Facile Blending Strategy for Boosting the Conjugated Polymer Semiconductor Transistor’s Mobility
Bibliographic record
Abstract
The optimization of field-effect mobility in polymer field-effect transistors (FETs) is a critical parameter for advancing organic electronics. Today, many challenges still persist in understanding the roles of the design and processing of semiconducting polymers toward electronic performance. To address this, a facile approach to solution processing using blends of PDPP-TVT and PTPA-3CN is developed, resulting in a 3.5-fold increase in hole mobility and retained stability in electrical performance over 3 cm 2 V –1 s –1 after 20 weeks. The amorphous D–A conjugated structure and strong intramolecular polarity of PTPA-3CN are identified as major contributors to the observed improvements in mobility. Additionally, the composite analysis by X-ray photoelectron spectroscopy (XPS) and the flash differential scanning calorimetry (DSC) technique showed a uniform distribution and was well mixed in binary polymer systems. This mobility enhancement technique has also been successfully applied to other polymer semiconductor systems, offering a new design strategy for blending-type organic transistor systems. This blending methodology holds great promise for the practical applications of OFETs.
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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".