Monolayer Arrays of Au Nanoparticles on Block Copolymer Brush Films for Optical Devices and Biosensors
Bibliographic record
Abstract
Gold nanoparticle (AuNP) monolayers possess unique optical properties and are widely used in optical devices and biosensors, sometimes necessitating dense but nonclose-packed monolayers. Current self-assembly methods are generally limited to small surfaces and are often plagued by AuNP aggregation. Here, we use a facile method, suitable for substrates of any size or form, to produce dense, unaggregated, randomly packed AuNP monolayers on brushlike films of polystyrene- block -poly(4-vinylpyridine) (PS-P4VP) obtained by dip-coating flat surfaces from very dilute solutions, where PS forms the brush, and the citrate-stabilized AuNPs interact with the P4VP anchoring layer when the template is incubated in an AuNP colloid. By investigating the effect of molecular weight ( M n ), dip-coating solvent, and colloid pH on the characteristics of the adsorbed films and the subsequent deposition of AuNPs, we constructed a morphology map in terms of PS M n and chain grafting density under favorable pH conditions, showing the region that yields the desired dense well-dispersed AuNP monolayers. Above an upper PS M n boundary (around 50–60 kg/mol), the PS brush─continuous or in the form of patches─is too thick and rigid for the AuNPs to access the underlying P4VP and is also subject to kinetic effects, thus causing little or nonuniform, even aggregated, AuNP deposition. These findings have important implications for the future employment of dense, unaggregated AuNP monolayers in miniature sensors and as optical coatings in a wide range of optical devices.
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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 | 0.001 |
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".