Chemically Prepared Li₀.₆FePO₄ Solid Solution as a Vehicle for Studying Phase Separation Kinetics in Li-Ion Battery Materials
Bibliographic record
Abstract
The commercial success of LiFePO4 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 Li1–xFePO4 (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 Li0.6FePO4 into a phase-separated material is studied by analysis of the local and bulk structure. 6Li MAS NMR is used to probe the immediate environment where proximity to Fe3+ 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 LiFePO4 or FePO4 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 LiMPO4 systems (M = transition metal) that currently are limited by poor high-rate performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".