Impact of lignin filler on mechanical properties and anisotropic behavior of compression moulded glass/polypropylene long fibre thermoplastics
Bibliographic record
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 petroleum-based polymer composites. To evaluate the impact of the bio-filler (Kraft lignin) on the mechanical properties of glass-reinforced polypropylene (glass/PP) LFTs, specimens were extracted from 4 mm flat plaques manufactured through a compression moulding process. The study examined three different weight percentages of lignin: 0%, 14%, and 21% while keeping the glass fibre content constant at 30% by weight. Quasi-static tensile and shear tests were conducted to analyse the mechanical properties of the material. Additionally, in order to investigate anisotropy, samples were tested in two material orientations, namely, 0° and 90° material directions which will be precisely defined in this paper. Specimens from both the charge and flow regions were tested to assess their effect on the mechanical properties of the LFT material. The findings revealed that tensile strength decreased with increasing lignin content for the 0° orientation. Specifically, at 21% lignin content, the reduction in strength was notably higher at 36%, compared to just a 15% decrease observed with 14% lignin content. In the 90° material direction, a slight increase in tensile strength was noted for the 14% lignin content when compared to the 0% lignin condition. In the 0° orientation, strength consistently reached up to 100 MPa for samples from both the charge and flow regions, whereas the 90° orientation exhibited tensile strengths of up to 43 MPa. In the 0° orientation, samples from the flow region demonstrated higher tensile strength than those from the charge region. Conversely, in the 90° orientation, the charge region samples surpassed the flow region samples in strength by approximately 66% for the 0% lignin condition. The influence of the filler material on shear strength was found to be minimal for 14% lignin specimens and a slightly higher reduction of approximately 10% - 15% in the case of 21% lignin specimens. This study provides essential material characterisation, enabling more accurate numerical and analytical modelling of LFT composite materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 teacher head, 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".