Flame‐assisted spray pyrolysis of lithium and manganese precursors to polycrystalline LiMn<sub>2</sub>O<sub>4</sub>
Bibliographic record
Abstract
Abstract Lithium manganese oxide cathodes have a smaller environmental footprint compared to cathodes with cobalt and nickel. Flame assisted spray pyrolysis (FASP) is an emerging technique that may improve the economics of the process at the commercial scale while controlling purity. reacts with to form . Carbon from the flame coats the particles and, together with the Mn oxides, they reduce electrical capacity. The reaction temperature and droplet residence time are the main parameters that determine manganese oxide content and therefore the product purity. LECO carbon analysis confirms that the fuel and precursor type affect carbon content. It increases from % with nitrate precursors to 2 % with carbonate and acetate precursors. The temperature profile as well as the solute type and concentration change the product morphology. The particle surface is wrinkled at a mild temperature and blowholes form at a high temperature. Primary nano‐crystals (5–8 nm) agglomerate to form 1 polycrystalline particles from a 0.5 nitrate precursors solution, while a 5 solution produces 10 powders. We modelled the reaction kinetics based on thermogravimetric analyses to identify the principle reaction steps. Oxide formation increases below 400 C and above 800 C, while fast heating rates and short residence time reduce purity.
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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".