Aluminium and iron impurity segregation in yttria-stabilized zirconia grain boundaries
Bibliographic record
Abstract
Yttria-stabilized zirconia (YSZ) is highly valued for its high ionic conductivity and thermal stability, making it indispensable in high-temperature applications like solid oxide fuel cells. However, performance in YSZ is intricately related to the behavior of impurities at grain boundaries, especially with respect to their effect on ionic transport. The present work systematically investigates segregation behaviors of aluminum (Al) and iron (Fe) impurities in YSZ grain boundaries by using molecular dynamics simulations. We have investigated the dynamics of Al and Fe impurities across two grain boundary configurations, symmetric and mixed boundaries, with respect to their relative impacts on oxygen ionic conductivity. Our findings indicate that Al impurities, because of its relatively low solubility, have a tendency to segregate extensively along the grain boundaries and, therefore, reducing significantly the ionic conductivity. On the other hand, impurities like Fe exhibit a lesser tendency to segregate and, hence, can potentially stabilize the crystal structure of YSZ without adversely impacting conductivity. Since both ions, Al and Fe, are positive ions, the barrier of ion diffusion at grain boundaries is enhanced, further affecting the overall conductivity of both species. These results improve our understanding of the impurities segregation in YSZ while providing pathways to optimize the electrochemical performance of YSZ-based devices by manipulating impurities concentrations and grain boundary engineering.
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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".