Recycled fibres and fibrous sludge as reinforcement materials in injection moulded polypropylene (PP) and poly(lactic acid) (PLA) composites
Bibliographic record
Abstract
Wood flour or sawdust is often used as filler in conventional wood plastic composite (WPC) materials. However, there has been an increasing interest to the use of wood pulp fibres in reinforced plastic applications, because they can provide enhanced strength properties and better biodegradability characteristics for the composite. This research compares the effect of recycled fibres or side streams of paper as reinforcement in poly(lactic acid) (PLA) or polypropylene (PP) composites. Fibres from liquid packaging board and non-deinked old newspapers, and fibrous sludge from recycling processes are compared with virgin softwood kraft pulp fibres. Composites were produced by melt processing to a fibre content of 30% (or 10% fibrous sludge), and the mechanical properties were investigated. Recycled fibres provided comparable, or even higher, plastic reinforcement than virgin softwood fibres. In PP composites, the differences in mechanical properties between different fibre types were relatively small. Fibrous sludge decreased the mechanical performance of composites but can be considered as cheap filler in cases when mechanical properties are not crucial. The possibility to use low-cost materials like recovered paper or deinking sludge in wood plastic composites is an interesting option for future sustainable applications.
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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 | 0.001 | 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".