Recent progress and future perspectives of flexible metal‐air batteries
Bibliographic record
Abstract
Abstract With the rapid development of wearable and intelligent flexible electronic devices (FEDs), the demand for flexible energy storage/conversion devices (ESCDs) has also increased. Rechargeable flexible metal‐air batteries (MABs) are expected to be one of the most ideal ESCDs due to their high theoretical energy density, cost advantage, and strong deformation adaptability. With the improvement of the device design, material assemblies, and manufacturing technology, the research on the electrochemical performance of flexible MABs has made significant progress. However, achieving the high mechanical flexibility, high safety, and wearable comfortability required by FEDs while maintaining the high performance of flexible MABs are still a daunting challenge. In this review, flexible Zn‐air and Li‐air batteries are mainly exemplified to describe the most recent progress and challenges of flexible MABs. We start with an overview of the structure and configuration of the flexible MABs and discuss their impact on battery performance and function. Then it focuses on the research progress of flexible metal anodes, gel polymer electrolytes, and air cathodes. Finally, the main challenges and future research perspectives involving flexible MABs for FEDs are 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".