Enhancing detecting ammonia from 1 to 10,000’s PPM using laser-induced fluorescence of tuned vapochromic coordination polymers
Bibliographic record
Abstract
The detection of ammonia and similar gases over a wide range from a few Parts Per Millions (ppm) to 10,000’s ppm in a single sensor is important for industrial applications. We are exploring Vapochromic Coordination Polymers (VCP) specifically Zn[Au(CN)2]2, developed to achieve fluorescence when exposed to NH3. At high concentration of ammonia under UV stimulation VCP spectrally shifts its fluorescent peak from 470nm to 530nm while the intensity grows 3~5X. We use a 405nm laser diode excitation source which provides a narrow (4nm) stimulation clearly separated from the spectral peak. Focusing the emission on a USB portable spectrometer (430 to 700nm) at concentrations <1000 ppm of ammonia there is almost no peak wavelength spectral shift or intensity change and only subtle fluorescent spectrum alterations. To detect first we create a method that gives unique values over the range 1- <1000 ppm by dividing the spectrum into 10 nm bins, integrate the emission in each bin, relative to that of 0 ppm exposure, then sum all the bins (Sum of Integrated Emissions, SIE). The key analysis point 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 <400 ppm, but poor accuracy in the 100-500ppm range. Using the SIE to identify measurements in that region we switch to a second metric, Limited Range SIE, that covers only the 430-470nm bins but for 100-500ppm gives an accurate linear response. This shows that in many spectral fluorescence cases in the region where the longer wavelength peak begins to dominate looking at regions outside of the peak maximas is more accurate than including those within the unexposed to saturated exposure (eg ammonia) peak range. By creating a model assuming the fraction z of 0ppm and saturated spectrum are linearly combined we fit the measured spectrum using regression analysis to obtain the z value for all ppm measurements which show what is going on in the VCP conversion.
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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".