Hierarchically Nanostructured Solid‐State Electrolyte for Flexible Rechargeable Zinc–Air Batteries
Bibliographic record
Abstract
Abstract The construction of safe and environmentally‐benign solid‐state electrolytes (SSEs) with intrinsic hydroxide ion‐conduction for flexible zinc–air batteries is highly desirable yet extremely challenging. Herein, hierarchically nanostructured CCNF‐PDIL SSEs with reinforced concrete architecture are constructed by nanoconfined polymerization of dual‐cation ionic liquid (PDIL, concrete) within a robust three‐dimensional porous cationic cellulose nanofiber matrix (CCNF, reinforcing steel), where plenty of penetrating ion‐conductive channels are formed and undergo dynamic self‐rearrangement under different hydrated levels. The CCNF‐PDIL SSEs synchronously exhibit good flexibility, mechanical robustness, superhigh ion conductivity of 286.5 mS cm−1, and decent water uptake. The resultant flexible solid‐state zinc–air batteries deliver a high‐power density of 135 mW cm−2, a specific capacity of 775 mAh g−1 and an ultralong cycling stability with continuous operation of 240 hours for 720 cycles, far outperforming those of the state‐of‐the‐art solid‐state batteries. The marriage of biomaterials with the diversity of ionic liquids creates enormous opportunities to construct advanced SSEs for solid‐state batteries.
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) | 0.000 | 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".