HVOF Sprayed Mullite Coatings for Use In Extreme Environments
Bibliographic record
Abstract
Products used in industry and marine applications are exposed to extreme environments like high heat, humidity, acidic or alkaline or hyper saline environment, UV and IR radiation. Metals with good corrosion and oxidation resistance may be used but are restricted to Ni and Cr alloys, titanium and super alloys etc., which are costly and have their limitations. Hence ceramic coatings on low cost metals may be an answer to this problem. Ceramics are inherently chemically inert, high temperature resistant, corrosion and oxidation resistant. Flame spraying of ceramics is a good and reliable method for applying ceramic coatings on metallic substrates with good bond strength (> 80 MPa) and 1% porosity. In this work, HVOF technique is applied to obtain 100 microns thick mullite coatings on MS substrates with a NiCr bond coat. Mullite has a high oxidation and corrosion resistance. It is chemically inert. It has high temperature resistance even at 2000 C. These properties are ideal for industrial components exposed to salty environments. Characterization studies like XRD, SEM/EDS, Corrosion tests using polarization technique, coating thickness and surface roughness have been studied and reported. A corrosion rate of 1.55 mm/ year has been achieved in a sea water environment.
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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) | 0.001 | 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".