Plasmonic electromagnetic-induced transparency from double quantum dot–metal nanoparticle structure
Bibliographic record
Abstract
This work studies the energy absorption rates in the case of plasmonic electromagnetically induced transparency (PEIT) from the double quantum dot (DQD)–metal nanoparticle (MNP) system. The orthogonalized plane wave between the wetting layer (WL) and QD transitions is considered. PEIT is not attained under a single applied field. It is obtained under a probe, pump, and two tunneling components. Two asymmetric PEIT windows can result from the transitions between the sixth energy states of the DQD structure. A huge Qtotal value (10−5 W) under 0.2 W/cm2 is obtained. Compared with other works, this result is higher by four orders, and the power applied is less by six orders than that used in earlier works. Our results exceed those obtained with double MNP-QD, QD-six MNP, and MNP-ten QDs. Such a vast result is ascribed to the manipulation flexibility in the DQD system, which is not found in all other structures. This possibility increases quantum correlation and the nonlinear optical properties of the DQD and the DQD–MNP system. The QD ( QQD) and MNP ( QMNP) absorption energy rates are also studied. Applying two tunneling components restores the system to peak at resonance due to the coupled work of the two dots as a DQD system. Strong plasmon–exciton coupling occurs when the probe power becomes considerable. Thus, the probe must not be so small.
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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".