Mechanistic Study of Electrolyte Additives to Stabilize High-Voltage Cathode–Electrolyte Interface in Lithium-Ion Batteries
Bibliographic record
Abstract
Current developments of electrolyte additives to stabilize electrode–electrolyte interface in lithium-ion batteries highly rely on a trial-and-error search, which involves repetitive testing and intensive amount of resources. The lack of understandings on the fundamental protection mechanisms of the additives significantly increases the difficulty for the transformational development of new additives. In this study, we investigated two types of individual protection routes to build a robust cathode–electrolyte interphase at high potentials: (i) a direct reduction in the catalytic decomposition of the electrolyte solvent; and (ii) formation of a “corrosion inhibitor film” that prevents severely attack and passivation from protons that generated from the solvent oxidation, even the decomposition of solvent cannot be mitigated. Effect of two exemplary electrolyte additives, lithium difluoro(oxalato)borate (LiDFOB) and 3-hexylthiophene (3HT), on LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC 622) cathode were investigated to validate our hypothesis. It is demonstrated that understandings of both electrolyte additives and solvent are essential and careful balance between the cathode protection mechanism of additives and their side effects is critical to obtain optimum results. More importantly, this study opens up new directions of rational design of functional electrolyte additives for the next-generation high-energy-density lithium-ion chemistries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".