Melt Synthesis of Lithium Manganese Iron Phosphate: Part I. Composition, Physical Properties, Structural Analysis, and Charge/Discharge Cycling
Bibliographic record
Abstract
Melt synthesis is a fast and simple process to make dense LiMnyFe1-yPO4 (LMFP with 0 ≤ y ≤ 1) from all-dry, low-cost precursors with zero waste. This study characterizes melt LMFP materials with 0%–100% Mn after particle size reduction by planetary milling and carbon coating with glucose. The melt LMFP samples show higher electrical conductivity at similar pellet density than LFP (0% Mn) and LMFP (79% Mn) reference samples made by traditional methods. The melt LMFP samples exhibit higher crystallinity than the reference samples and show no crystalline impurities. Their unit cell volume and crystallographic density scale with Mn content; the percentage of Fe and/or Mn in Li positions is below 1.5%, which is comparable to reference samples. Crystallite sizes of at least 100 to 175 nm are observed for melt LMFP, which is larger than the fine ∼50 nm crystallites of reference LMFP. Melt LFP shows specific discharge capacity and cycling stability comparable to reference LFP, but the melt LMFP samples with 25%–100% Mn shows worse performance than reference LMFP (79% Mn). Part two of this study will quantify the solid-state lithium diffusion coefficient in melt LMFP materials and correlate it to their electrochemical 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".