Kinetics of intermetallic compound layers during initial period of reaction between mild steel and molten aluminum
Bibliographic record
Abstract
Abstract Hot dip aluminizing of mild steel at different temperatures was conducted to reveal the influence of reaction temperature and time on interfacial intermetallic compounds (IMCs). Scanning electron microscopy, energy dispersive X-ray spectrometry and X-ray diffraction were employed to investigate the interfacial microstructures. The IMCs of the dipping interface consisted of a thick layer of η-Fe2Al5 between 4.2–132.2 μm next to the steel and a thin layer of θ-Fe4Al13 between 0–5.5 μm close to the aluminum. With increasing dipping temperature and time, the total thickness of IMCs (Fe2Al5 plus Fe4Al13) increased. Specifically, the growth of the Fe2Al5 layer can be described by parabolic rate laws. An activation energy of 93 kJ mol−1 was obtained, combining both the results from the present work and previous studies in the temperature range of 675–900°C. The change in Fe4Al13 thickness is not significant compared with the Fe2Al5. However, the decrease in IMC thickness of the Fe4Al13 with dipping temperature was observed for the first time and had never been reported before. Moreover, it can be clearly observed that the thickness of the Fe4Al13 decreased with dipping time based on the linear fitting results by excluding the result of the initial 1 s. A possible mechanism is that interfacial dynamics and thermodynamics work for the dissolution and decomposition of the Fe4Al13 layer. Higher temperature accelerates the dissolution of the θ layer.
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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) | 0.000 | 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) | 0.002 | 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".