Influence of Heat Disturbance on the Performance of YSZ based CO2 Sensor with Compound of Li2CO3-BaCO3-Nd2O3 as Auxiliary Sensing Electrode
Bibliographic record
Abstract
Suddenly changes and fluctuations of temperature often occur in the operational environment of the CO2 electrochemical sensor. In this work, the YSZ based potentiometric CO2 sensor having Li2CO3-BaCO3-Nd2O3 compound as its auxiliary sensing material was prepared. And the effects of several types of heat disturbance on the performance of this kind of sensor ware studied. The results indicate that the sensors after heat disturbances respond similarly with the sensor as prepared, which presents rapid and correct response for the change of CO2 concentration within the experimental range of 271-576802 ppm. The sensors, with or without heat disturbance, respond well as different extents of abrupt alteration of CO2 concentration occurs, and the EMF outputs recover rapidly as the concentration of CO2 change back to the base value. At the constant concentration of CO2, the EMFs of the sensors with or without heat treatment decrease slowly as the time increases, the reason for this phenomenon might be the accumulation of inert substances on the electrode interfaces and ageing of electrodes. However, heat treatment can improve the long-term stability of the sensor to some extent. Furthermore, this type of sensor works stably with the existence of water vapor (10%), it has similar response curve in the dry and water vapor content system. After some further investigations and improvements, it might be potentially applied in the practical combustion atmosphere.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".