Photolithographically Patterned TiO<sub>2</sub> Films for Electrolyte-Gated Transistors
Bibliographic record
Abstract
Metal oxides constitute a class of materials whose properties cover the entire range from insulators to semiconductors to metals. Most metal oxides are abundant and accessible at moderate cost. Metal oxides are widely investigated as channel materials in transistors, including electrolyte-gated transistors, where the charge carrier density can be modulated by orders of magnitude upon application of relatively low electrical bias (2 V). Electrolyte gating offers the opportunity to envisage new applications in flexible and printed electronics as well as to improve our current understanding of fundamental processes in electronic materials, e.g. insulator/metal transitions. In this work, we employ photolithographically patterned TiO2 films as channels for electrolyte-gated transistors. TiO2 stands out for its biocompatibility and wide use in sensing, electrochromics, photovoltaics and photocatalysis. We fabricated TiO2 electrolyte-gated transistors using an original unconventional parylene-based patterning technique. By using a combination of electrochemical and charge carrier transport measurements we demonstrated that patterning improves the performance of electrolyte-gated TiO2 transistors with respect to their unpatterned counterparts. Patterned electrolyte-gated (EG) TiO2 transistors show threshold voltages of about 0.9 V, ON/OFF ratios as high as 1 × 10(5), and electron mobility above 1 cm(2)/(V s).
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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) | 0.002 | 0.001 |
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".