Morphology and properties of highly filled iPP/TiO <sub>2</sub> nanocomposites
Bibliographic record
Abstract
Nanocomposites based on isotactic polypropylene (iPP) and titanium dioxide (TiO 2 ) nanoparticle containing 1–15 vol% (4.6–45.5 wt%) of the nanoparticle were prepared by the melt blending process. The effect of an anhydride‐modified polypropylene as a compatibilizer on dispersion of TiO 2 nanoparticles was assessed using SEM. TGA and DSC analysis were performed to study the thermal properties of the nanocomposites. Crystalline structures of iPP in the presence of TiO 2 were analyzed by XRD. Mechanical properties of the nanoparticles were measured and a micromechanical analysis was applied to quantify interface interaction between the polymer and particle. SEM results revealed improvement of TiO 2 particle dispersion by adding the compatibilizer. It was shown that the thermal stability and crystalline structure of the nanocomposite are significantly affected by the state of particle dispersion. TiO 2 nanoparticles were shown to be strong β‐nucleating agents for iPP, especially at concentrations less than 5 vol%. Presence of the β‐structure crystals reduced the elastic modulus and yield strength of the nanocomposites. Micromechanical analysis showed enhanced interaction between organic and inorganic phases of the compatibilized nanocomposites. POLYM. ENG. SCI., 54:874–886, 2014. © 2013 Society of Plastics Engineers
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".