A clean and sustainable method for recycling of lithium from spent lithium iron phosphate battery powder by using formic acid and oxygen
Bibliographic record
Abstract
With the widespread adoption of lithium iron phosphate (LiFePO4) batteries, the imperative recycling of LiFePO4 batteries waste presents formidable challenges in resource recovery, environmental preservation, and socio-economic advancement. Given the current overall lithium recovery rate in LiFePO4 batteries is below 1 %, there is a compelling demand for an eco-friendly, cost-efficient, and sustainable solution. This study introduces a green and sustainable recycling method that employs environmentally benign formic acid and readily available oxygen as reaction agents for selectively leaching lithium from discarded lithium iron phosphate powder. Formic acid was employed as the leaching agent, and oxygen served as the oxidizing agent. Utilizing a single-factor variable approach, various factors including formic acid concentration, oxygen flow rate, leaching time, liquid-to-solid ratio, and reaction temperature were individually investigated. Moreover, the feasibility of this method was explored mechanistically by analyzing E-pH diagrams of the Li-Fe-P-H2O system. Results demonstrate that under conditions of 2.5 mol/L formic acid concentration, 0.12 L/min oxygen flow rate, 25 mL/g liquid-to-solid ratio, 70 °C reaction temperature, and 3 h reaction time, lithium leaching efficiency exceeds 99.9 %, with iron leaching efficiency only at 1.7 %. Moreover, we also explored using air instead of oxygen as the oxidant and get the excellent lithium leaching rate (97.81 %) and low iron leaching rate (4.81 %), which shows the outstanding selectivity. Furthermore, the environmentally benign composition of the chemical reagents, comprising only C, H, and O elements, establishes it as a genuinely green and sustainable technology for secondary resource recovery. It can be considered as a highly environmentally friendly, cost-effective, and efficient approach. Nevertheless, in the current context of carbon neutrality and sustainable development, this method undoubtedly holds excellent prospects for industrialization.
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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".