Renewable resources-based PTT [poly(trimethylene terephthalate)]/switchgrass fiber composites: The effect of compatibilization
Bibliographic record
Abstract
In this research, switchgrass (SG) fiber-reinforced poly(trimethylene terephthalate) (PTT) biocomposites were prepared by extrusion followed by injection molding machine. The methylene-diphenyl-diisocyanate-polybutadiene (MDIPB) prepolymer was used to enhance the impact strength of the biocomposites. In addition, the polymeric methylene-diphenyl-diisocyanate (PMDI) compatibilizer was used to enhance the mechanical properties of the composites. The effect of compatibilizer on mechanical, crystallization melting, thermomechanical, melt flow index (MFI), morphological, and thermal stability properties of the composites was studied. Thermomechanical properties of the biocomposites were studied by dynamic mechanical analysis (DMA). Scanning electron microscopy (SEM) was used to observe the interfacial adhesion between the fiber and matrix. The results showed that MDIPB and PMDI have a significant effect on the mechanical properties of the composites. The impact strength of MDIPB- and PMDI-compatibilized composites was increased by 87 % when compared to the uncompatibilized composite.
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 | 0.000 | 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".