Nonlithographic Hierarchical Patterning of Semiconducting Nanoparticles via Polymer/Polymer Phase Separation
Bibliographic record
Abstract
We demonstrate a new and versatile nonlithographic strategy for the lateral patterning of semiconducting quantum dots (QDs) in thin polymer films via phase separation between two immiscible polymer components, one of which is attached as a stabilizing layer to the QD surface. Simple spin-casting of blend solutions containing block copolymer-stabilized cadmium sulfide (CdS) QDs with an external polystyrene (PS) brush layer (PS−CdS) and poly(methyl methacrylate) (PMMA) homopolymer results in solvent evaporation-induced organization of QDs on two disparate length scales: polymer/polymer spinodal decomposition gives rise to tunable microscopic lateral patterning of PS−CdS features (with mesoscopic feature heights), while simultaneous steric interactions between the polymer brush layers on neighboring nanoparticles give rise to a nanoscale liquidlike distribution of QDs within the PS−CdS domains. The correlation length and morphology of the lateral patterns are easily controlled via the spin-casting rotation speed and blend composition, respectively. Subsequent selective removal of the PMMA component allows the production of various photoluminescent PS−CdS features with structural hierarchy on glass substrates, including cellular and wirelike networks and arrays of spatially correlated islands.
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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".