Sviluppo di materiale composito Graphene/poliestere dissipativo di cariche elettrostatiche per applicazioni in ambienti corrosivi.
Bibliographic record
Abstract
This project has led to the development of a new industrial-aimed thermosetting nanocomposite, capable of electrostatic dissipation while guaranteeing the mechanical properties distinguishing of a glass fiber reinforced composite. \nThe matrix consists in a polyester resin, industrially formulated for glass fiber reinforced composites; commercially available graphene nanoplatelets are being used as the fillers, provided by NanoXplore, Canada; both unreinforced and \nglass fiber reinforced configurations of the composite has been made. \nThe production process has been chosen accordingly to the scalability need. Filler dispersion has been obtained through high shear mixing and 6 weight concentration were used, namely 0%, 1%, 3%, 5% ,7% and 10%. Samples have been produced by compression molding with previous manual lay-up preparation of the sample; specimens for the tests were cut directly from the plate sample with the help of a table saw and subsequent sandpaper refining. Electrical characterization has identified a percolation threshold in the range 3÷5 wt%, showing an increase in conductivity of over 7 orders of magnitude; cross plane conductivity is as high as 10-4 [S/cm] for the most conductive sample, with in-plane and cross-section conductivities consistently lower by an order of magnitude. The significant increase of permittivity values in the percolated samples suggests a possible suitability for EMI shielding due to absorption mechanism. Tests on mechanical behavior didn't show any clear trend in relation to fillers content, with variations between the samples shown through all the concentration range; overall, the presence of nanofillers don’t seem to significantly affect flexural and strength properties. Finally, the developed nanocomposite met the objective regarding surface and volume conductivity for electrostatic dissipation while maintaining the mechanical properties of the neat reinforced composite.
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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) | 0.003 | 0.001 |
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".