Exploiting a Dual‐Fluorescence Process in Fluorene–Dibenzothiophene‐<i>S</i>,<i>S</i>‐dioxideCo‐Polymers to Give Efficient Single Polymer LEDs with Broadened Emission
Bibliographic record
Abstract
Abstract A description of the synthesis of random (9,9‐dioctylfluorene‐2,7‐diyl)–(dibenzothiophene‐S,S‐dioxide‐3,7‐diyl) co‐polymers (p(F‐S)x) by palladium‐catalyzed Suzuki cross‐coupling polymerization where the feed ratio of the latter is varied from 2 to 30 mol % (i.e., x = 2–30) is given. Polymer light emitting devices are fabricated with the configuration indium tin oxide/poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonic acid)/p(F–S)x/Ba/Al. The device external quantum efficiency increased as the ratio of the S co‐monomer was increased, up to a maximum of 1.3% at 100 mA cm−2 for p(F‐S)30 and a brightness of 3 770 cd m−2 (at 10 V). The S units impart improved electron injection, more balanced mobilities, and markedly improved device performance compared to poly(9,9‐dioctylfluorene) under similar conditions. These co‐polymers display broad emission, observed as greenish‐white light, which arises from dual fluorescence, viz. both local excited states and charge transfer states. Utilizing dual emission can reduce problems associated with Förster energy transfer from high‐energy to‐low energy excited states.
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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 | 0.001 |
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".