Effects of silver nanoparticles on soil enzyme activities with and without added organic matter
Bibliographic record
Abstract
The effects of silver nanoparticles (AgNPs) on terrestrial ecosystems need to be better understood and assessed. Cationic silver (Ag+) has well-documented toxicity against bacteria, but it is not clear what will be the effect of nanoscale Ag. In the present study, the potential effects of AgNPs were investigated in soils by measuring activity of the enzymes phosphomonoesterase, arylsulfatase, β-D-glucosidase, and leucine-aminopeptidase. The toxicity of AgNPs was compared with that of ionic Ag, and the ameliorating effects of soil organic matter were evaluated. To this end, 2 soils with different organic matter contents were artificially contaminated with either AgNPs or Ag-acetate at equivalent total Ag concentrations. In general, enzyme activities were inhibited as a function of the Ag concentration in the soil. In the AgNP exposures, only a small fraction of the AgNP was actually truly dissolved (found in the <1-nm fraction), suggesting that the particulate forms of AgNPs resulted in a significant inhibition of soil enzymes. The addition of organic matter to the soils appeared to enhance enzyme activities; however, the mechanism of organic matter action is not clear given that dissolved Ag concentrations were similar in both the organic-matter–amended and unamended soils. The present study shows that the AgNP produces significant negative effects on the soil enzyme activities tested. The Ag chemical speciation measurements suggested that the AgNP caused greater toxic effects to the soil enzymes at the low Ag concentrations. For the larger concentrations of total soil Ag, causes of the negative effects on enzyme activities are less obvious but suggest that colloidal forms of Ag play a role.
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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) | 0.001 | 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".