Time-varying concentration-dependent interdiffusion coefficient in the Cu-Ni system
Bibliographic record
Abstract
Solid-state diffusion has attracted substantial research attention due to its key role in metallic materials processing and performance analysis. The isothermal interdiffusion coefficient is one of the crucial parameters for characterizing this process. Although the interdiffusion coefficient is commonly acknowledged as a function of temperature and concentration (𝐷=𝐹(𝐶,𝑇)), its time dependence cannot be neglected due to the possible effect of diffusion-induced stress (DIS). The aim in the present work is to experimentally verify the occurrence of time effect on the interdiffusion coefficient in a copper-nickel (Cu-Ni) system and then study its most suggestive underlying factor. To avoid the non-trivial errors that arise from the assumption that the initial concentration profile is a “step-function” in space, a new method that utilizes two concentration profiles is used in this work to calculate the concentration-dependent interdiffusion coefficients. The results verify that, in contrast to what is commonly recognized, the interdiffusion coefficient is not only a function of temperature and concentration but can also significantly vary with diffusion time due to the presence of DIS. This can considerably affect the accuracy of theoretical analysis and predictions of the diffusion process in practical applications such as welding, coating, and heat treatments.
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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".