Corrosion Resistance of Boronized, Aluminized, and Chromized Thermal Diffusion-Coated Steels in Simulated High-Temperature Recovery Boiler Conditions
Bibliographic record
Abstract
In this study, the high-temperature molten salt corrosion resistance of bare steels and steels with protective coatings, fabricated by thermal diffusion processes (boronizing, aluminizing and chromizing), were investigated and compared. Surface engineering through thermal diffusion can be used to fabricate protective coatings against corrosion, while alleviating issues around possible cracking and spallation that is typical for conventional thermal-sprayed coatings. In this regard, samples of low carbon steel and 316 stainless steel substrates were boronized, chromized, and aluminized through a proprietary thermal diffusion process, while some of the samples were further coated with additional thin oxide and non-oxide layers to create new surface architectures. In order to simulate the actual corrosion conditions in recovery boilers (e.g., from black liquor combustion), the surfaces of the samples sprayed with a modeling salt solution, were exposed to low-temperature (220 ∘C) and high-temperature (600 ∘C) environments. According to microstructural and X-ray diffraction (XRD) studies and results of hardness determination, the coatings with multilayered architectures, with and without additional oxide layers, showed successful resistance to corrosive attack over bare steels. In particular, the samples with boronized and chromized coatings successfully withstood low-temperature corrosive attack, and the samples with aluminized coatings successfully resisted both low- and high-temperature molten salt corrosive attacks. The results of this study conducted for the first time for the thermal diffusion coatings suggest that these coatings with the obtained architectures may be suitable for surface engineering of large-sized steel components and tubing required for recovery boilers and other production units for pulp and paper processing and power generation.
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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".