A Novel Flexible Hybrid Battery–Supercapacitor Based on a Self‐Assembled Vanadium‐Graphene Hydrogel
Bibliographic record
Abstract
Abstract A novel flexible hybrid battery–supercapacitor device is proposed consisting of high specific surface area electrodes paired with an electrolyte, which contains a redox species that can exist in more than two oxidation states. The two initially equal half‐cells of the device consist of a reduced graphene oxide hydrogel which encapsulates vanadium ions, synthesized with a single‐step method. During charge, the oxidation state of the vanadium ions changes, resulting in two half‐cells with different potentials which considerably increases the energy density. The achieved maximum capacity of more than 225 mAh g−1 is roughly eight times higher than that of comparable graphene hydrogel supercapacitors without vanadium content, but the potentiostatic charging time is only double. Operated as a supercapacitor, it retains 95% of the initial capacitance over 1000 cycles. As battery, the losses are more significant, retaining around 50% of the initial capacity. However, these losses during battery operation can be almost entirely restored by electric measures. The vanadium ion addition also improves the self‐discharge characteristics of the device. Moreover, the self‐discharge does not permanently damage the hybrid device since both half‐cells initially consist of the same vanadium graphene hydrogel and discharging resets it to initial conditions.
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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 | 0.001 | 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".