Improving Environmental and Operational Stability of Polymer Field-Effect Transistors by Doping with Tetranitrofluorenone
Bibliographic record
Abstract
Operational instability of organic field-effect transistors (OFETs) is one of the key limitations for applications of printed electronics. Environmental species, especially oxygen and water, unintentionally introduced in the OFET channel, can act as either dopants or traps for charge carriers, affecting the electrical characteristics and stability of devices. Here, we report that intentional doping of the benchmark p-type semiconducting polymer (DPP-DTT) with 2,4,5,7-tetranitrofluorenone (TeNF) markedly improves the operational and environmental stability of OFETs. Electrical interrogation of DPP-DTT OFETs in various environments and at variable temperatures shows suppression of electron-induced traps and increase of hole mobility in oxygen-rich environment, while the water molecules act as traps for positive charge carrier, reducing the hole mobility and significantly shifting the threshold voltage. Doping of DPP-DTT with TeNF suppresses both effects, resulting in environmentally independent performance and superior long-term stability of unencapsulated devices for up to 4 months in ambient air. Furthermore, the doped OFETs exhibit dramatically reduced hysteresis and bias-stressed current drop. Such improvement of the environmental and operational stabilities is ascribed to the mitigation of traps induced by the injected minority carrier (electrons) and the reduction of the majority carrier (hole) traps in doped polymer films due to enhanced microstructural order.
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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".