Energy Levels of Molecular Dopants in Organic Semiconductors
Bibliographic record
Abstract
Abstract The energy level alignments between hosts and dopants dictate many key physical processes such as charge transport and exciton formation, and thus the functionality and performance of organic semiconductor devices. Therefore, design and fabrication of high‐performance organic devices require knowledge of the energy offsets between hosts and dopants. Due to a typically low dopant concentration used in devices such as organic light‐emitting diodes and sometimes overlapping density of states, it is generally not possible to measure directly the energy offset in a doped system by photoemission technique. Here it is demonstrated that the energy offset between a host and a dopant in a doped system equals to that of its reciprocal heterojunction system. This opens the door for facile data collection. These directly measured offsets are also shown to be the same as the detrapping activation energies extracted from variable temperature charge transport modeling analysis. The energy level alignments between hosts and noncharge transfer dopants, charge transfer p‐type donors, and charge transfer n‐type acceptors are shown to exhibit the universal energy alignment rule for organic–organic heterojunction interfaces.
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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 | 0.001 | 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) | 0.002 | 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".