Conversion of LiFePO <sub>4</sub> to FePO <sub>4</sub> via Selective Lithium Bicarbonation: A Direct Pathway Towards Battery Recycling
Bibliographic record
Abstract
Recycling of spent LiFePO 4 batteries represents a challenge due to their relatively low economic value. This paper proposes a novel direct recycling route that extracts selectively lithium while keeping the delithiated solid product electrochemically active. The innovative use of CO 2 , as a mild solubilization agent for lithium, in conjunction with an oxidizing agent such as H 2 O 2 allows to selectively extract from 85% to 95% of the lithium content from pristine active material at room temperature and 2 atm CO 2 partial pressure, while keeping intact the orthorhombic heterosite structure of the delithiated iron phosphate (FePO 4 ). Extensive characterization studies revealed the FePO 4 product to remain highly pure with its carbon coating electronically active. In fact, the delithiated product showed similar electrochemical performance as the pristine material with an initial capacity at around 154 mAh.g −1 for a 12 h discharge rate (C/12) and a capacity retention of 98% after 100 cycles. When applied to spent LiFePO 4 batteries, the new direct process provided high de-lithiation efficiency exceeding 90% lithium extraction despite somewhat slower kinetics.
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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 | 0.001 |
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".