Metal–organic frameworks and derivatives as next-generation materials for electrochemical energy storage
Bibliographic record
Abstract
The global pursuit of carbon neutrality demands transformative clean energy solutions, with advanced energy storage materials at the forefront. Metal-organic frameworks (MOFs), owing to their tunable porosity, ultrahigh surface areas, and adaptable physicochemical properties, have rapidly risen as promising building blocks for next-generation electrochemical energy storage. Beyond pristine MOFs, engineered composites and derivatives now showcase remarkable multifunctionality, enabling improved performance in diverse battery systems. Despite this progress, significant barriers remain in translating laboratory success into practical deployment. This review provides a systematic overview of recent advances in MOF-based materials, highlighting their evolving roles as electrodes and separators in Li/Na/K-ion, Li/Na/K-S, and Zn-ion batteries. We classify design strategies by battery type, critically assess electrochemical performance, and dissect the structure-property-function relationships that underpin device operation. Finally, we outline the central challenges-stability, scalability, and interface engineering-while offering forward-looking perspectives on how to bridge these gaps. By integrating state-of-the-art progress with future opportunities, this review seeks to inspire innovative material design and accelerate the realization of sustainable MOF-based energy storage technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".