Simple and Versatile Platforms for Manipulating Light with Matter: Strong Light–Matter Coupling in Fully Solution‐Processed Optical Microcavities
Bibliographic record
Abstract
Planar microcavities with strong light-matter coupling, monolithically processed fully from solution, consisting of two polymer-based distributed Bragg reflectors (DBRs) comprising alternating layers of a high-refractive-index titanium oxide hydrate/poly(vinyl alcohol) hybrid material and a low-refractive-index fluorinated polymer are presented. The DBRs enclose a perylene diimide derivative (b-PDI-1) film positioned at the antinode of the optical mode. Strong light-matter coupling is achieved in these structures at the target excitation of the b-PDI-1. Indeed, the energy-dispersion relation (energy vs in-plane wavevector or output angle) in reflectance and the group delay of transmitted light in the microcavities show a clear anti-crossing-an energy gap between two distinct exciton-polariton dispersion branches. The agreement between classical electrodynamic simulations of the microcavity response and the experimental data demonstrates that the entire microcavity stack can be controllably produced as designed. Promisingly, the refractive index of the inorganic/organic hybrid layers used in the microcavity DBRs can be precisely manipulated between values of 1.50 to 2.10. Hence, microcavities with a wide spectral range of optical modes might be designed and produced with straightforward coating methodologies, enabling fine-tuning of the energy and lifetime of the microcavities' optical modes to harness strong light-matter coupling in a wide variety of solution processable active materials.
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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 | 0.001 |
| 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".