Atomic-Scale Oxide Growth and Dissolution Kinetics on Ni-Cr Alloys
Bibliographic record
Abstract
Passivation oxide formation is the key for corrosion control of metal alloys. The kinetics of competing oxide formation and dissolution determines alloy corrosion behaviors in aqueous solution. Despite the important role of the multi-component oxide evolution, little has been known on the kinetics from the atomistic level. To this end, we have built a computational framework that enables simulations of competing kinetic processes in multi-component oxides from first principles. The effects of applied voltage, pH and temperature on oxide growth, dissolution and reprecipitation can all be captured in this model. Combining with our experimental measurements on alloy 22, we identified three voltage regimes with distinct oxide thicknesses and compositions. In the low voltage regime, oxide growth is the dominating process. The Cr/Ni ratio in the oxide is close to that in the alloy. In the medium voltage regime, Ni dissolves from the mixed oxide, NixCryO, and reprecipitates back as Ni oxide, NixO, to achieve phase separation. The resulted oxide film is significantly thickened with a duplex structure formed. In the high voltage regime, Ni dissolution dominates, and the Cr/Ni ratio increases. The temperature and pH dependencies have also been simulated. Higher temperature moves the dissolution-reprecipitation to the lower voltage; lower pH suppresses Ni reprecipitation and results in a thinner oxide film. These trends are also in agreement with the experiments. The oxide energetics of various stoichiometries are calculated by the density functional theory (DFT). Then the obtained data are used to train a surrogate lattice Hamiltonian with the cluster expansion (CE) method. Finally, kinetic Monte Carlo (KMC) simulations are run with the cation hopping barriers calculated on-the-fly based on the local environments from the combination of the above Hamiltonian and the linear Brønsted−Evans−Polanyi (BEP) relation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".