Accelerated Removal of Fe-Antisite Defects while Nanosizing Hydrothermal LiFePO<sub>4</sub> with Ca<sup>2+</sup>
Bibliographic record
Abstract
Based on neutron powder diffraction (NPD) and high angle annular dark field scanning transmission electron microscopy (HAADF-STEM), we show that calcium ions help eliminate the Fe-antisite defects by controlling the nucleation and evolution of the LiFePO4 particles during their hydrothermal synthesis. This Ca-regulated formation of LiFePO4 particles has an overwhelming impact on the removal of their iron antisite defects during the subsequent carbon-coating step since (i) almost all the Fe-antisite defects aggregate at the surface of the LiFePO4 crystal when the crystals are small enough and (ii) the concomitant increase of the surface area, which further exposes the Fe-antisite defects. Our results not only justify a low-cost, efficient and reliable hydrothermal synthesis method for LiFePO4 but also provide a promising alternative viewpoint on the mechanism controlling the nanosizing of LiFePO4, which leads to improved electrochemical performances.
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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".