Enhancement of second harmonic generations in plasmonic nanohybrids
Bibliographic record
Abstract
We have developed a theory of the second harmonic generation (SHG) for plasmonic nanohybrids made of an ensemble of metallic nanoshells and quantum dots. The surface plasmon polaritons (SPPs) for the metallic nanoshell and the dipole–dipole interaction (DDI) between metallic nanoshells are calculated. The Maxwell’s coupled-mode theory and quantum mechanical density matrix method are used to obtain analytical expressions for the SHG intensity in the presence of the SPP and DDI fields. These analytical expressions can be useful for scientists to compare their experiments and make new plasmonic devices. We predict that there is a huge enhancement in the SHG intensity due to an extra contribution to the SHG mechanism from SPP and DDI polaritons present in the nanohybrid. We found that there are two contributions to the SHG. The first is called the photonic SHG induced by the probe field. The second is called the polaritonic SHG produced by the SPP and DDI fields. Further, we predict that in the presence of the DDI coupling, a peak in the SHG spectrum splits into two peaks. In other words, the SHG peaks split into two SHG peaks due to the DDI coupling. The physics of the splitting is due to the formation of the dressed states due to strong coupling between the DDI field and quantum dots. Additionally, we found that the disappearance of the SHG peak due to the DDI is related to the electromagnetically induced transparency phenomenon. This finding can be used to fabricate nanoswitches (i.e., ON=one peak and OFF=two peaks) and nanosensors using plasmonic nanohybrids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".