Hybrid poly(ethylene terephthalate)/silica nanocomposites prepared by in‐situ polymerization
Bibliographic record
Abstract
Abstract The nanocomposites, based on hybrid poly(ethylene terephthalate) (PET)/silica nanoparticles, were prepared via in‐situ condensation polymerization of terephthalic acid and ethylene glycol in the presence of silica nanoparticles pretreated with a silane coupling agent. Such a polymerization process ensured that the silica nanoparticles were well dispersed in PET matrix with the size ranging from 40 to 60 nm, which was confirmed by transmission electron microscope (TEM) observation. Attributed to the unique bonding between SiO2 nanoparticle and PET, the crystallization behavior of PET was improved significantly, at a low temperature in particular. To further explore the effects of silica nanoparticles on crystallization, extensive differential scanning calorimeter (DSC) measurements were performed in an attempt to reveal the impact of the morphology of the dispersed silica nanoparticle (i.e., sphere or gel‐like) on the peak temperature during melting as well as the amount of heat involved in crystallization. The influences of the structure of polyether glycol (PEG) used for PET preparation as well as the addition of glass fibres (GF) were also investigated using DSC. It was concluded that the synergy among silica nanoparticles, modified PEG, and GFs lowers both Tg and Tm of PET, thus facilitating the injection processes in application. POLYM. COMPOS. 28:42–46, 2007. © 2007 Society of Plastics Engineers
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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".