Production and characterization of UHMWPE/fumed silica nanocomposites
Bibliographic record
Abstract
Abstract Ultrahigh‐molecular‐weight polyethylene (UHMWPE)/fumed silica nanocomposites were prepared via in situ polymerization using a bi‐supported Ziegler‐Natta catalytic system. Nanocomposites with different nanoparticle weight fractions were produced in order to investigate the effect of fumed silica on thermal and mechanical properties of UHMWPE/fumed silica nanocomposites. The viscosity average molecular weight (M) of all samples including pure UHMWPE as the reference sample and nanocomposites were measured. Scanning electron microscope (SEM) images showed the homogenous dispersion of nanoparticles throughout the UHMWPE matrix while no nanoparticle cluster has been formed. Crystallization behavior of nanocomposites was investigated by differential scanning calorimetry (DSC), which showed a slight increase in melting temperature by enhancing the nanoparticle concentration while no significant change was observed in the crystallization temperature as the fumed silica concentration enhanced. The improvement in all thermal stability parameters was recorded by thermogravimetric analysis (TGA). Besides, via tensile testing, it was confirmed that addition of nanoparticles caused considerable improvement in such mechanical properties as Young's modulus, yield stress, and tensile strength of samples while the elongation at break declined by addition of more nanoparticles. POLYM. COMPOS., 2012. © 2012 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) | 0.000 | 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".