Characteristics of Spent Lithium Ion Batteries and Their Recycling Potential Using Flotation Separation: A Review
Bibliographic record
Abstract
At the end of their efficient functionality in energy production/storage applications, spent lithium-ion batteries need to be recycled. Recycling remains the most preferred economic option with benefits such as prevention/reduction of environmental issues due to landfilling and more efficient use of natural resources. In this paper, characteristics of lithium-ion battery components before and after being spent are presented, together with highlights of various extractive options suitable for recycling. The main emphasis of this review is on the direct recycling approach, which employs the physical separation of anode and cathode materials. Since flotation is the most common processing method successfully applied to the physical separation of minerals in the mining industry, researchers have given a lot of attention to this area. The success of recycling by flotation is mainly dependent on wettability differences between the anode (hydrophobic) and cathode (hydrophilic) components. However, such components are subjected to surface modifications due to the intimate organic coating introduced in battery production. As such, the hydrophobic entities of the solid electrolyte in battery assembly, which are so essential for the electrochemical functionality of the battery during its life cycle, present main challenges on the selectivity of flotation as a recycling option. Thus, the restoration of the original hydrophobicity/hydrophilicity level of each electrode has been the main focus area for many investigations. This paper also provides an up-to-date review of proposed pretreatment options.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".