Exploring the effect of lignin as a filler on the mechanical properties and anisotropic nature of Glass/Polypropylene LFTs manufactured via direct compounded compression moulding (LFT-D)
Bibliographic record
Abstract
ABSTRACT Glass Long Fibre Thermoplastics (LFTs) are becoming increasingly popular as lightweight, high-performance recyclable materials. This trend has also led to greater interest in the use of biomaterials, such as bio-fillers like lignin, to help reduce the carbon footprint of these petroleum-based polymer composites. This study investigated the influence of lignin bio-filler on the mechanical properties of glass-reinforced polypropylene (glass/PP) LFTs. Three lignin weight percentages (0%, 14%, and 21%) were evaluated while maintaining 30% glass fibre content. Tensile and shear tests were conducted on samples from both charge and flow regions of compression-moulded plaques, considering 0°, +45°, -45° and 90° material directions. Fracture surface analysis was conducted by utilising a Scanning Electron Microscope (SEM) to understand the failure mechanisms and structural behaviour under mechanical stress. The results indicated a decrease of up to 36% in tensile strength with increasing lignin content in the 0° direction, which was particularly significant at a lignin content of 21%. The 0° direction consistently exhibited higher tensile strength in the range of 70 MPa to 100 MPa than the 90° material directions followed by +45°, -45°. Shear strength remained largely unaffected for 14% lignin content, with an approximately 10% drop for 21% lignin content samples. SEM analysis revealed distinct failure mechanisms across the different material directions (0°, +45°, -45°, and 90°). This study provides essential material characterisation, enabling more accurate numerical and analytical modelling of these materials with a lightweight, low-cost filler.
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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".