Different Impacts of Dissolved Transition Metals on the Graphite Anode in Lithium-Ion Batteries
Bibliographic record
Abstract
The dissolution of transition metals (TM) from the cathode and their subsequent deposition on the anode represent significant degradation mechanisms in lithium-ion batteries, particularly as the industry seeks to transition towards more sustainable and cost-efficient materials. In this work, the impacts of Mn, Fe, Ni, and Co depositions on the lithiated graphite anode were investigated using pouch storage experiments to simulate the migration-deposition process and compare it to electrodes from real cells. The morphology, chemical distribution, and oxidation states of deposited TMs were investigated by scanning electron microscopy, X-ray absorption spectroscopy, and scanning transmission X-ray microscopy. X-ray diffraction and half-cell studies for post-storage electrodes determined the lithium loss and impedance growth due to TM deposition. The impact of each TM on the lithiated graphite was found to be significantly different. Deposited Mn and Fe were fully metallic, preferred to accumulate on electrode surface, and caused severe delithiation of the graphite, while Ni and Co deposition were rather harmless. The results obtained from simulated TM-containing graphite electrodes closely corresponded with those extracted from cycled cells. This alignment enhances our understanding of the behavior of dissolved TM and paves the way for solutions aimed at mitigating capacity fade in commercial lithium-ion batteries.
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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 | 0.000 |
| 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 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".