Poly(ionic liquid) dielectric for high performing P- and N-type single walled carbon nanotube transistors
Bibliographic record
Abstract
Abstract There is an increasing demand for low-cost and high-performance electronics which has stimulated a need for new high-performance dielectric materials. We have developed a facile synthesis of poly(2-(methacryloyloxy)ethyl trimethylammonium bis(trifluoromethylsulfonyl)azanide-ran-methyl methacrylate) (P(METATFSI-MMA)), a polymeric ionic liquid that can be used as a high-performance dielectric for semiconducting single walled carbon nanotube (SWCNTs) thin film transistors (TFTs). The P(METATFSI-MMA) polymer was synthesized at both 35 and 62 mol% of 2-(methacryloyloxy)ethyl trimethylammonium bis(trifluoromethylsulfonyl)azanide and produced p- and n-type devices that functioned under ambient conditions. These TFTs were then used to study the impact of electrochemical doping on the performance of SWCNT TFTs when switching from n-type, where an electrical double layer is formed, to p-type, where the TFSI− anions are free to interact with the SWCNTs. The TFTs operating in p-type had higher current on/off ratios and a larger transconductance than those operating in n-type, which is characteristic of electrochemically doped transistors. Furthermore, we tested the impact of operating frequency on device performance and discovered that decreasing the operating frequency of the TFTs resulted in a decreased hysteresis. The decrease in hysteresis was also observed to be more significant for the 35 mol% polymer.
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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".