Titanium Dioxide and Tin Oxide Composite for so<sub>2</sub> Gas Sensors
Bibliographic record
Abstract
We present the synthesis and utilization of semiconducting metal oxide composites of SnO2 and TiO2 composites (ST) to serve as a SO2 sensor for a range of applications including carbon capture and storage (CCS), enhance oil recovery (EOR) etc. ST-series pellets were prepared by conventional solid state synthesis approach (~700 oC in air), where the size of the pellet was ~1 cm in diameter. The chemical and thermal stability was demonstrated by employing scanning electron microscopy (SEM) and powder X-ray diffraction (PXRD). The average grain size of as-prepared ST composites was obtained in few nanometers. The as-prepared ST pellets were tested for the sensing properties of SO2 gas in ppm level. The effect of temperature was studied to optimize the operating temperature using Pt electrodes as current collector by applying a constant external potential (~100 mV). As-prepared ST2575 with 0.25 mol % SnO2 exhibited an excellent sensing properties for SO2 (10-40 ppm) at 450 oC. For the sensing measurements, SO2 gas was diluted in N2 gas by maintaining the total flow rate at 100 sccm. The t 90 (90% of response time) for ST2575 was found to be ~5 mins. The stability of the sensor was satisfactory for gas sensing measurements in optimized conditions.
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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 | 0.001 | 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".