LiNO<sub>3</sub>‐Based Electrolyte with Fast Kinetics for Lithium Metal Batteries Under Practical Conditions
Bibliographic record
Abstract
Abstract To be commercially viable, the electrolyte for lithium metal batteries (LMBs) must enable both long cycle life and fast charging characteristics under extreme conditions (high cathode loading, low negative/positive ratio, and low electrolyte/cathode ratio). While LiFSI‐based electrolytes typically provide LMBs with extended cycle life, they often fall short in terms of kinetics. This study, for the first time, demonstrates that the LiNO 3 ‐based electrolyte can simultaneously achieve excellent reversibility and rapid kinetics in LMBs, outperforming state‐of‐the‐art LiFSI‐based electrolytes. Notably, LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM811) || Li batteries exhibit 80% capacity retention after 430 cycles, along with outstanding rate performance (2.35 mAh cm⁻ 2 at 12 mA cm −2 ) under practical conditions (20 mg cm −2 NCM811, 50 µm Li foil, and 5.6 mL Ah⁻¹ electrolyte). The rapid kinetics can be attributed to the efficient transport of lithium ions through both the bulk electrolyte and the electrode/electrolyte interphases. The work highlights the significance of low‐cost LiNO 3 salt and presents an alternative pathway to achieving superior performance for lithium metal batteries under extreme conditions.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".