<i>In situ</i> emulsion polymerization and characterization of PVAc nanocomposites including colloidal silica nanoparticles for wood specimens bonding
Bibliographic record
Abstract
ABSTRACT Polyvinyl acetate (PVAc) nanocomposites for wood adhesives containing different amounts of colloidal silica nanoparticles (CSNs) were synthesized via in situ one‐step emulsion polymerization. The adhesion strength of wood specimens bonded by PVAc nanocomposites was investigated by the tensile test. Thermal properties of PVAc nanocomposites were also characterized by differential scanning calorimetry and thermogravimetric analysis. Rheological and morphological properties of the PVAc nanocomposites were investigated using rheometric mechanical spectrometry and field emission scanning electron microscopy (FESEM), respectively. The obtaining results showed that the shear strength of PVAc nanocomposite including 1 wt. % CSNs has the highest shear and tensile strength about 4.7 and 3.2 MPa, respectively. A small increment of Tg (~3 °C) and considerable increment of the ash content proved the enhancement of PVAc thermal characterization in the presence of CSNs. FESEM results showed uniform dispersion of nanoparticles throughout the PVAc matrix due to using the in situ emulsion polymerization process. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48570.
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".