Effect of Lithium Borate Additives on Cathode Film Formation in LiNi<sub>0.5</sub>Mn<sub>1.5</sub>O<sub>4</sub>/Li Cells
Bibliographic record
Abstract
Abstract A direct comparison of the cathode–electrolyte interface (CEI) generated on high-voltage LiNi0.5Mn1.5O4 cathodes with three different lithium borate electrolyte additives, lithium bis(oxalato)borate (LiBOB), lithium 4-pyridyl trimethyl borate (LPTB), and lithium catechol dimethyl borate (LiCDMB), has been conducted. The lithium borate electrolyte additives have been previously reported to improve the capacity retention and efficiency of graphite/LiNi0.5Mn1.5O4 cells due to the formation of passivating CEI. Linear sweep voltammetry (LSV) suggests that incorporation of the lithium borates into 1.2 M LiPF6 in EC/EMC (3/7) electrolyte results in borate oxidation on the cathode surface at high potential. The reaction of the borates on the cathode surface leads to an increase in impedance as determined by electrochemical impedance spectroscopy (EIS), consistent with the formation of a cathode surface film. Ex-situ surface analysis of the electrode via a combination of SEM, TEM, IR-ATR, XPS, and high energy XPS (HAXPES) suggests that oxidation of all borate additives results in deposition of a passivation layer on the surface of LiNi0.5Mn1.5O4 which inhibits transition metal ion dissolution from the cathode. The passivation layer thickness increases as a function of additive structure LiCDMB > LPTB > LiBOB. The results suggest that the CEI thickness can be controlled by the structure and reactivity of the electrolyte additive.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".