Combining Aqueous Solution Processing and Printing for Fabrication of Flexible and Sustainable Tin Dioxide Ion‐Gated Transistors
Bibliographic record
Abstract
Abstract Ion‐gated transistors (IGTs) are extensively used in chemo‐ and bio‐sensors as well as intelligent sensors, that is, with neuromorphic computing functionality, exploiting their ion to electron convertibility. Metal oxides are attractive as active channel materials in IGTs because of their low‐temperature solution processability, ambient stability, and tunable optoelectronic properties. SnO2 is a low‐cost material widely used in thin‐film transistors, gas sensors, and transparent electrodes. In this work, films of crystalline SnO2 nanorods are prepared on flexible substrates using a controlled aqueous growth technique, at 95 °C. The top contact source and drain metal contacts were patterned by either photolithography or printing. The transistor behavior of SnO2 nanorod films gated with different gating media, such as room‐temperature ionic liquids, in inert atmosphere, and aqueous saline solutions, in ambient air are studied. In addition, the transistor behavior of SnO2 IGTs in the original flat and tensile bending state are also studied. The earth abundance of tin oxide, the low energy consumption fabrication process by low‐temperature solution processing and printing, as well as, the use of an aqueous electrolyte for the gating medium make our devices extremely promising for green and sustainable electronics.
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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".