Analyzing the n-Doping Mechanism of an Air-Stable Small-Molecule Precursor
Bibliographic record
Abstract
Efficient n-doping of organic semiconductors requires electron-donating molecules with small ionization energies, making such n-dopants usually sensitive to degradation under air exposure. A workaround consists in the usage of air-stable precursor molecules containing the actual n-doping species. Here, we systematically analyze the doping mechanism of the small-molecule precursor o-MeO-DMBI-Cl, which releases a highly reducing o-MeO-DMBI radical upon thermal evaporation. n-Doping of N,N-bis(fluoren-2-yl)-naphthalene tetracarboxylic diimide yields air-stable and highly conductive films suitable for application as electron transport layer in organic solar cells. By photoelectron spectroscopy, we determine a reduced doping efficiency at high doping concentrations. We attribute this reduction to a change of the precursor decomposition mechanism with rising crucible temperature, yielding an undesired demethylation at high evaporation rates. Our results do not only show the possibility of efficient and air-stable n-doping, but also support the design of novel air-stable precursor molecules of strong n-dopants.
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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".