Structural and Electromechanical Insights into Thermoplastic Polyurethane/3D Hybrid Carbon Nanocomposites for Strain Sensor Applications
Bibliographic record
Abstract
Incorporation of carbon allotropes of different dimensions within elastomeric matrices has been established as an effective strategy to fabricate functional conductive polymer nanocomposites (PNCs). In this work, higher-dimensional 3D hybrid carbon nanofillers, comprising synergistically integrated multiwalled carbon nanotubes immobilized onto few-layer graphene, were incorporated into the thermoplastic polyurethane (TPU) matrix to demonstrate their effectiveness as strain sensors. The conductive films were fabricated through a simple solution casting technique, in which the mechanical, electrical, and strain-sensing characteristics were studied in view of filler distribution, structural confinement, and interfacial interactions. Analyses using wide-angle X-ray scattering, Raman spectroscopy, and tensile testing revealed a higher degree of filler reinforcement within the TPU moieties, indicating pronounced interfacial interactions. Further, the tensile modulus increased significantly with filler loading above its percolation threshold (363% for 20 wt % loading). The structural features of dispersed filler aggregates were explored through an iterative model fitting of the ultra-small-angle X-ray scattering (USAXS) data, along with scanning electron microscopy (SEM). As a strain sensor, the films displayed a superior working-strain Gauge Factor (GF = 123, up to 8%), with exceptional stability under both unidirectional and cyclic strain. The findings provide a fundamental understanding while validating the potential of hybrid carbonaceous fillers for the fabrication of PNCs with futuristic 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".