Towards sustainable, direct printed, organic transistors with biocompatible copolymer gate dielectrics
Bibliographic record
Abstract
Abstract We have investigated the potential of three dielectric materials to meet the future demands of green dielectrics: Polycaprolactone (PCL) thermoplastic, polyvinyl alcohol (PVA)‐carrageenan (CAR) crosslinked biopolymer, and boron nitride nanotubes (BNNTs) as a nano additive in PVA. Metal–insulator–metal (MIM) capacitors and organic thin film transistors (OTFT) were built with bilayer dielectric stacks of PVA‐CAR, PVA‐PCL, and PVA‐BNNT materials to examine their electrical properties. The PVA‐CAR layer uses a cyclic freeze thaw process to crosslink PVA and CAR for superior mechanical and electrical properties to either material alone. The PVA‐CAR MIM capacitors showed a dielectric constant of 23, which was found to be consistent with the extracted OTFT gate dielectric characteristics. Of the OTFT devices tested, PVA‐CAR OTFT showed highest device currents at low applied biases and produced an ON/OFF ratio of 104–105, both values were highest amongst the tested gate dielectrics. This material is therefore extremely promising for green electronics. The PVA‐PCL OTFT had very low leakage current and beneficial hydrophilic properties with comparable electrical properties to the commonly used organic material polytetrafluoroethylene. PVA‐BNNT MIM capacitors showed a low dielectric constant of 0.7, and the high resistivity makes this a promising material for shielding or substrates in high frequency applications. All three materials have the potential to fulfil different niches in a sustainable electronics future.
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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 | 0.001 |
| 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".