Alkali-ion Conduction Paths in LiFeSO<sub>4</sub>F and NaFeSO<sub>4</sub>F Tavorite-Type Cathode Materials
Bibliographic record
Abstract
A new family of fluorosulfates has attracted considerable attention as alternative positive electrode materials for rechargeable lithium batteries. However, an atomic-scale understanding of the ion conduction paths in these systems is still lacking, and this is important for developing strategies for optimization of the electrochemical properties. Here, the alkali-ion transport behavior of both LiFeSO 4 F and NaFeSO 4 F are investigated by atomistic modeling methods. Activation energies for numerous ion migration paths through the complex structures are calculated. The results indicate that LiFeSO 4 F is effectively a three-dimensional (3D) lithium-ion conductor with an activation energy of ∼0.4 eV for long-range diffusion, which involve a combination of zigzag paths through [100], [010], and [111] tunnels in the open tavorite lattice. In contrast, for the related NaFeSO 4 F, only one direction ([101]) is found to have a relatively low activation energy (0.6 eV). This leads to a diffusion coefficient that is more than 6 orders of magnitude lower than any other direction, suggesting that NaFeSO 4 F is a one-dimensional (1D) Na-ion conductor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".