Dynamics and enzymatic degradation of exopolymer particles under increasing concentrations of silver ions and nanoparticles during a marine mesocosm experiment
Bibliographic record
Abstract
Pollution of the marine environment is an emerging threat. Nowadays, engineered nanoparticles (<100 nm) such as zinc, copper and silver are widely used as antimicrobial agents, therefore often present in daily-life products. Consequently, the demand and production of nanoparticles are expected to increase. Here, we specifically focus on silver nanoparticles (AgNP). Once released into the environment, AgNPs pose an obvious ecotoxicological risk, potentially affecting ecosystem structure and functioning. For instance, phytoplankton-derived exudates, rich in acidic polysaccharides and amino acids, can abiotically aggregate into microgels such as transparent exopolymer particles (TEP) and Coomassie stainable particles (CSP). Hence, microgels can bridge dissolved and particulate size fractions and facilitate aggregate formation with organic and mineral particles. Both physical and chemical properties make TEP and CSP attractive nutrient hotspots for heterotrophic bacterioplankton. Bacteria, in turn, utilize extracellular enzymes to access these carbon and nitrogen pools. However, knowledge about the mechanisms by which AgNPs might interact with and affect the biogeochemical cycling of TEP and CSP is still insufficient. Therefore, we conducted a mesocosm experiment in the Eastern Mediterranean Sea and investigated the effects of environmentally relevant concentrations of silver ions (Ag+) and AgNP on the properties of TEP and CSP (i.e., area and abundance) along with enzymatic activity measurements. Our results showed that cyanobacteria were likely the primary source of CSP in the ultra-oligotrophic Mediterranean Sea. Also, CSP contributed more to the microgel pool than TEP, as indicated by a strong relationship between CSP and heterotrophic microbial dynamics. While silver (i.e., Ag+ or AgNP) had overall only marginal effects, both species affected the relationships between cell-specific LAPase activity and CSP and cell-specific APase activity and phosphate levels. Thus, Ag+ and AgNP have the potential to regulate microgel dynamics. However, future studies are needed to derive a robust understanding of the effects of silver pollution on the coupling of microgel formation and degradation and the follow-on effect on biogeochemical cycles.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".