Utilizing Low Sample Uptake Rates and A Nitrogen Mixed-gas Plasma for the Elimination of Oxide-based Interferences in ICPMS Analyses
Bibliographic record
Abstract
Spectroscopic interferences have long negatively impacted the accuracy of inductively coupled plasma mass spectrometry (ICPMS) analyses.Of these, oxide-based interferences, the combination of an analyte with oxygen producing a new ion 16 mass units greater than the original analyte, often proves most prevalent and difficult.A cheap and reliable method that permits the mitigation of oxide-based interference would be highly beneficial.Here-in, low sample uptake rate was used to reduce the formation of lanthanide oxide-based interferences in ICPMS analyses through temperature and Le Châtelier effects.Introduction of oxide forming solutions (50 μg L -1 ) composed of lanthanide elements at 1 mL/min yielded an average oxide ratio of 4.5 ± 7.2% while introduction at 50 μL L min -1 yielded 0.54 ± 0.26%.A similar method using 2% nitrogen gas in the bulk plasma concurrently decreased oxide-based interferences.The benefits observed with low sample uptake rate and a mixed-gas plasma were combined to virtually eliminate oxide based-interferences for many of the lanthanide elements and provide a modest signal enhancement compared to an Ar plasma operated at a higher sample uptake rate.For example, when comparing the best oxide reduction method to the worst, oxide formation is mitigated by 97%.Of the three sample uptake rates tested, 235 μL min -1 under mixed-gas plasma conditions offers the best balance between the oxide interferences mitigation and signal intensity.Ultimately, low sample uptake rate may prove essential in increasing ICPMS analysis accuracy while safeguarding resources and minimizing chemical waste for generations to come.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".