Engineering Functionalized 2D Metal‐Organic Frameworks Nanosheets with Fast Li<sup>+</sup> Conduction for Advanced Solid Li Batteries
Bibliographic record
Abstract
Abstract Solid‐state batteries can ensure high energy density and safety in lithium metal batteries, while polymer electrolytes are plagued by slow ion kinetics and low selective transport of Li+. Metal‐organic frameworks (MOFs) are proposed as emerging fillers for solid‐state poly(ethylene oxide)(PEO) electrolytes, however, developing functionalized MOFs and understanding their roles on ion transfer has proven challenging. Herein, combining computational and experimental results, the functional group regulation in MOFs can effectively change surficial charge distribution and limit anion movement is revealed, providing a potential solution to these issues. Specifically, functionalized 2D MOF sheets are designed through molecular engineering to construct high‐performance composite electrolytes, where the electron‐donating effect of substituents in 2D‐MOFs effectively limits the movement of ClO4− and promotes mechanical properties and ion migration numbers (0.36 up to 0.64) of PEO. As a result, Li/Li cells with composite electrolyte exhibit superior cyclability for 1000 h at a current density of 0.2 mA cm−2. Meanwhile, the solid LiFePO4/Li battery delivers highly reversible capacities of 148.8 mAh g−1 after 200 cycles. These findings highlight a new approach for anion confinement through the use of functional group electronic effects, leading to enhanced ionic conductivity, and a feasible direction for high‐performance solid‐state 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 | 0.000 |
| 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".