Development and Characterization of PLA-Based Bio Composites
Bibliographic record
Abstract
Polymers make up an important component of the manufacturing industry. Their density and mechanical properties makes them desirable for many applications. Currently much polymer produced is from non-renewable sources which adds to waste after disposal. PLA is a bio-based polymer shown to be promising in many studies but its inherent brittleness prevents wide scale application of the polymer. One readily feasible alternative is to reinforce PLA with fillers as a means to improve mechanical properties. Two types of PLA based bio-composites were considered: PLA-Lignin & PLA-Tannin composites. PLA was the matrix in these composites with the fillers either being Lignin or Tannin. Composites containing 5, 10, and 15 wt % of the fillers were studied. The composites were fabricated by means of melt blending in a twin screw compounder followed by injection molding. SEM morphological, mechanical, and dynamic mechanical evaluations of the composites were performed. Tan delta values of both Lignin and Tannin based PLA composites increased with increasing addition of fillers with the exception of PLA with 5 wt% Tannin. Lignin appears to have a plasticizing effect on the initial tensile stiffness while tannin appears to have a stiffening effect on the initial tensile stiffness. Glass transition temperatures of all composites do not seem to change significantly from that of pure PLA.
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 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) | 0.001 | 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".