A study on the effects of graphene nano-platelets (GnPs) sheet sizes from a few to hundred microns on the thermal, mechanical, and electrical properties of polypropylene (PP)/GnPs composites
Bibliographic record
Abstract
Polypropylene (PP) is incorporated with four different grades (H100, M25, M5, and C300) of graphene nanoplatelets (GnPs) via twin screw extrusion followed by injection moulding.The composites' thermal stability, crystallization behaviour, tensile strength, and electrical property are carefully examined.The thermal stability is significantly enhanced with the incorporation of small-sized GnPs as shown by the 11.2% improvement in T 5% (the temperature at which 5 wt% of the mass loss occurs) and 5.1% improvement in T max (the temperature at which the maximum loss rate occurs).The thermal stabilizing effect of fillers can be significantly enhanced when they are well distributed with less aggregation as is the case for small-sized GnPs.The GnPs show a considerable nucleating effect on PP by increasing the crystallization temperature (T c ).The greatest improvement in tensile property is achieved with the use of small-sized GnPs.A 33.0% enhancement in tensile strength and 59.1% improvement of tensile modulus are obtained with the use of C300 and M5, respectively.The significantly increased thermal stability and mechanical property with small-sized GnPs are due to the fact that these smallsized fillers achieve a high degree of dispersion with less agglomeration as shown in the scanning electron microscope (SEM) images.However, the fillers with a large sheet size are still beneficial for purposes concerning electrical conductivity since the lowest percolation is obtained with H100.The greater the size of the GnPs, the smaller the percolation threshold of composites is exhibited.
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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".