Flexible Electrode Based on MWCNT Embedded in a Cross-Linked Acrylamide/Alginate Blend: Conductivity vs. Stretching
Bibliographic record
Abstract
A polyacrylamide-alginate hydrogel electrolyte, blended with Multi-Walled Carbon Nanotubes (MWCNT) as an electronically conductive fraction, allows for the creation of a flexible, durable, and resilient electrode. The MWCNT content is correlated with mechanical characteristics such as stretch modulus, tensile resistance, and electrical conductivity. The mechanical analysis demonstrates tensile strength that is comparable to similar hydrogels reported in the literature, with increasing strength for MWCNT-embedded hydrogels. The impedance spectroscopy reveals that the total resistance of electrodes decreases with increasing MWCNT content upon elongation and that bending and twisting do not obstruct their conductivity. The MWCNT-inserted hydrogels show mixed ionic and electronic conductivities, both within a range of 1–4 × 10−2 S cm−1 in a steady state. In addition, the thermal stability of these materials increases with incrementing MWCNT content. This observation agrees with long-term charge-discharge cycling that shows enhanced electrochemical durability of the MWCNT-hydrogel hybrid when compared to pure hydrogel electrolyte. The hydrogel-carbon films demonstrate an increased interfacial double-layer current at a high MWCNT content (giving an area-specific capacitance of ~30 mF cm−2 at 2.79 wt.% of MWCNT), which makes them promising candidates as printable and flexible electrodes for lightweight energy storage applications. The maximum content of MWCNT within the polymer electrolyte was estimated at 2.79 wt.%, giving a very elastic polymer electrode with good electrical characteristics.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".