Poly(ionic liquid) Gating Materials for High-Performance Organic Thin-Film Transistors: The Role of Block Copolymer Self-Assembly at the Semiconductor Interface
Bibliographic record
Abstract
The widespread realization of wearable electronics requires printable active materials capable of operating at low voltages. Polymerized ionic liquid (PIL) block copolymers exhibit a thickness-independent double-layer capacitance that makes them a promising gating medium for the development of organic thin-film transistors (OTFTs) with low operating voltages and high switching speed. PIL block copolymer structure and self-assembly can influence ion conductivity and the resulting OTFT performance. In an OTFT, self-assembly of the PIL gate on the semiconducting polymer may differ from bulk self-assembly, which would directly influence electrical double-layer formation. To this end, we used poly{[N,N′-bis(2-octyldodecyl)-naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]- alt -5,5′-(2,2′-bithiophene)} (P(NDI2OD-T2)) as a model semiconductor for our OTFTs, on which our PILs exhibited self-assembly. In this study, we explore this critical interface by grazing-incidence small-angle X-ray scattering (GISAXS) and atomic force microscopy (AFM) of P(NDI2OD-T2) and a series of poly(styrene)- b -poly(1-(4-vinylbenzyl)-3-butylimidazolium-random-poly(ethylene glycol) methyl ether methacrylate) (poly(S)- b -poly(VBBI + [X]-r-PEGMA)) block copolymers with varying PEGMA/VBBI + ratios and three different mobile anions (where X = TFSI –, PF 6 –, or BF 4 – ). We investigate the thin-film self-assembly of block copolymers as a function of device performance. Overall, a mixed orientation at the interface leads to improved device performance, while predominantly hexagonal packing leads to nonfunctional devices, regardless of the anion present. These PIL gated OTFTs were characterized with a threshold voltage below 1 V, making understanding of their structure–property relationships crucial to enabling the further development of high-performance gating materials.
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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".