Designing Cooperative Ion Transport Pathway in Ultra‐Thin Solid‐State Electrolytes toward Practical Lithium Metal Batteries
Bibliographic record
Abstract
Abstract Solid polymer electrolytes (SPEs) are promising for high‐energy‐density solid‐state Li metal batteries due to their decent flexibility, safety, and interfacial stability. However, their development was seriously hindered by the interfacial instability and limited conductivity, leading to inferior electrochemical performance. Herein, we proposed to design ultra‐thin solid‐state electrolyte with long‐range cooperative ion transport pathway to effectively increase the ionic conductivity and stability. The impregnation of PVDF−HFP inside pores of fluorinated covalent organic framework (CF3−COF) can disrupt its symmetry, rendering rapid ion transportation and inhibited anion immigration. The functional groups of CF3−COF can interact with PVDF−HFP to form fast Li+ transport channels, which enables the uniform and confined Li+ conduction within the electrolyte. The introduction of CF3−COF also enhances the mechanical strength and flexibility of SPEs, as well as ensures homogeneous Li deposition and inhibited dendrite growth. Hence, a remarkably high conductivity of 1.21×10−3 S cm−1 can be achieved. Finally, the ultra‐thin SPEs with an extremely long cycle life exceed 9000 h can be obtained while the NCM523/Li pouch cell demonstrates a high capacity of 760 mAh and 96 % capacity retention after cycling, holding great promises to be utilized for practical solid‐state Li metal 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 | 0.001 |
| 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".