Sub-1 V Electrolyte-Gated Polymer Transistors with Ionic Liquid Electrolyte and High-Surface Area Carbon Gate Electrode
Bibliographic record
Abstract
Electrolyte-gated (EG) transistors permit to achieve current modulations of several orders of magnitude at relatively modest gate voltages, by exploiting the exceptionally high capacitance of electrical double layer forming at the electrolyte/transistor channel interface. Different doping mechanisms have been proposed to explain the gating process in EG transistors. For organic polymers, the common doping mechanism is faradic, depends on the channel electrical potential and the doping charge is about two orders of magnitude larger than for electrostatically doped non-porous materials. Moreover, organic channel and electrolyte have to be selected taking into account that channel modulation has to be performed by applying gate-source voltages (Vgs) which are compatible with the electrochemical stability window of the electrolyte. Ionic liquid electrolytes, for their exceptionally high chemical and electrochemical stability and good conductivity at room temperature are of interest as electrolyte gating media in EG transistor. The faradic nature of the polymer channel doping also requires the use of gate electrode materials that are non-limiting in terms of their capability to supply the charge required for channel modulation within the electrochemical stability window of the electrolyte. We targeted the use of high surface area carbons as gate electrode materials because of their capability to electrostatically supply the charge required to dope organic polymer transistor channels, within narrow electrode potential excursion. Here, we report on a new generation of low voltage EG transistors making use of a low-cost, high surface area gate electrode, an organic electronic polymer, like MEH-PPV (poly[2-methoxy-5-(2'-ethylhexyloxy)-p-phenylene vinylene), as the channel material and ionic liquid, like [EMIM][TFSI] (1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide), as the electrolyte gating medium. Using such configuration, is it possible to apply Vgs biases lower than 1.0 V to achieve effective channel modulations (ON/OFF ratio larger than 103) and hole mobility of about 10-3 cm2V-1s-1. Acknowledgement This work was financially supported by NSERC (Discovery grants, CS and FC) and FQRNT (Nouveau Chercheur, CS). JS acknowledges financial support by CONACYT. References [1] J. Sayago, F. Soavi, F. Cicoira, C. Santato, Adv. Mater., submitted.
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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 | 0.001 | 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".