Spatial profiling of ion distributions in a nitrogen–argon plasma in inductively coupled plasma mass spectrometry
Bibliographic record
Abstract
Spatial profiling was used to investigate the effect of nitrogen on non-spectroscopic interferences (matrix effects) in inductively coupled plasma mass spectrometry. Nitrogen was introduced as 5.9% of the outer gas flow. Sixteen elements and several analyte oxides and doubly charged ions were monitored in the absence and presence of 0.01 M Na and 0.01 M K matrices. While matrix-induced enhancement was seen for most analytes in the Ar plasma, the effect was greatly reduced in the mixed N2–Ar plasma, albeit with a sacrifice in sensitivity (which is a small price to pay for the freedom from non-spectroscopic interferences). Analyte oxides were also reduced by over an order of magnitude. A comparison of the analyte profiles with those of background ions suggests that electron-impact ionization is the predominant ionization mechanism in the Ar plasma whereas, in the mixed-gas plasma, charge-transfer with Ar+ was suggested by the close match between the Ar2+ profiles and those of the analytes. The identical radial profiles of background polyatomic ions also suggest that Ar-containing ions, including the Ar dimer, all have Ar+ as the precursor ion in the Ar plasma. In contrast, the different profiles observed for these ions compared with Ar2+ in the mixed-gas plasma suggest that these ions (but obviously not Ar2+) more likely originate from the combination of neutral Ar with an ion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".