An in-depth analysis of the physico-mechanical properties imparted by agricultural fibers and food processing residues in polypropylene biocomposites
Bibliographic record
Abstract
The use of agricultural and food processing residues as potential reinforcements in plastics has been extensively studied. However, there is a large variation in the mechanical performance of agricultural fiber-based biocomposites due to different processing materials and parameters. An in-depth comparison of the resulting effect of the agricultural filler on the matrix is often not possible given the discrepancy in processing conditions. This study seeks to determine the intrinsic properties of agricultural fibers and food processing residues for their use in polypropylene biocomposites based on a standardization of experimental design. The effect of 25wt% loading of miscanthus, fall-and spring-harvest switchgrass, wheat straw, oat hull, soy hull, soy stalk, hemp and flax on the physico-mechanical properties of polypropylene biocomposites was investigated. The addition of fiber led to an improvement in flexural strength, flexural modulus, and tensile modulus, and a general decrease in tensile strength at yield, elongation at break and Izod impact strength. Scanning electron microscopy highlighted the interfacial adhesion, orientation and distribution of the fibers within the matrix, confirming that fiber length and dispersion within the matrix are positively correlated with mechanical properties. The crystallization of the polypropylene phase and a compositional analysis of the agricultural fibers and processing residues were also compared to offer insight into the effect of the filler’s intrinsic properties on the resulting material performance.
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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".