Mechanisms of the Accelerated Li<sup>+</sup> Conduction in MOF‐Based Solid‐State Polymer Electrolytes for All‐Solid‐State Lithium Metal Batteries
Bibliographic record
Abstract
Abstract Solid polymer electrolytes (SPEs) for lithium metal batteries have garnered considerable interests owing to their low cost, flexibility, lightweight, and favorable interfacial compatibility with battery electrodes. Their soft mechanical nature compared to solid inorganic electrolytes give them a large advantage to be used in low pressure solid‐state lithium metal batteries, which can avoid the cost and weight of the pressure cages. However, the application of SPEs is hindered by their relatively low ionic conductivity. In addressing this limitation, enormous efforts are devoted to the experimental investigation and theoretical calculations/simulation of new polymer classes. Recently, metal–organic frameworks (MOFs) have been shown to be effective in enhancing ion transport in SPEs. However, the mechanisms in enhancing Li + conductivity have rarely been systematically and comprehensively analyzed. Therefore, this review provides an in‐depth summary of the mechanisms of MOF‐enhanced Li + transport in MOF‐based solid polymer electrolytes (MSPEs) in terms of polymer, MOF, MOF/polymer interface, and solid electrolyte interface aspects, respectively. Moreover, the understanding of Li + conduction mechanisms through employing advanced characterization tools, theoretical calculations, and simulations are also reviewed in this review. Finally, the main challenges in developing MSPEs are deeply analyzed and the corresponding future research directions are also proposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".