Carbon nanotube network evolution during deformation of PVDF-MWNT nanocomposites
Bibliographic record
Abstract
The emergence of novel electronic systems and their requirements have necessitated the evolution of new material classes. The traditional electronic semiconductors and components are shifting from silicon based substrates to polymers and other organic compounds. Sensor components are no exceptions, where compliant polymeric materials offer the possibility of flexible electronics. This paper examines the fabrication and characterization of piezoresistive nanocomposites for pressure sensing applications. The matrix material employed was Polyvinylidene Fluoride (PVDF). The PVDF phase was reinforced with conductive particles, in order to form a conductive filler network throughout the nanocomposite. Multiwall carbon nanotubes (MWNT) were selected as conductive particles to form the networks. The composites were prepared by melt mixing the PVDF and conductive particles in compositions ranging from 0.25 to 10 wt% conductive particle in PVDF. The dielectric permittivity and electrical conductivity of the composites was characterized and the electrical percolation behavior of PVDF nanocomposites fitted to the statistical percolation model. Scanning electron was employed to understand the morphology of the filler networks in the PVDF nanocomposites. Quasi-static piezoresistance of the nanocomposites was characterized using a custom-built force-resistance measurement setup under compressive loading 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 | 0.000 | 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".