A clean and sustainable method for recycling of lithium from spent lithium iron phosphate battery powder by using formic acid and oxygen
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it