Enhancing Phosphorescence and Electrophosphorescence Efficiency of Cyclometalated Pt(II) Compounds with Triarylboron
Bibliographic record
Abstract
Abstract A synthetic strategy for the preparation of cyclometalated platinum(II) acetylacetonate (acac) complexes functionalized with triarylboron is achieved. This method is used to synthesize a series of triarylboron‐functionalized phosphorescent Pt(acac) compounds, which are characterized by NMR spectroscopy, X‐ray crystallography, and theoretical calculations. These complexes exhibit a range of bright phosphorescent colors spanning the green to red region of the visible spectrum (λmax = ∼520–650 nm) in solution and the solid state. Functionalization with a triarylboron group leads to significant enhancement in quantum yield for several of these complexes relative to the non‐borylated Pt(II) parent chromophores, which may be attributed to the increased mixing of 1MLCT and 3LC states. The phosphorescent enhancement, electron transport capabilities, and steric bulkiness offered by the triarylboron group can be used to significantly enhance the performance of electrophosphorescent devices based on Pt(II) emitters. A high efficiency green electrophosphorescent device is fabricated with a maximum external quantum efficiency of 8.9%, luminance efficiency of 34.5 cd A−1, and power efficiency of 29.8 lm W−1, giving significantly improved performance over control devices in which the Pt(II) emitter lacks the boron functionality.
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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 | 0.001 | 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".