All-in-One Graphene Based Composite Fiber: Toward Wearable Supercapacitor
Bibliographic record
Abstract
Graphene fibers (GF) have aroused great interest in wearable electronics applications because of their excellent mechanical flexibility and superior electrical conductivity. Herein, an all-in-one graphene and MnO 2 composite hybrid supercapacitor fiber device has been developed. The unique coaxial design of this device facilitates large-scale production while avoiding the risk of short circuiting. The core backbone of the device consists of GF that not only provides mechanical stability but also ensures fast electron transfer during charge–discharge. The introduction of a MnO 2 (200 nm in length) hierarchical nanostructured film enhanced the pseudocapacitance dramatically compared to the graphene-only device in part because of the abundant number of active sites in contact with the poly(vinyl alcohol) (PVA)/H 3 PO 4 electrolyte. The entire device exhibits outstanding mechanical strength as well as good electrocapacitive performance with a volumetric capacitance of 29.6 F cm –3 at 2 mv s –1 . The capacitance of the device did not fade under bending from 0° to 150°, while the capacitance retention of 93% was observed after 1000 cycles. These unique features make this device a promising candidate for applications in wearable fabric supercapacitors.
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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".