Polyethylene and Semiconducting Polymer Blends for the Fabrication of Organic Field-Effect Transistors: Balancing Charge Transport and Stretchability
Bibliographic record
Abstract
Polyethylene is amongst the most used polymers, finding a plethora of applications in our lives owing to its high impact resistance, non-corrosive nature, light weight, cost effectiveness, and easy processing into various shapes from different sizes. Despite these outstanding features, the commodity polymer has been underexplored in the field of organic electronics. This work focuses on the development of new polymer blends based on a low molecular weight linear polyethylene (LPE) derivative with a high-performance diketopyrrolopyrrole-based semiconducting polymer. Physical blending of the polyethylene with semiconducting polymers was performed at ratios varying from 0 to 75 wt.%, and the resulting blends were carefully characterized to reveal their electronic and solid-state properties. The new polymer blends were also characterized to reveal the influence of polyethylene on the mechanical robustness and stretchability of the semiconducting polymer. Overall, the introduction of LPE was shown to have little to no effect on the solid-state properties of the materials, despite some influence on solid-state morphology through phase separation. Organic field-effect transistors prepared from the new blends showed good device characteristics, even at higher ratios of polyethylene, with an average mobility of 0.151 cm2 V−1 s−1 at a 25 wt.% blend ratio. The addition of polyethylene was shown to have a plasticizing effect on the semiconducting polymers, helping to reduce crack width upon strain and contributing to devices accommodating more strain without suffering from decreased performance. The new blends presented in this work provide a novel platform from which to access more mechanically robust organic electronics and show promising features for the utilization of polyethylene for the solution processing of advanced semiconducting materials toward novel soft electronics and sensors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".