Controlling Structural and Energetic Disorder in High-Mobility Polymer Semiconductors via Doping with Nitroaromatics
Bibliographic record
Abstract
Molecular doping has emerged as a powerful strategy to tune the charge transport properties of organic field-effect transistors (OFETs). However, the limited tool-box of molecular dopants and unresolved challenges of stability, uniformity of the doping, and matching the energy levels constrain the achieved OFET device performance and thwart the practical applications. Here, nitrofluorene (NF) acceptors are introduced as effective p-dopants in polymer OFETs, resulting in outstanding device performance of a standard commercial diketopyrrolopyrrole-thienothiophene (DPP-DTT) polymer. An ∼5-fold enhancement in the saturation field-effect mobility (up to ∼8 cm 2 V –1 s –1 ) is realized in ambient air operations after doping with 2,4,5,7-tetranitrofluorenone. Importantly, the achieved effective mobility (which accounts for device nonideality) exceeds 6 cm 2 V –1 s –1, which is among the highest values reported for polymer OFETs. The spectroscopic, microscopic, X-ray diffraction, and electrical investigations elucidate the role of NF dopants in mitigating charge carrier traps, lowering the contact resistance, and maximizing the structural order of the polymer films. Energetic disorder reduces significantly upon doping as revealed via variable temperature mobility measurements.
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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".