Activating Carbon and Oxygen Bonds for Low-Temperature Thermal Decomposition of Spent Lithium-Ion Battery Cathode Materials
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The temperature for complete disintegration of spent lithium-ion battery (LIB) cathode materials is typically in a range of 750–1400 °C, resulting in intensive energy consumption and high carbon emissions. Here, we promote the bond activation of oxygen in LiNi 0.5 Co 0.2 Mn 0.3 O 2 and carbon in graphite electrodes, achieving rapid gasification and thermal decomposition of active crystals at lower temperatures in the absence of other activating agents. The activation of C and O bond leads to the storage of internal energy and the transition of the crystalline phase (single crystal to polycrystal) of the active crystals. Density functional theory modeling confirms that the CO adsorption energy is significantly higher with C a –O a (−3.35 eV, C and O activation) than with no activation (−1.66 eV). The differential charge results show that the bond activation model has the highest charge accumulation and consumption, improving the electron transfer. The Bader charge transfer between C a –O a and CO is also the largest, with a value of 0.433 |e|. Therefore, synchronous activation of C and O bonds can reduce the decomposition temperature of active crystals by 200 °C and allows a low-temperature pyrolysis recycling of retired LIB cathode materials. Our research provides a potential strategy for low-carbon recycling of retired LIBs worldwide.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".