Selective leaching of lithium ions from <scp>LiFePO<sub>4</sub></scp> powders using hydrochloric acid and sodium hypochlorite system
Bibliographic record
Abstract
Abstract High‐efficiency and selective leaching of lithium ions from spent lithium iron phosphate (LiFePO4) batteries is currently an urgent problem to be solved. Hydrochloric acid and sodium hypochlorite were used as acidic media and oxidant for recycling LiFePO4 powders based on the stoichiometric ratio. The effect of operation conditions on leaching performance was investigated. The leaching yield of Li and Fe were higher than 95% and lower than 0.1% within 20 min at 15°C. In addition, X‐ray diffraction, X‐ray photoelectron spectroscopy, scanning electron microscopy, and laser particle size analysis characterizations were applied for revealing the selective leaching mechanism, the results of which indicated that the crystal structure of powders remained basically unchanged during the leaching process. Finally, an efficient and cost‐effective recycling process for LiFePO4 powders was proposed, and lithium carbonate products with purity higher than 99.7% were obtained. The proposed recycling process shows strong industrial application potential for LiFePO4 powders.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".