Poly(ethylene oxide) grafted silica nanoparticles: efficient routes of synthesis with associated colloidal stability
Bibliographic record
Abstract
In this study, poly(ethylene oxide) monomethyl ether (MPEO) of molecular weight of 5000, 10 000, and 20 000 g mol-1 were grafted onto colloidal silica nanoparticles (NPs) of a 27.6 nm diameter using two distinct "grafting to" processes. The first method was based on the coupling reaction of epoxide-end capped MPEO with amine-functionalized silica NPs, while the second method was based on the condensation of triethoxysilane-terminated MPEO onto the unmodified silica NPs. The influence of PEO molecular weight, grafting process and grafting conditions (temperature, reactant concentration, reaction time) on the PEO grafting density was fully investigated. Thermogravimetric analysis (TGA) was used to determine the grafting density which ranged from 0.12 chains per nm2 using the first approach to 1.02 chains per nm2 when using the second approach. 29Si CP/MAS NMR characterization indirectly revealed that above a grafting density value of 0.3 PEO chains per nm2, a dendri-graft PEO network was built around the silica surface which was composed of PEO chains directly anchored to the silica surface and those grafted to silica NPs by intermediate of >CH-O-Si- bonds. The colloidal stability of the particles during different steps of the grafting process was characterized by small-angle X-ray scattering (SAXS). We have found that the colloidal systems are stable whatever the achieved grafting density due to the strong repulsions between the NPs, with the strength of repulsion increasing with the molecular weight of the grafted MPEO chains.
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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".