A review on recent advancement of nano-structured-fiber-based metal-air batteries and future perspective
Bibliographic record
Abstract
In an era of sustainable development and innovation ecosystem, a high-energy density is one of the important requirements for the development of new energy storage modalities, including battery technology. In a metal-air battery (MAB) system, metals function as negative active substances, and oxygen in the air acts as the positive component. Research to date confirms that, MABs have an exceptionally high energy density, durability, and specific power compared to conventional batteries. In addition, they are environmentally friendly, low-cost, and lightweight. In battery construction, fibrous materials have been shown to improve thermal stability, electronic & ionic conductivity, and cyclical performance of MABs. This is due to their outstanding properties that include large surface area-to-volume ratio, controllable morphology and highly porous structure. In this review, the basic working principle of MABs is presented. A critical overview that using a diverse array of fibrous materials related to metal-based, metal oxide-based, and carbon-based fiber shape materials used in MABs such as Li–O2, Zn–O2, Mg–O2, and Al–O2 systems are comprehensively summarized. We also identify the most promising systems according to their established scientific database with respect to sustainable development. Finally, the existing challenges and prospects are stressed considering the future perspective of MABs and fiber-shaped structure electrode material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".