Progress and Status of Hydrometallurgical and Direct Recycling of Li-Ion Batteries and Beyond
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
An exponential market growth of Li-ion batteries (LIBs) has been observed in the past 20 years; approximately 670,000 tons of LIBs have been sold in 2017 alone. This trend will continue owing to the growing interest of consumers for electric vehicles, recent engagement of car manufacturers to produce them, recent developments in energy storage facilities, and commitment of governments for the electrification of transportation. Although some limited recycling processes were developed earlier after the commercialization of LIBs, these are inadequate in the context of sustainable development. Therefore, significant efforts have been made to replace the commonly employed pyrometallurgical recycling method with a less detrimental approach, such as hydrometallurgical, in particular sulfate-based leaching, or direct recycling. Sulfate-based leaching is the only large-scale hydrometallurgical method currently used for recycling LIBs and serves as baseline for several pilot or demonstration projects currently under development. Conversely, most project and processes focus only on the recovery of Ni, Co, Mn, and less Li, and are wasting the iron phosphate originating from lithium iron phosphate (LFP) batteries. Although this battery type does not dominate the LIB market, its presence in the waste stream of LIBs causes some technical concerns that affect the profitability of current recycling processes. This review explores the current processes and alternative solutions to pyrometallurgy, including novel selective leaching processes or direct recycling approaches.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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