Hybrid composites with engineered polysaccharides for automotive lightweight
Bibliographic record
Abstract
In this study, the objective was to develop hybrid composites combining the semi-crystalline engineered polysaccharide α-1,3-glucan (Nuvolve™) with typical long glass fiber in a polypropylene matrix to optimize specific indicators of performance while also considering environmental attributes. Morphological analyses were conducted in conjunction with the evaluation of mechanical performance of these hybrid composites to gain further understanding of filler-matrix interaction. Optimum loadings of the polysaccharide / glass fiber system were identified as promising alternative to the current glass fiber / polypropylene incumbent material utilized in many commercial application (e.g. by Ford Motor Company for body interior and under-the-hood applications). Formulations with 10/15 (e.g. 10 wt.% polysaccharide and 15 wt.% glass fiber) or 10/20 showed an overall increase of >100% with respect to modulus, strength and impact properties while also demonstrating a density reduction of up to 13%. Interestingly, the life cycle analysis showed that addition of only 10 wt.% polysaccharide was able to save 4,720 liters of fuel for every ton of polysaccharide used in vehicles. Therefore, hybrid reinforced thermoplastic composites offer a balance of engineering and environmental performance to exceed materials in use. These new composites are commercially viable while also advancing the environmental stewardship and eco-efficiency within the automotive industry.
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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".