The Influence of Excess Coupling Agent on the Microdeformation Processes and Mechanical Properties of Poly(propylene)/Wood‐Flour Composites
Bibliographic record
Abstract
Abstract Summary: It is well known that coupling agents improve the adhesion between poly(propylene) and wood fillers leading to an increase of the composite mechanical properties. Above a certain concentration limit of the coupling agent, however, deterioration of the mechanical properties often occurs, and little attention has been directed so far towards finding out the reasons for that behavior. Transmission electron microscopy and high‐voltage electron microscopy were used in this work to study the morphology and microdeformation behavior of PP/wood‐flour composites modified with high amounts of maleated poly(propylene) as a coupling agent. It was found that the coupling agent containing a higher concentration of grafted maleic anhydride forms a separate phase in the poly(propylene) matrix, influencing the mechanical properties of the composites. HVEM micrograph of the deformation structure of wood‐filled poly(propylene) composite showing fibrils stretching between the coupling agent domains and the PP matrix. magnified image HVEM micrograph of the deformation structure of wood‐filled poly(propylene) composite showing fibrils stretching between the coupling agent domains and the PP matrix.
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".