Chemically Prepared Li<sub>0.6</sub>FePO<sub>4</sub> Solid Solution as a Vehicle for Studying Phase Separation Kinetics in Li-Ion Battery Materials
Bibliographic record
Abstract
The commercial success of LiFePO 4 in high-power Li-ion batteries is strongly related to its unique ultrahigh-rate charge/discharge performance that permits full charge in less than a minute. Since Li 1– x FePO 4 (0.05 ≤ x ≤ 0.95) separates into two phases with poor electronic and ionic conduction, this raises questions regarding the structural dynamics of phase separation. In this paper, the transformation of metastable solid solution Li 0.6 FePO 4 into a phase-separated material is studied by analysis of the local and bulk structure. 6 Li MAS NMR is used to probe the immediate environment where proximity to Fe 3+ results in a significant shift in resonance frequency. Conversely, time-resolved X-ray diffraction (XRD) measurements reveal the transformation kinetics at the unit cell scale. The XRD showed no preferential relaxation along the a, b, and c crystal axes, consistent with the absence of a phase boundary perpendicular to the fast diffusion b axis. Key to the analysis is the preparation of the solid solution, which yields phase-pure samples exhibiting no evidence of the thermodynamically stable LiFePO 4 or FePO 4 phases. Long-term measurement indicated that after 263 days under an argon atmosphere these samples still exhibited a solid solution fraction > 40%. However, in the presence of an electrolyte, phase separation is significantly more rapid. The results presented support Li et al. model [ Nat. Mater. 2018, 17, 915], where vehicular lithium transport at the surface determines the rate of phase separation and offers a methodology for studying high-energy-density LiMPO 4 systems (M = transition metal) that currently are limited by poor high-rate performance.
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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".