Dispersibility of Poly(vinyl acetate) Modified Silica Nanoparticles in Carbon Dioxide with Several Cosolvents
Bibliographic record
Abstract
The dispersibility and stabilization of silica nanoparticles with surface-capped poly(vinyl acetate) (PVAc) chains are examined in carbon dioxide with four different cosolvents. Three surface coverages of silica-PVAc were formed by using different weight ratios of the silica and PVAc. The dispersibilities of three silica-PVAc nanoparticles in CO 2 with the four cosolvents were tested in a rotatable high-pressure variable-volume view cell. The effects of surface coverage, cosolvent type, pressure, and particle concentration on dispersion were investigated. Results show that, in the experimental pressure range (5.5 to 20 MPa), the pressure has no significant effect on the dispersion of nanoparticles, and the cosolvent is the key factor in dispersing silica-PVAc particles in CO 2 . 1-Butanol is an adequate cosolvent to disperse silica-PVAc in CO 2 with any coverage of PVAc on the surface of the particles when the concentration of particles is smaller than 0.31 wt %. Ethanol can only improve the dispersibility of particles with a high surface coverage of PVAc when the concentration of particles is smaller than 0.14 wt %. 1-Hexanol and ethyl acetate cannot disperse the particles in CO 2 with any coverage of PVAc. Molecular dynamics simulations were carried out to study the nanoparticle-CO 2 -cosolvent dispersions. Results suggest that 1-butanol has a good solubility in the CO 2 condensed phase and can effectively absorb onto the nanoparticle surface, which help to prevent the formation of nanoparticle aggregation. The precipitation of nanoparticles in the nanoparticle/1-hexanol/CO 2 and nanoparticle/ethyl acetate/CO 2 systems is attributed to the relatively low solubility of CO 2 in 1-hexanol and ethyl acetate. The precipitation of nanoparticles in the nanoparticle/ethanol/CO 2 system is the result of less hindrance of ethanol molecules to the aggregation of nanoparticles.
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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".