Investigation of the Corrosion of Heating Treatment Medium Carbon Steel in Sulfur Aqueous Solution
Bibliographic record
Abstract
To achieve the necessary mechanical properties and high corrosion resistance, heat treatment is based on a significant alteration in the microstructure of metals and alloys.The metal is heated above a critical temperature during heat treatment processes.Several methods, such as quenching and cooling in various media, followed by tempering and other heat treatment operations, can be used to achieve this.Steel can be made more malleable by annealing, which also increases ductility and improves corrosion resistance.In this study, thirty medium carbon steel specimens were used, which were split into different groups, various heat treatments were applied.First quenching, first tempering, second quenching, and second tempering were among the heat treatment procedures used, and cooling media made of distilled water were used at various temperatures.After that, the corrosion rate in these specimens was looked at, and the results were compared to the corrosion rate in the base specimen.The results show that repeated heat treatment of the metal generally lowers the rate of corrosion in the metal, particularly when distilled water is used for the cooling process after the two tempering stages at a temperature of almost absolute zero.The findings show that the second sample had the lowest corrosion rate of all the samples.When compared to the corrosion rate in the basic sample, the corrosion rate in this sample decreased by about 92.9%.
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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".