparable corrosion of the metal substrate. More detrimentally, these microcracks are not easily detectable at the initial stage. If OACs can autonomously heal their damage at the initial stage, the microcracks will be repaired in time and the metal substrate will be protected well. Thus, giving OACs the self‐healing capability is a promising strategy for metal protection. Herein, an autonomously self‐healing TiO2‐reinforced silicone polyurea (SPU‐g‐TiO2) elastomer was successfully designed as anticorrosion coating for Q235 carbon steel (QCS) protection. The SPU‐g‐TiO2 elastomer matrix exhibited excellent stretchability with a maximum strain of 3826.72% and fast self‐healing ability with an outstanding self‐healing efficiency of 98.69% for 6 h without any external stimulus at room temperature. The QCS coated with SPU‐g‐TiO2 showed excellent damage resistance and good anticorrosive ability, and it can self‐heal the microcracks generated in its coating in time to prevent corrosive media corroding the internal mental substrate. This work has a positive guiding significance for the development of functional OACs to improve their protection for metallic materials. Highlights A bionic smart self‐healing SPU‐g‐TiO2 coating was successfully synthesized. The mechanical and self‐healing properties are balanced using nanoparticles. The coating features excellent self‐healing capability and stretchability. Q235 steel coated with SPU‐g‐TiO2 shows good anticorrosive ability.
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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".