Bibliographic record
Abstract
Abstract Biocomposites from renewable resources have been attracting increasing attention over the last two decades, mainly for two major reasons: first, environmental concerns, and second, the realization that our petroleum resources are finite. Nowadays, biofiber‐reinforced composites are seen as potential materials for many engineering applications. Unfortunately, there are still issues that limit their future applications including long‐term performance and processing variability. This article gives an overview of the wide variety of biocomposite processing techniques as well as the factors (moisture content, fiber type, content as well as coupling agents, and their influence on composites properties) that affect the processes. Before processing biocomposites, semifinished product manufacturing is also a vital part, which is illustrated. Processing technologies for biofiber‐reinforced composites are discussed on the basis of the thermoplastic matrix (compression molding, extrusion, injection molding, LFT‐D method, and thermoforming) and thermosets (resin transfer molding, sheet molding compound) as well as other implemented processes, that is, thermoset compression molding and pultrusion. Furthermore, we include comparative studies between different processes regarding the biocomposites' structure–property relationships.
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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".