Molecular Oxygen (O<sub>2</sub>) Artifacts in Tandem Mass Spectra
Bibliographic record
Abstract
Peak annotation plays an important role in mass spectral evaluation of the NIST 2023 tandem mass spectral library. While most fragment ions are formed by neutral losses, there are peaks that represent adduct ions from these fragments. Previously, we have reported two main types of addition reactions in the collision cell, namely addition of H 2 O and N 2 . Here we report a different reaction in the collision cell, with addition of O 2 leading to a small peak that could only be assigned to a peroxyl radical ion. For example, some protonated iodoaromatics lose an iodine atom to form a radical cation [M+H–I] +•, which reacts with O 2 to generate a peroxyl radical ion [M+H–I+O 2 ] +• . Higher concentrations of O 2 result in higher peroxyl radical peaks, which become dominant while the precursor ions are consumed, as examined by five compounds under different concentrations of O 2 . The correlation of the peroxyl radical peak intensities to the concentration of O 2 provides a tool to estimate trace amounts of O 2 within the instrument. In the NIST 2023 tandem mass spectral library, the peaks for [M+H–X+O 2 ] +· are most abundant in numbers and in intensity for X = NO 2 or I, are much less abundant for X = Br, and are rare for X = Cl. Other leaving groups in this library are SO 3 H, SO 2 NH 2, CSNH 2, CO 2 C 6 F 5, SO 2 CH 3, and COCH 3 . The O 2 addition reaction is also observed with negative ions in this library. While adducts of H 2 O and N 2 often constitute major peaks, the peaks of the peroxyl radicals under standard conditions are mostly very small and may be mistaken for noise, but their correct annotation improves the quality of the spectra and is important when comparing spectra from different instruments or conditions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".