Diffusion mechanisms for spinel ferrite NiFe2O4 by using kinetic activation–relaxation technique
Bibliographic record
Abstract
Mass transport in bulk spinel ferrites NiFe2O4 is studied computationally using the kinetic activation-relaxation technique (k-ART), an off-lattice kinetic Monte Carlo algorithm. Diffusion mechanisms-difficult to observe with molecular dynamics-are described by k-ART. Point defects are assumed to be responsible for ionic diffusion; thus, both cation and anion defects are investigated. This work focuses on vacancies and interstitials by comparing their properties with two Buckingham potential parameterizations: one with nominal charges and the other with partial charges. Both potentials are corrected at short distances, thus allowing interstitial diffusion and avoiding the catastrophic infinite energies appearing with Buckingham at short distances. The energy landscape along different pathways is described in detail. Both potentials predict the same mechanisms but different migration energies. Mechanisms by which a normal spinel is transformed to an inverse spinel via cation diffusion are unveiled, and diffusion coefficients are predicted. We find that interstitial Ni diffusion involves the movement of two Ni ions and that O interstitials trigger a collective diffusion of O ions, while an O vacancy diffuses by an O ion moving to the center of a cuboctahedron.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".