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 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) | 0.002 | 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".