Product Ion Distributions using H <sub>3</sub> O <sup>+</sup> PTR-ToF-MS: Mechanisms, Transmission Effects, and Instrument-to-Instrument Variability
Bibliographic record
Abstract
Abstract. Proton-transfer-reaction mass spectrometry (PTR-MS) using hydronium ion (H3O+) ionization is widely used for the measurement of volatile organic compounds (VOCs) both indoors and outdoors. Unlike more energetic ionization methods (e.g., electron impact), H3O+ ionization can leave a target VOC molecule mostly intact and thus a VOC in a PTR-MS mass spectrum can be identified by its mass-to-charge ratio corresponding to the proton-transfer product (MH+). However, H3O+ ionization, and associated chemistry in the ion molecule reactor, is known to generate other product ions besides the proton-transfer product. The product ion distributions (PIDs) created during ionization include ions resulting from charge transfer reactions, water clustering, and fragmentation, all of which can create ambiguity when interpreting PTR-MS mass spectra. A standardized method of evaluating and quantifying the possible influence of PIDs on PTR-MS mass spectra is limited in part due to an incomplete understanding of the formation mechanisms and effects of instrument settings on measured PIDs, as well as the reasons for instrument-to-instrument variability. We present a method, using gas-chromatography pre-separation, for quantifying PIDs from PTR-MS measurements of nearly 100 VOCs of different functional types including alcohols, ketones, aldehydes, acids, aromatics, halogens, and alkenes. Using this method we highlight major contributions of water cluster and fragment product ions to the PIDs of oxygenated VOCs. We characterize the influence of ion-molecule reactor conditions, ion transmission effects from quadrupole and ion optic tuning, and inlet capillary configuration on measured PIDs. We find that reactor conditions have the strongest impact on measured PIDs, but ion optic voltage differences and inlet capillary configuration can also affect PIDs. Through an interlaboratory comparison of PIDs measured from calibration cylinders we characterize the variability of PID production from the same model of PTR-MS across seven participating laboratories. A subset of VOCs measured by the different laboratories had standard deviations (1 σ) associated with product ions that varied no more than 20 % thus providing a constraint for predicting PIDs across instruments operating under different conditions. We highlight the potential for misidentification of VOCs in PTR-MS mass spectra with a case study measurement of restroom air. We propose methods for identifying likely product ions and constraining the influence of PIDs on PTR-MS mass spectra. Finally, we present a library of H3O+ PIDs, from measurements acquired as part of this study, to be publicly available and updated periodically with user-provided data for the continued investigation into instrument-to-instrument variability of PIDs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".