Printing organic‐field effect transistors from semiconducting polymers and branched polyethylene
Bibliographic record
Abstract
Abstract Organic electroactive materials, particularly semiconducting polymers, are at the forefront of emerging organic electronics. Among the plethora of unique features, the possibility to formulate inks out of these materials is particularly promising for the large‐scale manufacturing of electronics at lower cost on a variety of soft substrates. While solution deposition of semiconducting materials is promising for developing printed electronics, the environmental footprint of the materials and related devices needs to be considered to achieve sustainable manufacturing. Towards the development of greener printed electronics, this work investigates the utilization of a non‐toxic, environmentally‐friendly solvent, namely branched polyethylene (BPE), to formulate semiconducting inks. Focusing on a diketopyrrolopyrrole‐based (DPP) semiconducting polymer, shellac as dielectric, and BPE as the solvent, solutions were prepared in different concentrations and their rheological properties were characterized. Then, printing on polyethylene terephthalate (PET) substrates using two different techniques was performed to fabricate organic field‐effect transistors (OFETs). Both printing techniques yielded OFETs with good performance and device characteristics, averaging approximately 10−2 and 10−4 cm2 V−1 s−1, respectively, for slot‐die coating and direct‐ink writing deposition. Notably, despite some difference in threshold voltages, OFETs produced via slot‐die coating and direct‐ink writing showed comparable charge mobilities to previously reported OFETs prepared from similar materials, particularly those prepared on silicon dioxide wafers. Overall, this work confirms the suitability of BPE to formulate semiconducting inks to develop printed electronics in a greener manner. The printing methodology developed in this work also open new avenues for the design of functional printed electronics and related technologies.
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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".