Extracting Metallic Nanoparticles from Soils for Quantitative Analysis: Method Development Using Engineered Silver Nanoparticles and SP-ICP-MS
Bibliographic record
Abstract
The lack of an efficient and standardized method to disperse soil particles and quantitatively subsample the nanoparticulate fraction for characterization analyses is hindering progress in assessing the fate and toxicity of metallic engineered nanomaterials in the soil environment. This study investigates various soil extraction and extract preparation techniques for their ability to remove nanoparticulate Ag from a field soil amended with biosolids contaminated with engineered silver nanoparticles (AgNPs), while presenting a suitable suspension for quantitative single-particle inductively coupled plasma mass spectroscopy (SP-ICP-MS) analysis. Extraction parameters investigated included reagent type (water, NaNO 3, KNO 3, tetrasodium pyrophosphate (TSPP), tetramethylammonium hydroxide (TMAH)), soil-to-reagent ratio, homogenization techniques as well as procedures commonly used to separate nanoparticles from larger colloids prior to analysis (filtration, centrifugation, and sedimentation). We assessed the efficacy of the extraction procedure by testing for the occurrence of potential procedural artifacts (dissolution, agglomeration) using a dissolved/particulate Ag mass ratio and by monitoring the amount of Ag mass in discrete particles. The optimal method employed 2.5 mM TSPP used in a 1:100 (m/v) soil-to-reagent ratio, with ultrasonication to enhance particle dispersion and sedimentation to settle out the micrometer-sized particles. A spiked-sample recovery analysis shows that 96% ± 2% of the total Ag mass added as engineered AgNP is recovered, which includes the recovery of 84.1% of the particles added, while particle recovery in a spiked method blank is ∼100%, indicating that both the extraction and settling procedure have a minimal effect on driving transformation processes. A soil dilution experiment showed that the method extracted a consistent proportion of nanoparticulate Ag (9.2% ± 1.4% of the total Ag) in samples containing 100%, 50%, 25%, and 10% portions of the AgNP-contaminated test soil. The nanoparticulate Ag extracted by this method represents the upper limit of the potentially dispersible nanoparticulate fraction, thus providing a benchmark with which to make quantitative comparisons, while presenting a suspension suitable for a myriad of other characterization analyses.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".