On the formation and diffusion of oxygen vacancies in non-stoichiometric mixed Co <sub> 3- <i>x</i> </sub> Mn <sub> <i>x</i> </sub> O <sub>4</sub> spinel structures from first-principles calculations
Bibliographic record
Abstract
Abstract Using first-principles simulations, we focus on the study of Co 3 O 4 –Mn 3 O 4 mixed oxides, which have recently shown alluring features as thermochemical heat storage materials. We provide fundamental atomistic-level insight into the thermodynamics and kinetics of a series of non-stoichiometric Co 3- x Mn x O 4- y (0 ⩽ x ⩽ 3 and y = 0, 0.125, 0.250) bulk systems, by examining in detail the formation and diffusion processes of oxygen vacancies as a function of Mn content. We find a preference for the formation of vacancies at x = 1.5. And we predict a significant drop of diffusion barriers for x ⩾ 1.5, when Mn atoms start to populate the spinel octahedral sites as Mn 3+ . Our results pave the way for better understanding the underlying mechanisms that govern oxygen vacancy dynamics in Co 3- x Mn x O 4 in general, and, in particular, the reversible reduction and re-oxidation reactions of these promising mixed oxides for thermal energy storage. Nevertheless, some discrepancies are found between our calculations on bulk models and recent experimental insights from the literature, which suggests that surface and finite size effects might play an important role in controlling the observed macroscopic behavior of these materials during reversible reduction and re-oxidation cycles.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".