Self-Healable Electro-Conductive Hydrogels Based on Core-Shell Structured Nanocellulose/Carbon Nanotubes Hybrids for Use as Flexible Supercapacitors
Bibliographic record
Abstract
Recently, with the development of personal wearable electronic devices, the demand for portable power is miniaturization and flexibility. Electro-conductive hydrogels (ECHs) are considered to have great application prospects in portable energy-storage devices. However, the synergistic properties of self-healability, viscoelasticity, and ideal electrochemistry are key problems. Herein, a novel ECH was synthesized by combining polyvinyl alcohol-borax (PVA) hydrogel matrix and 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO)-cellulose nanofibers (TOCNFs), carbon nanotubes (CNTs), and polyaniline (PANI). Among them, CNTs provided excellent electrical conductivity; TOCNFs acted as a dispersant to help CNTs form a stable suspension; PANI enhanced electrochemical performance by forming a “core-shell” structural composite. The freeze-standing composite hydrogel with a hierarchical 3D-network structure possessed the compression stress (~152 kPa) and storage modulus (~18.2 kPa). The composite hydrogel also possessed low density (~1.2 g cm−3), high water-content (~95%), excellent flexibility, self-healing capability, electrical conductivity (15.3 S m−1), and specific capacitance of 226.8 F g−1 at 0.4 A g−1. The fabricated solid-state all-in-one supercapacitor device remained capacitance retention (~90%) after 10 cutting/healing cycles and capacitance retention (~85%) after 1000 bending cycles. The novel ECH had potential applications in advanced personalized wearable electronic 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".