Highly sensitive and fully printable humidity sensor on a flexible substrate based on a zinc oxide and polyethylenimine composite
Bibliographic record
Abstract
Abstract We report a highly sensitive and fully printable capacitive humidity sensor based on a zinc oxide (ZnO) and polyethylenimine (PEI) composite. The sensor has a simple structure, consisting only of a layer of the ZnO:PEI composite, coated using an ethanol solution, on a layer of silver inter-digital electrodes that have been pre-printed on a polyethyleneterephthalate substrate. The sensor with ZnO:PEI in the ratio of 2:1 by volume exhibits a response of 43 907 000% at maximum humidity, with a detection range of 15%–95% relative humidity, higher than other sensors fully made by wet-coating processes. Fourier transform infrared spectroscopy, atomic force microscopy, and scanning electron microscopy measurements suggest that the high response likely arises from the use of a hydrophilic polymer with a high dipole moment which facilitates dipole-dipole interactions with water molecules and from the highly granular morphology of the composite which leads to a high surface-to-volume ratio and more-numerous water adsorption sites. The fabricated sensor also demonstrates short response/recovery times (5 s/3 s), good repeatability over multiple humidification and desiccation cycles, and only 5% loss in response after being kept in the ambient for three weeks.
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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 | 0.001 | 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".