Identification of Organic Hydroperoxides and Peroxy Acids Using Atmospheric Pressure Chemical Ionization – Tandem Mass Spectrometry (APCI-MS/MS): Application to Secondary Organic Aerosol
Bibliographic record
Abstract
Abstract. Molecules with hydroperoxide functional groups are of extreme importance to both the atmospheric and biological chemistry fields. In this work, an analytical method is presented for the identification of organic hydroperoxides and peroxy acids (ROOH) by direct infusion of liquid samples into a positive-ion atmospheric pressure chemical ionization-tandem mass spectrometer ((+)-APCI-MS/MS). Under collisional dissociation conditions, a characteristic neutral loss of 51 Da (arising from loss of H2O2+NH3) from ammonium adducts of the molecular ions ([M+NH4]+) is observed for ROOH standards (i.e. cumene hydroperoxide, isoprene-4-hydroxy-3-hydroperoxide (ISOPOOH), tert-butyl hydroperoxide, 2-butanone peroxide and peracetic acid), as well as the ROOH formed from the reactions of H2O2 with aldehydes (i.e. acetaldehyde, hexanal, glyoxal and methylglyoxal). This new ROOH detection method was applied to methanol extracts of secondary organic aerosol (SOA) material generated from ozonolysis of α-pinene, indicating a number of ROOH molecules in the SOA material. While the full scan mass spectrum of SOA demonstrates the presence of monomers (m/z = 80–250), dimers (m/z = 250–450) and trimers (m/z = 450–600), the neutral loss scan shows that the ROOH products all have masses less than 300 Da, indicating that ROOH molecules may not contribute significantly to the SOA oligomeric content. We anticipate this method could also be applied to biological systems with considerable value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".