Sum of integrated emissions detecting ammonia from 1 to 10,000 ppm using 405nm induced fluorescence of vapochromic coordination polymers
Bibliographic record
Abstract
A common problem in fluorescence detection of gases like ammonia over a wide range from Parts Per Millions (ppm) to 10,000’s ppm (~saturation) is that often over a 0 to 1000 ppm range the spectrums show almost no visible changes. For many materials common detection methods like integrated emission, spectral subtraction, peak wavelength shift, peak intensity, FWHM, asymmetry all show only small fluorescent spectrum changes with ppm, typically shift <0.1% at 1000 ppm. Hence metrics more sensitive to subtle spectral changes are needed. We are exploring this with three different Vapochromic Coordination Polymers (VCP). which fluorescence when exposed to NH3 but in different ways: Zn[Au(CN)2]2, shifts its peak from 470 to 530nm under high concentration while peak intensity grows 3 to 5X, but shows tiny change <1000 ppm. Another VCP In2[Pt(CN)4]3 shifts opposite, from 560nm (yellow) to 530nm but with even less change <1000ppm while Zn[Pt(CN)4] fluoresces at the short 430nm. To enhance subtle differences we use a 405nm laser diode excitation where the narrow 4nm stimulation does not mask short λ contributions. Observing emission with a USB spectrometer we increase the slight spectra changes by dividing the spectrum into 10nm bins, integrate the emission in each bin relative to that of 0 ppm exposure, then sum all the bins (Sum of Integrated Emissions, SIE). This emphasizes wavelength regions having rapid relative change at different ammonia ppm’s. SIE gives excellent sensitivity in most ppm ranges, but at mid range 100 to 500 ppm regions it changes <1%: eg Zn[Au(CN)2]2, There some SIE bins decline while others increase due to the peak spectral shifts so it best to observe fewer SIE bins and look for ranges showing increasing values creating a second metric, Limited Range SIE, eg for Zn:Au 430 to 470nm bins show an accurate linear response. In many spectral fluorescence cases the region where the longer wavelength peak begins to dominate it is best to focus on regions outside of the peak maxim.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".