Self‐Formation CoO Nanodots Catalyst in Co(TFSI)<sub>2</sub>‐Modified Electrolyte for High Efficient Li‐O<sub>2</sub> Batteries
Bibliographic record
Abstract
Abstract The major challenges for Li‐O2 batteries are sluggish reaction kinetics and large overpotentials due to the cathode passivation resulting from insulative and insoluble Li2O2. Here, a novel nanodot (ND)‐modified electrolyte is designed by employing cobalt bis(trifluoromethylsulfonyl)imide (Co(TFSI)2) as an electrolyte additive. The Co(TFSI)2 additive can react with discharge intermediate LiO2 and product Li2O2 to form CoO NDs. The generated CoO NDs are well dispersed in electrolyte, which integrates both the high catalytic activity of solid catalyst and the good wettability of soluble catalyst. Under the catalytis of CoO NDs, Li2O2 is produced and deposits on the cathode together with them. At the recharge process, these well dispersed CoO NDs help to decompose solid Li2O2 at a lower overpotential. The Li‐O2 cells with Co(TFSI)2 exhibit a long cycle life of 200 cycles at a current density of 200 mA g−1 under a cutoff capacity of 1000 mAh g−1, as well as a superior reversibility associated with the Li2O2 formation and decomposition. The study is expected to broaden the range of electrolyte additives and provide a new view to developing highly dispersed NDs‐based catalysts for Li‐O2 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 | 0.000 | 0.000 |
| 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".