Quantitation of Trace Metals in Liquid Samples by Dried-Droplet Laser Ablation Inductively Coupled Plasma Mass Spectrometry
Bibliographic record
Abstract
A new, discrete sample introduction approach based on laser ablation (LA) is described for the quantitation of several trace metals in aqueous samples by ICPMS. Dried microdroplets of sample, previously mixed with a sodium acetate matrix, were quantitatively ablated from a polystyrene substrate. Calibration via the method of standard additions or isotope dilution provided accurate results for Ni, Cd, and Pb in drinking water and Se in a yeast extract. Compared to conventional solution nebulization, LA sample introduction provided a 2-7-fold enhancement in absolute sensitivity and transport efficiency of 2-14% for the elements examined. Estimated detection limits are 1-7-fold poorer for the dried-droplet LA technique, primarily a result of degraded precision arising from counting statistics limitations for discrete sample introduction. On the basis of the several-second half-width of the resulting transient signals, sample throughput can be in the range of 250 samples per hour. Additionally, integration of the transient signal should eliminate contributions to elemental fractionation from the LA step. Dried-droplet LA-ICPMS offers several advantages over its counterpart, ETV-ICPMS, with respect to background intensity, throughput, and ease of desorption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".