Optimization of the technique of standard additions for inductively coupled plasma mass spectrometry
Bibliographic record
Abstract
The use of standard additions in the presence of instrument drift and the optimum size of the added spike relative to the unknown analyte concentrations have been investigated for ICP-MS. In particular, a bracket approach, where the spiked sample is measured between two different measurements of the sample, has been investigated. The average of the two sample measurements is used in the standard additions formula to estimate concentration. Several multi-element analyte solutions with single element matrices (Na, Cs, Ba), were analyzed using both the bracket approach and regular standard additions. It was found that the bracket approach led to better results where drift was significant. In addition, optimum spike size was investigated. Simple models predict that determinations would be more precise with larger spikes if the instrument response was linear and RSD was constant. These results show that while the use of larger spikes (from 7 to 50× the unknown concentration) did not yield the better precision predicted by the models, the precision was no worse than for spikes of size equal to the unknown concentration. The Autonomous Instrument project is an approach to the automation of ICP-MS based on choosing an appropriate analytical calibration methodology for an unknown sample. The method of standard additions is the most accurate analytical methodology considered by the Autonomous Instrument. These results have implications for the Autonomous Instrument, suggesting that bracket standard additions should be considered the best method, followed by regular standard additions. In addition, the spike size results imply that in automatic determination, the long linear range of ICP-MS allows the addition of large analyte spikes with minimal prior knowledge of the sample.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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".