(Keynote) Progress in the Development of Metal Oxide Gas Sensors to Reduce Carbon Footprint
Bibliographic record
Abstract
A chemical gas sensor is a device that transfers chemical information (receptor) of a specific sample component or total composition into an analytically useful signal (transducer). Based upon the transducer operating principle, the signals could be the results of optical, electrochemical, mass, calorimetric and/or magnetic properties. The challenge to fix the current issues on cost, sensitivity, selectivity and reliability of a gas sensor, metal oxide based electrochemical gas sensors are much emphasized for the development of suitable sensor materials. As such, electrochemical gas sensors are mainly categorized into potentiometric, amperometric and resistive-type depending upon the analytical electrical signals; V, I and R, respectively. The real-time detection of gas composition is essential for improving efficiency in industrial process and to lower the greenhouse gas (GHG) emissions. CO2, one of the major components of GHGs, encounters a challenge on being monitored at real-time under harsh environmental conditions. Spectroscopic and optical techniques based on infrared radiation are commonly used to detect CO2 at room temperature, however, the in situ monitoring of the gas species by placing such sensors directly at high temperature and aggressive environment is practically incompatible due to their size and stability issues. Na+ and Li+ ion conducting electrolytes (e.g., Na-β-alumina, NASICON-type), and Na2CO3 and BaCO3 electrodes have been widely employed to fabricate all-solid-state EMF CO2 sensor. On the other hand, SnO2, TiO2 and WO3 as semiconductor-based sensors were also used as resistive-type CO2 sensors. In both cases, the stability and cross sensitivities, however, have remained as major obstacles. Our approach was, therefore, to use perovskite oxides (POs) in semiconductor-based gas sensors, for their high thermal and chemical stability, which in turn improve sensor's reliability and long-term performance. In addition, the doping flexibility in POs allowed us to prepare transition metal-doped perovskites. The critical role of dopant on CO2 sensing properties is discussed. The future prospect of using nanotech with its ability to precisely control the structure of these materials may guide CO2 sensors into a whole new level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.035 |
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".