Analyte-dependent Rabi splitting in solid-state plexcitonic sensors based on plasmonic nanoislands strongly coupled to J-aggregates
Bibliographic record
Abstract
A key challenge in the field of plexcitonic quantum devices is the fabrication of solid-state, device-friendly plexcitonic nanostructures using inexpensive and scalable techniques. Lithography-free, bottom-up nanofabrication methods have remained relatively unexplored within the context of plexcitonic coupling. In this work, a plexcitonic system consisting of thermally dewetted plasmonic gold nanoislands (AuNI) coated with a thin film of J-aggregates was investigated. Control over nanoisland size and morphology allowed for a range of plasmon resonances with variable detuning from the exciton. The extinction spectra of the hybrid AuNI/J-aggregate films display clear splitting into upper and lower hybrid resonances, while the dispersion curve shows anti-crossing behavior with an estimated Rabi splitting of 180 eV at zero detuning. As a proof of concept for quantum sensing, the AuNI/J-aggregate hybrid was demonstrated to behave as a plexcitonic sensor for hydrochloric acid vapor analyte. This work highlights the possibility of using thermally dewetted nanoparticles as a platform for high-quality, tunable, cost-effective, and scalable plexcitonic nanostructures for sensing devices and beyond.
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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) | 0.000 | 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".