Fe<sup>3+</sup> reduction during melt‐synthesis of LiFePO<sub>4</sub>
Bibliographic record
Abstract
LiFePO4 (LFP) is a safe and low cost cathode material for Li‐ion batteries. Its solid‐state synthesis requires micron‐sized reactants yielding high production costs. Here, we melt‐synthesized up to 5 kg batches of LFP from low‐cost coarse Fe2O3 (509 µm) in an induction furnace. Graphite from the crucible was an effective reducing agent. Adding metallic Fe or CO increased the Fe2+ content and reaction kinetics. Metallic Fe improves the lifetime of the graphite crucible but requires a premixing step for it to be effective, otherwise the Fe powder agglomerates due to the presence of a eutectic in the LiPO3‐Fe‐Fe2O3 system. In a pushout furnace configuration, for an hour‐long holding period, injecting CO into the melt increased the Fe2+ content from 0.301 to 0.315 g/g, which we attributed to melt protection. Likewise, graphite powder floating on top of the melt further improved the Fe2+ content to 0.331 g/g. The Fe2+ content reached 0.325 g/g when using fine Fe3+ (142 µm) and CO as reducing agent at half the holding period at 1150 °C. We attribute the higher reaction rate to the improved contact between the suspended Fe3+ and the CO reducing gas. When the graphite crucible is the unique reducing agent, the reaction rate was proportional to the crucible base surface area. A zero‐order kinetic model characterized the solids disappearance with time. A thermal model developed to compare lab‐scale data against small pilot‐scale demonstrated that the charge lagged the furnace temperature by as much as 22 min at 1000 °C.
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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".