Evolution of Fe-Antisite Defects in Standard Hydrothermal LiFePO<sub>4</sub> Synthesis and Their Accelerated Removal with Ca<sup>2+</sup>
Bibliographic record
Abstract
Based on neutron powder diffraction (NPD), high angle annular dark field scanning transmission electron microscopy (HAADF-STEM), Electron Energy Loss Spectroscopy (EELS), X-Ray Photoelectron Spectroscopy (XPS) we show that Fe-antisites defects are surface defects (see Figure 1) and slowly eliminated by Fe-Li cation exchange during standard hydrothermal synthesis1. Recently2 we demonstrated that the calcium ions help eliminate the Fe antisite defects by controlling the nucleation and evolution of the LiFePO4 particles during their hydrothermal synthesis. The final Ca:LFP particles present Fe-antsite defects confined in a thin surface layer compared to standard LFP. Fig. 1. Figure 1: Schematic representation of Fe-antisite defects elimination during standard hydrothermal synthesis of LFP Reference 1. Paolella, A. et al. Cation exchange mediated elimination of the Fe-antisites in the hydrothermal synthesis of LiFePO 4. Nano Energy 16, 256–267 (2015). 2. Paolella, A. et al. Accelerated Removal of Fe-Antisite Defects while Nanosizing Hydrothermal LiFePO 4 with Ca 2+. Nano Lett. 2–7 (2016). doi:10.1021/acs.nanolett.6b00334 Figure 1
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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".