Exploring oxide cathodes for Li-ion batteries: From mineral mining to active material production
Bibliographic record
Abstract
Electrification is a pivotal strategy for addressing the challenges of climate change. Li-ion batteries (LIBs) have emerged as an essential technology for driving this transition. Over the years, researchers have focused on diverse cathode chemistries to achieve high energy density , safety, and cost-efficiency. In this study, cobalt-, nickel-, and manganese-rich oxide cathodes were investigated with a focus on their crystal structures and strategies for improving their structural stability and electrochemical performance. This study also explored the journey from critical mineral ores to battery-grade material production. With the growing demand for energy, the demand for necessary minerals has surged. Although battery recycling is a promising mineral recovery technique, extraction techniques must be improved to make them more efficient and environmentally friendly. This paper also discusses various synthesis methods used to produce CAM, emphasizing the parameters that can influence the electrochemical performance of the cathode oxides. Furthermore, the environmental impact of these LIBs was reviewed to identify areas for improvement and solidify the position of electric vehicles as greener alternatives to internal combustion engine vehicles.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".