Ion exchange technique (IET) to characterise Ag+ exposure in soil extracts contaminated with engineered silver nanoparticles
Bibliographic record
Abstract
Environmental context Biosolid-amended soils are likely sinks for manufactured silver nanoparticles, the environmental toxicity of which is believed to be related to the release and accumulation of Ag+ ions. This study demonstrates how an ion exchange technique can be applied to soil extracts to provide Ag+ measurements at low, environmentally relevant levels. The technique is a valuable addition to existing analytical methods for tracking the behaviour of Ag nanoparticles and Ag+ ions in the terrestrial environment. Abstract The lack of silver speciation exposure data in toxicity studies investigating the effects of manufactured silver nanoparticles (AgNPs) in natural soil media limits the ability to discern nano-specific effects from effects of the toxic Ag+ form, which may be released from the manufactured AgNPs contained in wastewater, biosolids or soil environment. Using samples containing Ag+ or mixtures of Ag+ and AgNPs, ranging in total Ag concentrations of 10–5 to 10–9 M, and prepared in de-ionised water and filtered soil extracts, the validity of the ion exchange technique (IET) to quantify Ag+ was investigated by comparing measurements to those of an Ag+ ion selective electrode (ISE) and to the dissolved fraction from single particle inductively coupled plasma–mass spectrometry (SP-ICP-MS) analysis (SP-dissolved). When analysing samples in the filtered soil extract, IET and ISE gave comparable results down to 10–7 M, below which Ag+ activities were below the ISE detection limit. For water samples, SP-dissolved values were generally comparable or slightly greater (on average 65 %) compared with IET-Ag+ at all concentrations. The high bias was likely due to inclusion of unresolved particles below the SP-ICP detection limit of 19 nm. However, when analysing samples in the soil extract, SP-dissolved values were on average eight-fold greater than IET-Ag+, highlighting the effect that natural colloidal and dissolved soil constituents have on complexing Ag+, as well as the lack of specificity of the SP-dissolved analysis for the Ag+ species. IET is shown here to be a valid procedure to quantify Ag+ activity in soil extracts, and while the study highlights the limitations of using the SP-dissolved fraction to estimate this biologically relevant Ag fraction, it shows that combined, IET and SP-ICP-MS provide a valuable approach for investigating the behaviour of manufactured AgNPs in different matrixes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".