Kinetic investigation of the energy storage process in graphene fiber supercapacitors: Unraveling mechanisms, fabrications, property manipulation, and wearable applications
Bibliographic record
Abstract
Abstract Graphene fiber supercapacitors (GFSCs) have garnered significant attention due to their exceptional features, including high power density, rapid charge/discharge rates, prolonged cycling durability, and versatile weaving capabilities. Nevertheless, inherent challenges in graphene fibers (GFs), particularly the restricted ion‐accessible specific surface area (SSA) and sluggish ion transport kinetics, hinder the achievement of optimal capacitance and rate performance. Despite existing reviews on GFSCs, a notable gap exists in thoroughly exploring the kinetics governing the energy storage process in GFSCs. This review aims to address this gap by thoroughly analyzing the energy storage mechanism, fabrication methodologies, property manipulation, and wearable applications of GFSCs. Through theoretical analysis of the energy storage process, specific parameters in advanced GF fabrication methodologies are carefully summarized, which can be used to modulate nano/micro‐structures, thereby enhancing energy storage kinetics. In particular, enhanced ion storage is realized by creating more ion‐accessible SSA and introducing extra‐capacitive components, while accelerated ion transport is achieved by shortening the transport channel length and improving the accessibility of electrolyte ions. Building on the established structure–property relationship, several critical strategies for constructing optimal surface and structure profiles of GF electrodes are summarized. Capitalizing on the exceptional flexibility and wearability of GFSCs, the review further underscores their potential as foundational elements for constructing multifunctional e‐textiles using conventional textile technologies. In conclusion, this review provides insights into current challenges and suggests potential research directions for GFSCs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".