Effect Of Sample Injection Volume On Non-Spectroscopic And Spectroscopic Interferences In Inductively Coupled Plasma Mass Spectrometry
Bibliographic record
Abstract
Non-spectroscopic (also called matrix effects) and spectroscopic interferences (in particular, from oxide and doubly-charged ions) may compromise the accuracy of inductively coupled plasma mass spectrometry measurements.Dilution is widely used to reduce the matrix effects that depend on the absolute quantity of matrix.However, dilution is a source of errors (especially when performed manually) and takes considerable time.An alternative method of reducing the absolute quantity of matrix is proposed in this article, through a reduction in sample injection volume.In this study, capillary-based mono-segmented flow analysis (MSFA) with sample injection as small as 1 µL was compared to the continuous nebulization of sample solutions and flow injection of 50-µL aliquots.The injection volume and oxide interference had a positive correlation, with 1-µL MSFA reducing the amount of CeO + /Ce + by up to 69%.The concurrent increase in Ba ++ /Ba + as the sample volume decreased suggests an increase in plasma temperature when smaller sample volumes are introduced.Signal suppression induced by the 400-mg L -1 Na matrix significantly decreased as the volume of the injected sample decreased and was virtually eliminated with 1-µL MSFA.This decrease translated into a negative correlation between the sample volume and accuracy when a drinking water-certified reference material was analyzed by external calibration without internal standardization or matrix matching.Only 1-µL MSFA yielded concentrations within the range of inclusion for all analytes.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".