Comparison of laser ablation, electrothermal vaporization and solution nebulization for the determination of radionuclides in liquid samples by inductively coupled plasma mass spectrometry
Bibliographic record
Abstract
The performance of solution nebulization (SN), electrothermal vaporization (ETV) and laser ablation (LA) of dried micro-droplets as sample introduction systems for ICP-MS are compared for the determination of several radionuclides in digested biological tissue, nearshore sea-water and river water. Samples were subjected to a Ca3(PO4)2 co-precipitation preconcentration prior to analysis. Each introduction system possesses unique advantages and disadvantages. ETV accommodates samples having higher dissolved solids content; consequently, for SN and LA, sample concentrates require additional dilution by 50- and 10-fold, respectively. ETV and LA achieved similar sensitivities and limits of detection, the latter ranging from 0.017 to 0.029 pg ml−1. Although SN provides the best precision (2% RSD versus 7 and 8% for ETV and LA, respectively), formation of uranium hydride can be reduced at least 100-fold using LA of dried micro-droplets of samples. The accuracy of the method was validated by determination of U and Th in NIST SRM 1566b Oyster Tissue, NRC CRM CASS-4 Nearshore Seawater and SLRS-4 Riverine Water and via spike recoveries for Pu.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".