Sulfonated White‐Graphene for High‐Performance Gel Polymer Electrolytes: The Interplay between Ion Conductivity and Rheology
Bibliographic record
Abstract
Abstract Research into flexible solid‐state supercapacitors for wearable electronics focuses on achieving high performance and safety. Gel polymer electrolytes (GPEs) are preferred over fully solid‐state electrolytes due to their better ionic conductivity while addressing safety concerns associated with liquid electrolytes. This study aims to enhance high‐performance gel polymer electrolytes (HP‐GPEs) by improving the ion transfer rate of polyvinyl alcohol (PVA) with sulfonated hexagonal boron nitride (known as white‐graphene) and exploring how rheology influences ion‐conduction within HP‐GPEs. The systematic analysis of GPEs highlights the dominant role of the loss factor in quasi‐solid GPEs. With less energy dissipation in the polymeric structure, ion movement occurs along an optimized pathway, as reflected in the calculated values of the diffusion coefficient and ion mobility from impedance analysis. Physico‐electro‐chemical characterizations of the HP‐GPEs revealed that the 3D network of 2D nanosheets and crystallites formed a more uniform and reduced pore size (decreasing from ≈7 µm to ≈221 nm), increased ion conduction by 6‐fold (70.7 mS cm−1), and led to an increment of ≈35% in specific capacitance with an impressive 96% retention after 10 000 cycles. These findings underscore the importance of engineering the rheological and structural properties in hydrogels as promising electrolytes for high‐performance energy storage devices.
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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) | 0.000 | 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".