Homogeneous and Stable Lithium Electrodeposition through a Thin Single-Ion Conducting Layer for High Cycling Stability of Lithium Metal Secondary Batteries
Bibliographic record
Abstract
The inhomogneous and unstable Li electrodeposition of lithium metal electrode has been a major impedment to the realization of rechargeable lithium metal batteries. Although polymeric single ion conductors can provide more homogeneous Li electrodeposition owing to their high Li ion transference number, their poor ionic conductivities prevent practical battery design. In the paper, we sugest a hybrid electrolyte based on a few micron-thick single-ion conducting layer laminated on Li metal electrode and a liquid electrolyte, which allows rapid Li+ transport and stable Li electrodeposition. The introduction of the single-ion conducting layer increases Li+ transference number from 0.451 to 0.855, consequently suppressig the generation of Li dendrite as demonstrated by SEM and impedance analysis. Reducing the thickness of the single-ion conducting layer down to a few microns and incorporating the bi-ionic liquid electrolyte permit room-temperature operation at high current densities. The Li/Li symmetric cell with the hybrid approach operate at a high current density of 10 mA cm-2 for more than 2000 h, which corresponds to more than five-fold enhancement compared to bare Li metal electrode, and the protyopye Li/LiCoO2 battery offers cycling stability more than 350 cycles at 0.2 C, demonstraing the practical applicability of this approach.
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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 | 0.001 |
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".