Electrolyte reactivity at the charged Ni-rich cathode interface and degradation in Li-ion batteries
Bibliographic record
Abstract
The chemical and electrochemical reactions at the positive electrode-electrolyte interface in Li-ion batteries are hugely influential on cycle life and safety. Ni-rich layered transition metal oxides exhibit higher interfacial reactivity than their lower Ni-content analogues, reacting via poorly understood mechanisms. Here, we study the role of the electrolyte solvent, specifically cyclic ethylene carbonate (EC) and linear ethyl methyl carbonate (EMC), in determining the interfacial reactivity at LiNi0.33Mn0.33Co0.33O2 (NMC111) and LiNi0.8Mn0.1Co0.1O2 (NMC811). Parasitic currents are measured during high voltage holds in NMC/Li4Ti5O12 (LTO) cells, LTO avoiding parasitic currents related to anode-cathode reduction species cross-over, and are found to be higher for EC-containing vs. EC-free electrolytes with NMC811. No difference between electrolytes are observed with NMC111. On-line gas analysis reveals this to be related to lattice oxygen release, and accompanying electrolyte decomposition, which increases substantially with greater Ni content, and for EC-containing electrolytes with NMC811. This is corroborated by electrochemical impedance spectroscopy (EIS) and transmission electron microscopy (TEM) of NMC811 after the voltage hold, which show a higher interfacial impedance and a thicker oxygen-deficient rock-salt surface reconstruction layer, respectively. Combined findings from solution NMR, ICP (of electrolytes) and XPS analysis (of electrodes) reveal that higher lattice oxygen release from NMC811 in EC-containing electrolytes is coupled with more electrolyte breakdown and higher amounts of transition metal dissolution compared to EC-free electrolyte. Finally, new mechanistic insights for the chemical oxidation pathways of electrolyte solvents and, critically, the knock-on chemical and electrochemical reactions that further degrade the electrolyte and electrodes curtailing battery lifetime are provided.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".