Laser‐Induced Graphene Electrodes for Organic Electrochemical Transistors (OECTs)
Bibliographic record
Abstract
Abstract Organic electrochemical transistors (OECTs) have drawn significant interest because of their low cost, biocompatibility, and ease of fabrication, allowing them to be utilized in various applications including flexible displays, electrochemical sensing, and biosensing. Key components of OECTs are the gate, source, and drain electrodes. Herein, OECTs with laser‐induced graphene (LIG) electrodes are demonstrated. The electrode patterns for the source, drain, and gate are created by converting the polymer substrate polyimide (PI) into LIG using a scanned laser. The process is simple and inexpensive without complicated chemical synthesis routines or expensive materials such as gold. Patterns can be customized quickly and digitally. The low‐cost and porous LIG electrodes with low contact resistance and good electrical stability play an essential role in device performance. The minimum sheet resistance achieved with this laser method for the square patterned electrodes is 7.86 Ω sq−1. The LIG‐based OECTs demonstrate good electrical modulation with ON–OFF ratio of 72.80 and high ON current on the order of mA. The LIG‐based OECTs exhibit comparable or better performance in comparison with other reports of OECTs on plastic substrates using more complex fabrication methods in terms of OFF current, ON current, transconductance (gm), and contact resistance.
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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".