Multiple Emission Peaks Challenge Polariton Condensation in Phenethylammonium-Based 2D Perovskite Microcavities
Bibliographic record
Abstract
Two-dimensional metal halide phases, commonly known as 2D perovskites, have emerged as promising materials for exciton-polaritons, particularly for polariton condensation. This process entails the spontaneous accumulation of population in the polariton ground state and relies on efficient energy relaxation. In this class of materials, this relaxation is mediated by exciton reservoir emission, which pumps polariton states through radiative pumping. To achieve strong light-matter coupling and sustain a high polariton density, the material must possess excitations with large oscillator strength and high exciton binding energy. While 2D perovskites exhibit these desirable characteristics, there are no reports of room-temperature polariton condensation and only one successful demonstration at cryogenic temperatures. In this work, we systematically explore the role of energy alignment between the exciton reservoir emission and the lower polariton branch in populating the polariton ground state via radiative pumping. Through cavity detuning, we shift the lower polariton energy minimum to overlap with the emission of the exciton reservoir at different energies. We identify that the multiple radiative pathways of 2D perovskites lead to inefficient radiative pumping of the lower polariton branch at the lowest-energy state, ultimately posing challenges for polariton condensation in this class of 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".