Performance of HVOF-Sprayed Carbide Coatings in Aqueous Corrosive Environments
Bibliographic record
Abstract
Abstract The HVOF technology is well known to provide a wide variety of coating materials having excellent performance characteristics under different aggressive conditions such as wear, erosion by impact of particle and corrosion. Carbides, as a family, constitute a big segment of materials used by the thermal spray industry. Although their material properties may be well known since they are often used in wear or corrosive-wear industrial applications, aqueous corrosion of such coatings are not well characterized. Moreover, thermal spray process technology being in constant evolution, past literature on these coatings may not be directly applicable as newer produced coatings have higher adhesive and cohesive strength. Recent technology allows a better control on density and oxides content that are important parameters to consider for corrosion applications. The success of a coating is related to judicious material selection for various applications. However, the choice of the starting materials for producing a coating is often difficult since there is a lack of data on the corrosion performance of thermal spray coatings. The present paper addresses the performance of various carbide HVOF coatings in terms of corrosion rate and degradation mode in two corrosive environments — HCl and HNO3. Behavior of the coatings is compared using bulk SS316 and SS316 HVOF coating as a benchmark.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".