Amendment of Agricultural Soil with Metal Nanoparticles: Effects on Soil Enzyme Activity and Microbial Community Composition
Bibliographic record
Abstract
Several types of engineered nanoparticles (ENPs) are being considered for direct application to soils to reduce the application and degradation of pesticides, provide micronutrients, control pathogens, and increase crop yields. This study examined the effects of different metal ENPs and their dissolved ions on the microbial community composition and enzyme activity of agricultural soil amended with biosolids. The activity of five extracellular nutrient-cycling enzymes was measured in biosolid-amended soils treated with different concentrations (1, 10, or 100 mg ENP/kg soil) of silver (nAg), zinc oxide (nZnO), copper oxide (nCuO), or titanium dioxide (nTiO 2 ) nanoparticles and their ions over a 30-day period. At 30 days, nZnO and nCuO either had no significant effect on soil enzyme activity or enhanced enzyme activity. In contrast, Ag inhibited selected enzymes when dosed in particulate or dissolved form (at 100 mg/kg). nTiO 2 either had no significant effect or slightly decreased enzyme activity. Illumina MiSeq sequencing of microbial communities indicated a shift in soil microbial community composition upon exposure to high doses of metal ions or nAg and negligible shift in the presence of nTiO 2 . Some taxa responded differently to nAg and Ag + . This work shows how metal ENPs can impact soil enzyme activity and microbial community composition upon introduction into soils amended with biosolids, depending on their type, concentration, and dissolution behavior, hence providing much needed information for the sustainable application of nanotechnology in agriculture.
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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".