A Novel APPI-MS Setup for In Situ Degradation Product Studies of Atmospherically Relevant Compounds: Capillary Atmospheric Pressure Photo Ionization (cAPPI)
Bibliographic record
Abstract
We report on the development of a novel atmospheric pressure photoionization setup and its applicability for in situ degradation product studies of atmospherically relevant compounds. A custom miniature spark discharge lamp was embedded into an ion transfer capillary, which separates the atmospheric pressure from the low pressure region in the first differential pumping stage of a conventional atmospheric pressure ionization mass spectrometer. The lamp operates with a continuous argon flow and produces intense light emissions in the VUV. The custom lamp is operated windowless and efficiently illuminates the sample flow through the transfer capillary on an area smaller than 1 mm(2). Limits of detection in the lower ppbV range, a temporal resolution of milliseconds in the positive as well as the quasi simultaneously operating negative ion mode, and a significant reduction of ion transformation processes render this system applicable to real time studies of rapidly changing chemical systems. The method termed capillary atmospheric pressure photo ionization (cAPPI) is characterized with respect to the lamp emission properties as a function of the operating conditions, temporal response, and its applicability for in situ degradation product studies of atmospherically relevant compounds, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".