Melt‐synthesis of LiFePO<sub>4</sub> over a metallic bath
Bibliographic record
Abstract
Abstract Compared with large‐scale processes, the LiFePO4 (LFP) melt‐synthesis is a low‐cost method with short dwell times and rapid reaction rates. However, secondary phases and impurities remaining in the olivine structure lower the cathode's electrochemical properties. Starting from a low‐cost Fe3+ precursor, we evaluated tin and silver charged metallic baths to purify the melt‐synthesis of LiFePO4 at laboratory scale. In the tin bath exploration, an x‐ray diffraction (XRD) confirmed the olivine structure and a temperature‐dependent generation of Li3PO4 and Li4P2O7. An SEM image analysis identified tin‐rich phases (SnxPyOz) segregated from the LFP structure and FexP phases on the internal walls of the crucible. A multi‐element analysis (ICP‐AES) detected more than 0.03 g of Sn/g of LFP. The tin bath prepared samples delivered up to156 mAh/g of LFP in a carbon‐free basis, 3 % lower than the capacity of the high purity Fe2O3‐based material at 0.1 C. The silver bath‐based LFP samples produced cleaner XRD patterns (less than 160 ppm of Ag in the LFP ingots), closer to the estimated molar ratios and neither silver compounds nor silver oxides. In this case, the sample delivered 161 mAh/g of LFP, the same capacity as the cathode prepared without a metallic bath. Starting from a commodity Fe3+ source, future work should explore the silver bath roles as a reactive media, a heating source, a crucible insulator, and a potential contaminant trap for the melt‐synthesis of LiFePO4.
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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) | 0.002 | 0.001 |
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".