Carbon disulfide as a dopant in photon‐induced chemical ionization mass spectrometry
Bibliographic record
Abstract
RATIONALE: The addition of a dopant to an Atmospheric Pressure PhotoIonization (APPI) source of a mass spectrometer has been shown to enhance the degree of analyte ionization. A series of different dopants has been successfully utilized; however, there has been very little published on the characteristics of a good dopant. We have proposed carbon disulfide (CS2) as a novel new dopant based on its absorption cross-section for the VUV photon's energy used and its unique gas-phase ion chemistry, notably the fact that it does not contain a proton. METHODS: The ability of CS2 to enhance the ionization effectiveness of APPI was tested by using a group of compounds that have different proton affinities (PAs) and electron affinities (EAs). These results were compared to results obtained using the commonly used dopants, toluene and anisole. Particular attention was paid to the formation of [M](+) ions relative to [M+H](+) ions. Mass spectra were collected using a Waters Quattro Premier liquid chromatography/tandem mass spectrometry (LC/MS/MS) system equipped with a commercial Photomate™ photoionization source. RESULTS: The results show that CS2 increases the ionization efficiency of most of the analytes studied in this work comparably to toluene and anisole. CS2 promotes both ionization routes of [M](+) and [M+H](+). In addition, due to the higher ionization energy (IE) of CS2 (10.01) compared to the IEs of toluene (8.83) and anisole (8.20), CS2 can enhance the ionization efficiency of analytes that cannot be enhanced with toluene and anisole. CONCLUSIONS: We have determined that CS2 is a viable dopant for use in APPI sources. For some analytes, significant [M+H](+) ion signals are observed; therefore, the donated proton must come from either water clusters or solvents. In addition, CS2 promotes the ionization of analytes with low PAs and higher IEs than that of toluene and anisole.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".