Synthesis of Semibatch Copoly(Ionic Liquid) Solid-State Electrolytes for Organic Thin-Film Transistors
Bibliographic record
Abstract
Poly(ionic liquids) (PILs) are promising dielectric/gating layers for low-power organic thin-film transistors (OTFTs), as their double-layer capacitance enables thickness-independent, low-voltage operation. The use of block copolymer PILs enables the simultaneous optimization of the mechanical and capacitive properties of the material; however, their synthesis is laborious. Semibatch Nitroxide-Mediated Polymerization (NMP) is a one-pot method for producing gradient block copolymers with simplified purification. This approach offers pathways toward industrially relevant control over molecular architecture while reducing time-consuming polymerization steps, ultimately enhancing material performance. A library of nine poly(styrene)- g -(poly(styrene- co -dimethylaminoethyl methacrylate)) gradient copolymers, denoted as PS- g -P(S- co -DMAEMA + [TFSI – ]-QX), were synthesized by semibatch NMP. Gradient copolymer formation was achieved by modulating the DMAEMA injection time, yielding neat PS blocks (Block A) with molecular weights ranging from 8 to 20 kg·mol –1 . Following DMAEMA addition, the resulting copolymers (Block B) were quaternized with alkyl chains of varying lengths (X = methyl, butyl, or hexyl) to tune the ionic character and side-chain functionality. The PILs with longer neat PS chains and those containing methyl quaternizing chains displayed the most prominent self-assembly and led to two glass transition temperatures ( T g ). When integrated into capacitors, the formation of an electrical double layer (EDL) at 10 Hz suggests efficient ion migration between electrodes. Finally, the gradient copolymers were integrated as the gating medium in OTFTs, serving as a proof-of-concept for their application in low-power electronics. The resulting n-type devices exhibited near-zero threshold voltages while maintaining good electron mobilities and high on/off current ( I ON /I OFF ) ratios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".