Evaluation of the Mechanical Properties of Injection Moulded Hemp Fibre Reinforced Polypropylene Composites
Bibliographic record
Abstract
In recent years, industrial hemp fibre reinforced thermoplastic composites have attracted substantial interest as potential structural materials. These composites have been subject to intense study for use in lightweight, recyclable and low cost applications. The aim of this research was to improve and evaluate the composite tensile strength and fibre/matrix interfacial adhesion by means of fibre treatment and addition of a coupling agent. Hemp fibre was digested in a small pressure vessel with a solution of 5wt% NaOH / 2wt% Na2SO3. Single fibre tensile tests were performed on treated and untreated fibres, and it was found that the alkali treatment resulted in an increase in fibre strength and an improvement in fibre separation. Composites containing either treated or untreated fibre, polypropylene and a maleic anhydride modified polypropylene (MAPP) coupling agent were then compounded in a twin-screw extruder and injection moulded into tensile test specimens. Tensile tests revealed that significant improvements in composite strength were made by using treated fibre and MAPP. The effect of MAPP on the interface of treated hemp fibre/polypropylene composites was assessed by means of the single fibre fragmentation test, and the interfacial shear strength was determined thereafter.
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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".