Highly electro‐responsive composite gel based on functionally tuned graphene filled polyvinyl chloride
Bibliographic record
Abstract
Abstract Electroresponsive devices have attended significant interest for actuators and artificial muscles in recent days but are susceptible to low deformation at applied voltages. To address this problem, herein, we have prepared novel flexible composite gel based on polyvinyl chloride (PVC), dibutyl adipate (DBA), and functionalized reduced graphene oxide (f‐rGO) via solution casting technique. The structural, morphological, optical, mechanical, and thermal properties of the composite PVC/f‐rGO gel were characterized by using various techniques. In order to test the usefulness of PVC/f‐rGO, we fabricated the new planar PVC/f‐rGO‐based gel actuator, by holding the gel between two electrodes. Stimuli‐responsive deformation of ~2.7 mm was measured at a voltage of 1200 V for an optimized gel in the designed device. The loading of silane‐f‐rGO greatly enhanced the stimulation, response time and flexibility of the PVC gel. The highest elongation at break of about 433% was achieved for f‐rGO‐loaded PVC gel owing to strong dipole–diploe interactions between alpha hydrogen of PVC and ‐NH2 of f‐rGO. We believe that highly electro‐stimulated, soft and, flexible PVC/f‐rGO gels could open a new avenue for the fabrication of advanced artificial muscles and tunable soft actuators for number of applications.
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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".