DNA origami directed nanometer-scale integration of colloidal quantum emitters with silicon photonics
Bibliographic record
Abstract
Abstract Incorporation of colloidal quantum emitters into silicon-based photonic devices would enable major advances in quantum optics. However, deterministic placement of individual sub-10 nm colloidal particles onto micron-sized photonic structures with nanometer-scale precision remains an outstanding challenge. Here, we introduce Cavity-Shape Modulated Origami Placement (CSMOP) that leverages the structural programmability of DNA origami to precisely deposit colloidal nanomaterials within lithographically-defined resist cavities. CSMOP enables clean and accurate patterning of origami templates onto photonic chips with high yields. Soft-silicification-passivation stabilizes deposited origamis, while preserving their binding sites to attach and align colloidal quantum rods (QRs) to control their nanoscale positions and emission polarization. We demonstrate QR integration with photonic device structures including waveguides, micro-ring resonators, and bullseye photonic cavities. CSMOP therefore offers a general platform for the integration of colloidal quantum materials into photonic circuits, with broad potential to empower quantum science and technology.
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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".