Na-Ion Mobility in Layered Na<sub>2</sub>FePO<sub>4</sub>F and Olivine NaFePO<sub>4</sub>
Bibliographic record
Abstract
Materials for sodium-ion batteries are attracting renewed interest. Olivine NaFePO4 and layered Na2FePO4F are interesting materials that have been reported recently as possible positive electrodes. Here, we report their Na-ion conduction behaviour and intrinsic defect properties using atomistic simulation methods.[1,2] In the NaFePO4 olivine, Na ion migration is essentially restricted to the [010] direction along a curved trajectory, similar to that of LiFePO4, but with a lower migration energy. However, Na/Fe antisite defects are also predicted to have a lower formation energy: the higher probability of tunnel occupation with a relatively immobile Fe2+ cation – along with a greater volume change (17%) on redox cycling – contributes to the poor electrochemical performance of the Na olivine. Na+ ion conduction in Na2FePO4F is predicted to be two-dimensional (2D) in the interlayer plane with a similar low activation energy. The antisite formation energy is slightly higher; furthermore, antisite occupation would not impede transport significantly owing to the 2D pathway. This factor, along with the much lower volume change (3.7%) on redox cycling, is undoubtedly responsible for the better electrochemical performance of the layered structure. [1] R. Tripathi, S.M. Wood, M.S. Islam and L.F. Nazar, Energy and Envrion. Sci., 2013, 6, 2257-2264 [2] M.S. Islam and C.A.J. Fisher, Chem. Soc. Rev., 2014, 43, 185-204
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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".