Effect of grain boundaries on hydrocarbon sensing in Fe-doped p-type semiconducting perovskite SrTiO3 films
Bibliographic record
Abstract
Films of Fe-doped SrTiO3 deposited using pulsed laser deposition on sapphire and alumina, when exposed to propane, showed different sensor responses measured (as a resistance change) as a function of temperature and microstructure. The film deposited on alumina has a stronger response towards 3000-ppm propane than does the film deposited on sapphire. Films deposited on alumina exhibit higher dc conductivity than the films on sapphire. The activation energies indicate a mixed electronic/ionic conduction at low temperature with the high-temperature regime showing a temperature-independent conductivity. The origin of the difference in gas responses caused by varying temperature and morphology has been explored using ac impedance techniques and measured as a function of frequency (1Hz⩽f⩽1MHz) and temperature (200⩽T⩽480°C). A single relaxation (a single semicircle in the complex impedance plane) in the frequency domain was observed in addition to the relaxation due to the electrode-film interface. A model and a mechanism of conduction for the above are derived using equivalent circuits to fit the ac impedance data and dc conductivity. It is proposed that the reduction of Fe-doped SrTiO3, which is induced by propane, enhances the space-charge barrier near the grain boundaries and increases the sensitivity to propane.
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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 | 0.001 |
| 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".