Quantum dot–metallic nanorod sensors via exciton–plasmon interaction
Bibliographic record
Abstract
We investigate quantum nanosensors based on hybrid systems consisting of semiconductor quantum dots and metallic nanorods in the near-infrared regime. These sensors can detect biological and chemical substances based on their impact on the coherent exciton-plasmon coupling and molecular resonances supported by such systems when they interact with a laser field. We demonstrate that the ultrahigh sensitivity of such molecular resonances on environmental conditions allows dramatic and nearly instantaneous changes in the total field experienced by the semiconductor quantum dot via minuscule variations of the local refractive indices of the quantum dot or nanorod. The proposed nanosensors can utilize quantum effects to control the sense (or direction) of the changes in the quantum dot emission, allowing us to have bistable switching from dark to bright states or vice versa via adsorption (or detachment) of biomolecules. These sensors can also offer detection of ultra-small variations in the local dielectric constant of the quantum dots or metallic nanorods via coherent induction of time delays in the effective field experienced by the quantum dots when the hybrid systems interact with time-dependent laser fields. This leads to unprecedented bulk refractive index sensitivities. Our results show that one can utilize quantum phase to control the coherent exciton-plasmon dynamics in these sensors such that introduction of a biomolecule can increase or decrease the time delay. These results offer novel ways to detect single biomolecules via application of quantum coherence to convert their impact into spectacular optical events.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".