A Study of Nanoclay Reinforcement of Biocomposites Made by Liquid Composite Molding
Bibliographic record
Abstract
Liquid composite molding (LCM) processes are widely used to manufacture composite parts for the automotive industry. An appropriate selection of the materials and proper optimization of the manufacturing parameters are keys to produce parts with improved mechanical properties. This paper reports on a study of biobased composites reinforced with nanoclay particles. A soy-based unsaturated polyester resin was used as synthetic matrix, and glass and flax fiber fabrics were used as reinforcement. This paper aims to improve mechanical and flammability properties of reinforced composites by introducing nanoclay particles in the unsaturated polyester resin. Four different mixing techniques were investigated to improve the dispersion of nanoclay particles in the bioresin in order to obtain intercalated or exfoliated structures. An experimental study was carried out to define the adequate parameter combinations between vacuum pressure, filling time, and resin viscosity. Two manufacturing methods were investigated and compared: RTM and SCRIMP. Mechanical properties, such as flexural modulus and ultimate strength, were evaluated and compared for conventional glass fiber composites (GFC) and flax fiber biocomposites (GFBiores-C). Finally, smoke density analysis was performed to demonstrate the effects and advantages of using an environment-friendly resin combined with nanoclay particles.
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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) | 0.000 | 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".