Carbon–Nitrogen Axial Chirality as a Novel Chiral Framework Design Strategy for Circularly Polarized Luminescence Materials
Bibliographic record
Abstract
ABSTRACT Circularly polarized luminescence (CPL) materials are essential for advanced optoelectronic applications, yet efficient chiral design strategies remain challenging. Axial chirality has been widely employed in the construction of CPL materials due to its unique rigid structure. However, the focus has been primarily on the derivatives of carbon–carbon axial chirality. We herein propose a strategy for constructing carbon–nitrogen (C─N) axially chiral molecular frameworks to fully exploit the excellent chromophoric properties of nitrogen‐containing heterocycles (such as carbazole). A pair of chiral emitters, ( S / R )‐AI‐2TCFC, was designed and synthesized, exhibiting an emission peak at 578 nm both in the toluene solution and in the neat film state. It possessed typical aggregation‐induced emission (AIE), thermally activated delayed fluorescence (TADF), and a luminescence dissymmetry factor ( g lum ) of 10 −3 , demonstrating its potential for high‐performance device applications. These materials were successfully applied in circularly polarized organic light‐emitting diodes (CP‐OLEDs), demonstrating promising electroluminescence performance. This innovative strategy not only expands the design toolbox for CPL materials but also paves the way for next‐generation high‐performance optoelectronic devices.
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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".