Bilayer Zwitterionic Metal‐Organic Framework for Selective All‐Solid‐State Superionic Conduction in Lithium Metal Batteries
Bibliographic record
Abstract
Abstract Solid‐state batteries (SSBs) hold immense potential for improved energy density and safety compared to traditional batteries. However, existing solid‐state electrolytes (SSEs) face challenges in meeting the complex operational requirements of SSBs. This study introduces a novel approach to address this issue by developing a metal‐organic framework (MOF) with customized bilayer zwitterionic nanochannels (MOF‐BZN) as high‐performance SSEs. The BZN consist of a rigid anionic MOF channel with chemically grafted soft multicationic oligomers (MCOs) on the pore wall. This design enables selective superionic conduction, with MCOs restricting the movement of anions while coulombic interaction between MCOs and anionic framework promoting the dissociation of Li + . MOF‐BZN exhibits remarkable Li + conductivity (8.76 × 10 −4 S cm −1 ), high Li + transference number (0.75), and a wide electrochemical window of up to 4.9 V at 30 °C. Ultimately, the SSB utilizing flame retarded MOF‐BZN achieves an impressive specific energy of 419.6 Wh kg anode+cathode+electrolyte −1 under constrained conditions of high cathode loading (20.1 mg cm −2 ) and limited lithium metal source. The constructed bilayer zwitterionic MOFs present a pioneering strategy for developing advanced SSEs for highly efficient SSBs.
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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".