Extraction of Silicon-Containing Nanoparticles from an Agricultural Soil for Analysis by Single Particle Sector Field and Time-of-Flight Inductively Coupled Plasma Mass Spectrometry
Bibliographic record
Abstract
The increased use of silica and silicon-containing nanoparticles (Si-NP) in agricultural applications has stimulated interest in determining their potential migration in the environment and their uptake by living organisms. Understanding the fate and behavior of Si-NPs will require their accurate analysis and characterization in very complex environmental matrices. In this study, we investigated Si-NP analysis in soil using single-particle ICP-MS. A magnetic sector instrument was operated at medium resolution to overcome the impact of polyatomic interferences (e.g., 14N14N and 12C16O) on 28Si determinations. Consequently, a size detection limit of 29 ± 3 nm (diameter of spherical SiO2 NP) was achieved in Milli-Q water. Si-NP were extracted from agricultural soil using several extractants, including Ca(NO3)2, Mg(NO3)2, BaCl2, NaNO3, Na4P2O7, fulvic acid (FA) and Na2H2EDTA. The best extraction efficiency was found for Na4P2O7, for which the size distribution of Si-NP in the leachates was well preserved for at least one month. On the other hand, Ca(NO3)2, Mg(NO3)2 and BaCl2 were relatively less effective and generally led to particle agglomeration. A time-of-flight ICP-MS was also used to examine the nature of the extracted Si-NP on a single-particle basis. Aluminosilicates accounted for the greatest number of extracted NP (~46%), followed by NP where Si was the only detected metal (presumably SiO2, ~30%).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".