Mechanisms of Thermal Decomposition in Spent NCM Lithium-Ion Battery Cathode Materials with Carbon Defects and Oxygen Vacancies
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Bibliographic record
Abstract
Resource recovery from retired electric vehicle lithium-ion batteries (LIBs) is a key to sustainable supply of technology-critical metals. However, the mainstream pyrometallurgical recycling approach requires high temperature and high energy consumption. Our study proposes a novel mechanochemical processing combined with hydrogen (H 2 ) reduction strategy to accelerate the breakdown of ternary nickel cobalt manganese oxide (NCM) cathode materials at a significantly lower temperature (450 °C). Particle refinement, material amorphization, and internal energy storage are considered critical success factors for the accelerated decomposition of NCM cathode materials. In our proposed approach, NCM cathode materials can develop active sites with carbon defects (C v ) and oxygen vacancies (O v ), which improve the reduction and breakdown of H 2 . The adsorbed H 2 on the surface of NCM decomposes into H* and combines with oxygen to form OH species, which can be facilitated by O v via the enhanced charge transfer. The introduced C v can enhance H 2 cracking and generate *C–H species to promote the thermal decomposition of NCM. The presence of defects proves to foster the preferential reduction of Mn(IV) by H 2, leading to a lower activation energy for the NCM decomposition (from 139 to 110 kJ/mol) with less H 2 consumption. Life cycle assessment suggests a reduction of 4.42 kg CO 2 eq for the recycling of every 1.0 kg of retired batteries. This study can promote material circularity and minimize the environmental burden of mining technology-critical metals for a low-carbon transition.
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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) | 0.000 | 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