Bibliographic record
Abstract
ABSTRACT Electrochemical amperometric gas cells are becoming the sensor of choice when measuring polluting gases using low-cost air quality networks. A number of technical issues remain to be resolved to deliver fit-for-purpose monitoring systems: humidity corrections are needed but not well understood, interfering gases such as ozone can have variable cross-sensitivity and calibration intervals, and procedures are still being investigated. Another unanswered question is the limit of detection (LOD) for electrochemical gas sensors. Estimates range from hundreds of equivalent parts per billion (ppbv) to single-digit ppbv concentrations. We discuss the LOD for nitrogen dioxide (NO2), an important gas when monitoring air quality. Multiple NO2 sensor systems were tested in an environmental chamber to determine, among other parameters, the LOD for NO2 electrochemical gas sensors. Low-noise electronics and battery powering further reduced electronic noise, allowing the intrinsic LOD of the electrochemical cell to be determined. Noise, quantified as the standard deviation in zero air in a very stable temperature and relative humidity–controlled chamber was <500 pA, which translated into 1.6 ppbv, so the LOD, 3 × standard deviation, was 4.8 ppb. Interestingly, the LOD calculated with 300 ppbv NO2 test gas was the same (±0.1 ppbv). Further tests with a higher resolution analog-to-digital converter resulted in the same LOD, further leading to the conclusion that for the Alphasense NO2-A43F NO2 sensor, the limiting value for LOD is 4.8 ppbv.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".