Fully Printed Organic Electrochemical Transistors With Low‐Resistance Electrodes on Planarized 3D‐Printed Substrates
Bibliographic record
Abstract
ABSTRACT This work presents a novel methodology for fabricating organic electrochemical transistors (OECTs) integrated with planarized 3D‐printed substrates, offering a scalable approach to integrate functional electronics into 3D‐printed systems. By combining fused deposition modeling (FDM) with in situ surface ironing, dispense printing for low‐resistance silver electrodes, and inkjet printing for high‐quality poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) channels, we achieve high‐performance transistors with an average transconductance (g m ) of 78.2 mS, one of the highest values reported in the literature. OECTs integrated with planarized 3D printed substrates are compared to devices fabricated on glass substrates and achieved comparable performance. The thick, low‐resistance electrodes produced via dispense printing are critical for this performance, demonstrating the technique's suitability for OECT manufacturing. This study establishes a process for integrating OECTs with 3D‐printed structures, paving the way for customizable, application‐specific electronic devices. By addressing key challenges in material compatibility and integrated fabrication, this work contributes to advancing the fields of bioelectronics, wearables, and additive manufacturing.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".