Experimental verification of a common assumption in the use of concentration-dependent interdiffusion coefficient
Bibliographic record
Abstract
• Interdiffusion coefficient, D(C), is used to predict and analyze diffusion. • A general view that D(C) at any temperature and time can predict diffusion. • D(C) at long time fails to predict concentration profiles at shorter times. • Diffusion-induced stress varies and causes the D(C) to isothermally change. There has generally been a view that concentration-dependent interdiffusion coefficient, D(C), obtained at any diffusion temperature and time is a material constant and can be reliably used to predict diffusion effects at all other isothermal diffusion times. This notion which is implicitly predicated on the assumption that diffusion-induced stress (DIS) rapidly and completely relaxes once formed and does not influence D(C) is experimentally verified in the present work. The results of the study show that reliably computed D(C) from concentration profile obtained at long diffusion time fails to correctly predict concentration profiles at shorter diffusion periods, due to isothermal variation of the D(C) with time. Accordingly, instead of continuing to assume that D(C) is time-independent and can be reliably used to predict diffusion effects at any isothermal diffusion time, without appropriate experimental support, proper experimental verification of this crucial concept in other alloy systems, as done in the present work, is imperative.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".