Effect of Nano-silica Content on Properties of Waterborne Polyurethane Composites
Bibliographic record
Abstract
We have prepared waterborne polyurethane(WPU) nano-SiO2 composites by in situ polymerization,during which modified silica sol was first mixed with the N-methyl-pyrolidone(NMP),and then polyme-rization was carried out with the addition of isophorone diiocyanate(IPDI),polycaprolactone(PCL) dimethylol-propionic acid(DMPA) and dibutyltin dilaurate.Effects of nano-silica content on the thermal and mechanical properties of WPU/nano-silica composites were investigated by thermogravimetric analysis(TGA),differential scanning calorimetry(DSC),transmittance electron microscopy(TEM) and dynamic mechanical analysis(DMA).The experiment results show that the nano-silica particles were well dispersed in the WBPU solution when the silica content was lower than 3.0%,a wider Tg gap and higher microphase separation degree between soft and hard segment were observed for WPUNS,and the thermal degradation temperature of the composite was higher than that of a pure WPU at temperature over 50 ℃.Besides,as the nano-silica content was increased from 0 to 0.5%,the elongation at break and water swell were decreased from 550% to 430% and 16.8% to 4.5%,respectively,while,the tensile strength was increased from 3.4 MPa to 5.95 MPa.As the nano-silica content was increased to 2.5%,the elongation at break and water swell decreased to 210% and 2.3%,and the tensile strength increased to 7.58 MPa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".