Thermoset-natural fiber composites using nanocomposites for mass transit applications
Bibliographic record
Abstract
Mass transit vehicle weight has grown significantly over the last 20 years penalizing the road infrastructure, fuel consumption and environmental signature. Increased use of lightweight, high-strength and new sustainable composites for mass transit vehicle components is a promising avenue to explore since weight reduction is one of the few ways Canadian mass transit vehicle manufacturers can influence fuel consumption and hence help reduce greenhouse gases emissions. Weight saving opportunities could possibly be obtained by the use of natural fiber and hybrid composites as compared to “traditional” glass fiber based composites. The use of nanocomposites to improve the property of resins without any significant increase in density could also be a possible avenue to obtain the high performance composite with weight saving. In this paper, the role of nanoparticles on the performance improvement of natural fibers thermoset composites was investigated. Different nanoparticles such as Cloisite Na+, 10A, 20A and 30B, Perkalite G100, Nanocryl C140 were first dispersed in a thermoset resin before performing the impregnation of the natural fiber fabric. Natural fiber composites were prepared by hand lay-up followed by a vacuum step and by infusion. The quality of dispersion and intercalation/exfoliation was analyzed by X-ray diffraction (XRD), field emission gun scanning electron microscopy (FEGSEM) and transmission electron microscopy (TEM). Mechanical performance and fire resistance of nanoreinforced thermoset resin and natural fiber composites were also evaluated. The results obtained demonstrate that the presence of nanoparticles can improve the the tensile strength, tensile modulus and fire resistance performance of the resin and of the natural fiber composites. The levels of improvement of the properties were seen to vary with the type of nanoparticles used.
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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".