Genome Regions Associated with Functional Performance of Soybean Stem Fibers in Polypropylene Thermoplastic Composites
Bibliographic record
Abstract
Plant fibers can be used to produce composite materials for automobile parts, thus reducing plastic used in their manufacture, overall vehicle weight and fuel consumption when they replace mineral fillers and glass fibers. Soybean stem residues are, potentially, significant sources of inexpensive, renewable and biodegradable natural fibers, but are not curretly used for biocomposite production due to the functional properties of their fibers in composites being unknown. The current study was initiated to investigate the effects of plant genotype on the performance characteristics of soybean stem fibers when incorporated into a polypropylene (PP) matrix using a selective phenotyping approach. Fibers from 50 lines of a recombinant inbred line population (169 RILs) grown in different environments were incorporated into PP at 20% (wt/wt) by extrusion. Test samples were injection molded and characterized for their mechanical properties. The performance of stem fibers in the composites was significantly affected by genotype and environment. Fibers from different genotypes had significantly different chemical compositions, thus composites prepared with these fibers displayed different physical properties. This study demonstrates that thermoplastic composites with soybean stem-derived fibers have mechanical properties that are equivalent or better than wheat straw fiber composites currently being used for manufacturing interior automotive parts. The addition of soybean stem residues improved flexural, tensile and impact properties of the composites. Furthermore, by linkage and in silico mapping we identified genomic regions to which quantitative trait loci (QTL) for compositional and functional properties of soybean stem fibers in thermoplastic composites, as well as genes for cell wall synthesis, were co-localized. These results may lead to the development of high value uses for soybean stem residue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".