Detection of low-concentration ammonia using differential laser-induced fluorescence on vapochromic coordination polymers
Bibliographic record
Abstract
The detection of ammonia in parts per millions range has been challenging in sensors research, and is of great importance for industrial applications. In previous literature, Vapochromic Coordination Polymers (VCP) were developed to achieve luminescence upon a targeted gas exposures. We investigate a specific VCP, Zn[Au(CN)2]2,as an ammonia sensing material. Upon high concentration ammonia exposure, the fluorescent peak under near-UV stimulation undergoes a spectral shift from 460nm to 520nm, while the intensity increases by 3~4X. However, at ammonia concentrations < 50ppm, the spectral shift becomes hidden within the overall changing fluorescent spectrum shape. Then simple methods, such as detecting the peak wavelength or subtracting post-exposure from pre-exposure spectrums do not work. We developed further excitation and data processing techniques to detect ammonia at lower concentrations. A low-cost 405nm blue-ray DVD laser diode was used as the excitation source, providing a narrow band-width (4nm) stimulation that is separated from the emission peak. We measured the emission using a portable spectrometer (Photon Control SPM-002), and processed the data by separating the spectrum into two regions; (A) from 425 nm to 460 nm and (B) from 460nm to 500nm. Next, the integrated emissions under both regions were computed, and the value of shorter wavelength region (A) was subtracted from the longer wavelength one (B). When exposed to ammonia, region (A) reduces overall intensity while region (B) increases, resulting a signal starting from negative value and gradually increases to positive values, enabling the detection of 5ppm ammonia in less than 30 seconds gas exposure.
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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".