Size Control of Mesoscale Aqueous Assemblies of Quantum Dots and Block Copolymers
Bibliographic record
Abstract
Dropwise addition of water to blend solutions of block copolymer-stabilized quantum dots (QDs) and amphiphilic block copolymer stabilizing chains PS(665)-b-PAA(68) (PS = polystyrene, PAA = poly(acrylic acid)) in DMF induces self-assembly to form photoluminescent mesoscale QD/block copolymer colloids in water termed QD compound micelles (QDCMs). Here we demonstrate reproducible kinetic control of QDCM particle size and chain stretching within the external PAA stabilizing layer via changes in the initial polymer concentration and rate of water addition. By increasing the initial polymer concentration or decreasing the rate of water addition for a constant blend composition, larger QDCM particles are obtained. From a combination of transmission electron microscopy and dynamic light scattering, the thickness of the external PAA layer is determined for various QDCM sizes, showing that PAA stretching in the external brush layer increases with increasing particle size, reaching the limit of fully extended chains for sufficiently large particles. The photoluminescence spectra from QDCMs in pure water indicate that photoluminescence properties of the block copolymer-stabilized QD building blocks are retained during self-assembly. The demonstrated control of mesoscale particle size and conformation of the stabilizing PAA layer, among other related structural parameters, via simple variation of experimental conditions is a promising step toward the application of QDCM assemblies in photonics and biolabeling.
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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".