Detecting PPM ammonia over wide range using laser induced fluorescence of vapochromic coordination polymers
Bibliographic record
Abstract
The detection of ammonia over a wide range from parts per millions (PPM) to 1000’s ppm in a single sensor is of great importance for industrial applications. We have been exploring Vapochromic Coordination Polymers (VCP) specifically Zn[Au(CN)2]2, that was developed to achieve fluorescence when exposed to NH3. Upon high concentration ammonia exposure, the fluorescent peak under near-UV stimulation undergoes a spectral shift from 470nm to 530nm, while the intensity increases by 3~4X. However, at ammonia concentrations < 100 ppm, there is almost no peak wavelength spectral shift or intensity change and only subtle fluorescent spectrum alterations. Using a 405nm laser diode excitation source provides a narrow (4nm) stimulation easily separated from the emission peak. The emission is focused on a USB portable spectrometer (430 to 650 nm). First we create a method that gives unique values over the range <1000 ppm by dividing the spectrum into 20 nm bins, and integrate the emission in each bin, relative to that of 0 ppm exposure (Sum of Integrated Emissions). The key point in this analysis is to note that the way the spectrum changes in each wavelength bin varies at different ammonia ppm exposures. SIE gives excellent sensitivity between 0-50 ppm and <300 ppm, but the 100-300 ppm region has low accuracy. There we change the metric to the Spectral Region Subtraction (SRS) by separating the spectrum into (A) 430-516 nm and (B) from 516 -650 nm, integrate the spectrum and subtract A from B, giving a rapid change within 100-300 ppm.
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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 | 0.000 | 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".