In Situ Polymer-Integrated Metal–Organic Framework for Solid-State Electrolyte Membrane
Bibliographic record
Abstract
Solid-state lithium metal batteries hold great promise for energy storage by addressing the limited cycle life and safety issues inherent in liquid electrolyte systems. Nevertheless, the development of efficient solid-state electrolytes remains a significant challenge. In this work, we present an electrolyte membrane fabricated through the integration of metal–organic frameworks (MOFs) by in situ-formed polymer chains. Specifically, lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) is employed to initiate the in situ polymerization of N, N -dimethylacrylamide within MOFs. The resulting polymer chains not only interconnect MOF particles to form a composite electrolyte membrane but also provide continuous ion-conductive paths for Li + transport. Meanwhile, the confinement within the MOF channels restricts the mobility of bulky TFSI – anions. As a result, the composite electrolyte membrane achieves a high room-temperature ionic conductivity exceeding 10 –4 S·cm –1, a remarkable Li + transference number of 0.77, and a broad electrochemical stability window up to 5.67 V. Furthermore, the electrolyte membrane demonstrates excellent interfacial compatibility with the lithium metal, effectively inhibiting dendrite growth. When applied in lithium metal batteries, it enables a specific capacity of 124.2 mAh·g –1 at 1.0 C, a high Coulombic efficiency of 99.5%, and outstanding rate performance.
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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".