Single Particle-Inductively Coupled Plasma Mass Spectroscopy Analysis of Metallic Nanoparticles in Environmental Samples with Large Dissolved Analyte Fractions
Bibliographic record
Abstract
There is an increasing interest to use single particle-inductively coupled plasma mass spectroscopy (SP-ICPMS) to help quantify exposure to engineered nanoparticles, and their transformation products, released into the environment. Hindering the use of this analytical technique for environmental samples is the presence of high levels of dissolved analyte which impedes resolution of the particle signal from the dissolved. While sample dilution is often necessary to achieve the low analyte concentrations necessary for SP-ICPMS analysis, and to reduce the occurrence of matrix effects on the analyte signal, it is used here to also reduce the dissolved signal relative to the particulate, while maintaining a matrix chemistry that promotes particle stability. We propose a simple, systematic dilution series approach where by the first dilution is used to quantify the dissolved analyte, the second is used to optimize the particle signal, and the third is used as an analytical quality control. Using simple suspensions of well characterized Au and Ag nanoparticles spiked with the dissolved analyte form, as well as suspensions of complex environmental media (i.e., extracts from soils previously contaminated with engineered silver nanoparticles), we show how this dilution series technique improves resolution of the particle signal which in turn improves the accuracy of particle counts, quantification of particulate mass and determination of particle size. The technique proposed here is meant to offer a systematic and reproducible approach to the SP-ICPMS analysis of environmental samples and improve the quality and consistency of data generated from this relatively new analytical tool.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".