A Case Study: Investigation of Untreated and Treated 304 Stainless Steel on Corrosion Behaviour
Bibliographic record
Abstract
A titanium coating on an iron-based metal surface significantly enhanced its resistance to localised corrosion. The research thoroughly investigated the microstructure and corrosion behaviour of both the untreated and treated 304 stainless steel substrates. The coating’s morphology was meticulously examined using scanning electron microscopy (SEM), while its chemical composition was determined via energy-dispersive X-ray spectroscopy (EDX). Electrochemical impedance spectroscopy (EIS) was employed in an open circuit potential experiment to evaluate the coating’s resistance to localised corrosion in an alkaline solution. SEM was again utilised to assess the coating’s morphologies and cross-sectional view. The result revealed that untreated samples showed small and large pits on the microstructure, while no pit was detected in treated samples. Only fine dimples and voids were observed for the treated sample. The treated sample exhibited superior corrosion resistance to the untreated sample with a corrosion rate of 0.002348 mm/year and 0.007109 mm/year, respectively. This is attributed to the presence of the coating for a treated sample with curing for 10 minutes. The corrosion rate value is still considered excellent and accepted for stainless steel because the corrosion rate penetration is below 1 mils per year (mpy).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".