Online Standard Additions Calibration of Transient Signals for Inductively Coupled Plasma Mass Spectrometry
Bibliographic record
Abstract
An online standard additions calibration method for transient signals in ICPMS is demonstrated in which a small volume of standard is injected as a spike into the sample/carrier stream, overlaying the analyte peak. This technique provides the advantages of conventional standard additions but requires only a single sample run. The method corrects for matrix effects and is suitable for transient signals in which the severity of the matrix effect changes over the analyte peak. The method uses a peak-fitting program to determine the area of the underlying peak and is shown to be effective for the determination of trace metal concentrations in both a high ionic strength matrix and in a biological matrix (urine). Eight analytes with concentrations in the range of 0.82-233.2 mug L-1 in urine were simultaneously determined using a standard spiking solution of 75 mug L-1 injected through a 100-muL loop. The measured concentrations for analytes free of spectral interferences agreed with the certified values, and the precision achieved was comparable to that achieved by the certifying agency. Using a conventional cross-flow nebulizer and Scott-type spray chamber, the accuracy obtained for online standard additions calibration was within 2%, and the precision was within 5%.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".