A laser-enabled low carbon emission pyrometallurgical approach to recycle Li-ion batteries via silicothermic reductions
Bibliographic record
Abstract
In response to the growing shift from graphite to silicon in Li-ion battery anodes, we propose a novel low-carbon pyrometallurgical recycling method that uses silicon as the reducing agent. Silicon was chosen as the reductant because, as the emerging high-capacity anode material, it not only integrates seamlessly with next-generation battery chemistries but also offers a substantially lower carbon footprint than conventional carbon-based reducing agents. The thermodynamics and reaction mechanism between LiCoO 2 and Si are investigated using differential thermal and thermogravimetric analyses. The reaction products are characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction. When heated to 1500 °C, LiCoO 2 undergoes simultaneous decomposition and melting, reacting with Si to produce cobalt spheres. Through a laser-enabled recycling process for only 30 s with a laser power of 2 kW, LiCoO 2 is reduced via silicothermic reaction to a Co–Si alloy with only a small amount of slag (Li 2 SiO 3 and Li 2 Co(SiO 4 )). This successful use of silicon paves the way for a cleaner, more sustainable battery recycling strategy.
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.
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".