Preparation and performance study of hollow glass microsphere-based polymeric silazane high-temperature resistant anticorrosion coatings
Bibliographic record
Abstract
Thermal insulation and anticorrosion coatings can effectively improve the energy efficiency of equipment and pipelines, reducing energy consumption, and have good application prospects in marine and petrochemical pipelines. Therefore, this study prepared a robust composite coating through chemical cross-linking and analyzed its anticorrosion and thermal insulation mechanisms. Infrared analysis showed that the –OH group in epoxy resins could react with the Si–H group in polysilazane, forming a stable cross-linked structure, thereby imparting good mechanical stability to the coating. SEM and AFM images revealed that after 500 abrasion cycles, the coating surface showed no obvious scratches or peeling. After five sand drop and water impact test cycles, the contact angle and rolling angle of the coating only decreased slightly. The adhesion level of the coating was rated as level 1, and the pencil hardness was 6H. When hollow glass microspheres were added to the coating, they not only isolated heat but also enhanced the corrosion resistance of the coating. Thermogravimetric analysis-differential thermogravimetric analysis curves indicated that the mass loss of the coating before 800 °C was only 12.35%. In thermal insulation experiments, the temperature of the composite coating was reduced by 38.6 °C compared to the internal temperature. After being immersed in 3.5 wt. % NaCl solution for 168 h, the low-frequency resistance of the coating remained greater than 102 Ω cm2. We believe that this green, environmentally friendly, and cost-effective thermal insulation and anticorrosion coating has broad application prospects in various industrial fields.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".