Dual‐Enhanced Charge Transfer through Prelithiation Strategy in Polymer Electrolyte Enables Robust LiF‐Rich SEI for Ultralong‐Life All‐Solid‐State Batteries
Bibliographic record
Abstract
Abstract Lithium fluoride (LiF)‐rich solid electrolyte interface (SEI) is critical for enabling the stable operation of polymer‐based all‐solid‐state lithium‐metal batteries (ASSLMBs). Precisely controlling the C─F dissociation chemistry in fluorine‐containing lithium salts to construct a LiF‐rich SEI is a logically viable but still challenging approach. Current strategies for constructing LiF‐rich SEI primarily focus on designing non‐metal polar groups and related structures. In contrast, approaches leveraging metal‐based electron donors to facilitate charge transfer for C─F bond cleavage and LiF formation remain largely unexplored. Herein, a dual‐enhanced charge transfer mechanism through prelithiation strategy is proposed in solid polymer electrolyte (SPE) for C─F bond cleavage. The charge transfer occurs between LiTFSI and the introduced metal sites and further enhanced by lithiation design, thereby achieving a robust LiF‐rich SEI. The achieving SPEs enable an excellent cyclability of Li|Li cell over 3800 h at 0.3 mA cm −2 . Li||LiFePO 4 ASSLMBs demonstrate a high Coulombic efficiency of ≈100% and a stability of 1200 cycles with capacity retention of 80% at 2C. The corresponding pouch cell delivers a high average areal capacity of 2.41 mAh cm −2 over 1600 h. This work offers a novel approach for constructing LiF‐rich SEI toward durable ASSLMBs.
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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".