Use of Ar–He mixed gas plasmas for furnace atomisation plasma ionisation mass spectrometry (FAPIMS)
Bibliographic record
Abstract
The effect of Ar–He mixed gas plasmas on analyte signal intensities generated in a furnace atomization plasma ionization mass spectrometry (FAPIMS) source is presented. Analyte is introduced as a volatile headspace gas effluent (I2(g) and Hg(g)) and in discrete liquid sample volumes (Fe, Rb, Pd, In, Cs, Yb, Pt, Pb and Bi). The presence of as little as 2–10% (v/v) Ar increases the signal intensity of analytes having first ionization potentials greater than 6 eV by up to 10-fold. This may be attributed to the formation of more energetic plasmas whose electron density, ionization temperature and gas kinetic temperatures increase with Ar content. Consistent with this, elements with the highest ionization potentials showed the greatest enhancements. Analytes with ionization potentials of less than 6 eV, which are already presumed 100% ionized, are unaffected. Further increases in Ar content (50–100% v/v) can lead to plasma instability and accelerated erosion of graphite surfaces within the source. Most of the analytes studied showed slight improvements in the limits of detection for plasmas containing ≈5% v/v Ar; the increased signal intensity was accompanied by little or no increase in the background signal. Although Ar can significantly affect conditions within the source, the composition of the Ar–He mixture can also influence the transmission of analyte into the free jet expansion and to the MS detector. The appearance and intensity of Ar-based spectral interferences, such as ArC+, ArO+ and Ar2+, increase with Ar content and source temperature, degrading the determination of species such as 52Cr+, 56Fe+ and 80Se+. The amount of Ar required to generate significant signal enhancements is small (<10% v/v) and therefore does not significantly ease the pumping requirements on the interface.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".