Gold Nanoparticle/Polymer Nanocomposites: Dispersion of Nanoparticles as a Function of Capping Agent Molecular Weight and Grafting Density
Bibliographic record
Abstract
The dispersion of polymer-covered gold nanoparticles in high molecular weight (MW) polymer matrixes is reported. Complete particle dispersion was achieved for PS125-Au in the polystyrene (PS) matrixes studied (up to and including Mn = 80 000 g/mol). PS19-Au, on the other hand, exhibits complete dispersion in a low MW PS matrix (Mn = 2000 g/mol) but only partial dispersion in higher MW matrixes (up to 80 000 g/mol). Similarly, PEO45-Au is fully dispersed in a low MW poly(ethylene oxide) (PEO) matrix (Mn = 1000 g/mol) but only partially in a higher MW PEO matrix (Mn = 15 000 g/mol). Wetting of the polymer-Au brushes by the polymer matrix is associated with dispersibility. Theory predicts that, for dense polymer brushes, wetting is achieved when the MW of the polymer brush equals (and is greater than) that of the polymer matrix. The observed partial dispersion of the PS19-Au and PEO45-Au nanoparticles in matrixes whose MW is greater than the brush MW is attributable to the existence of a high volume fraction of voids within the brush. These voids arise from the unique geometry of the nanoparticle surface arising from the juxtaposed facets of the gold nanoparticle. PS125-Au brushes are wetted by PS matrixes whose degree of polymerization is larger than 125, probably because of their lower grafting density on the gold core or the high fraction of void volumes caused by the facets on the gold cores. Dispersion thus occurs when the matrix MW is greater than that of the brush.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".