Directed Self‐Assembly – A Controllable Route to Optical and Electronic Devices Based on Single Nanostructures
Bibliographic record
Abstract
Self-assembled semiconductor quantum dots (QDs) offer an attractive route to electronic structure control. Size, shape, composition and strain can all be used to tune the electronic structure of individual dots. By the very nature of the self-assembled growth process, the characteristics of individual dots can vary widely and their spatial location is uncontrolled. This chapter shows how these limitations may be overcome, and presents examples in which control structures, such as optical microcavities or electrostatic gates, are constructed around individual QDs in an effort to determine their coupling to the optical field or to tune their electronic structure. Using such techniques one can engineer the symmetries of individual dots, introduce optical transitions that were previously forbidden, and facilitate the construction of devices for the emission of single and entangled photon pairs. The chapter discusses the application of directed self-assembly techniques to the production of single, site-selected InAs/InP QDs. Controlled Vocabulary Terms Microcavities; nanostructured materials; optical devices; semiconductor quantum dots
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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) | 0.002 | 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".