Linear Electro‐Optic Modulation in Highly Polarizable Organic Perovskites
Bibliographic record
Abstract
Abstract Electrical‐to‐optical signal conversion is widely employed in information technology and is implemented using on‐chip optical modulators. State‐of‐the‐art modulator technologies are incompatible with silicon manufacturing techniques: inorganic nonlinear crystals such as LiNbO3 are integrated with silicon photonic chips only using complex approaches, and hybrid silicon–LiNbO3 optical modulators show either low bandwidth or high operating voltage. Organic perovskites are solution‐processed materials readily integrated with silicon photonics; and organic molecules embedded within the perovskite scaffold allow in principle for high polarizability. However, it is found that the large molecules required for high polarizability also require an increase of the size of the perovskite cavity: specifically, using the highly polarizable DR2+ (R = H, F, Cl) in the A site necessitates the exploration of new X‐site options. Only by introducing BF4– as the X‐site molecule is it possible to synthesize (DCl)(NH4)(BF4)3, a material exhibiting a linear EO coefficient of 20 pm V–1, which is 10 times higher than that of metal halide perovskites and is a 1.5 fold enhancement compared to reported organic perovskites. The EO response of the organic perovskite approaches that of LiNbO3 (reff ≈ 30 pm V–1) and highlights the promise of rationally designed organic perovskites for use in efficient EO modulators.
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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".