Biofilm formation by marine bacteria is impacted by concentration and surface functionalization of polystyrene nanoparticles in a species‐specific manner
Bibliographic record
Abstract
The world's oceans are becoming increasingly polluted by plastic waste. In the marine environment, larger plastic pieces may degrade into nanoscale (<100 nm in at least one dimension) plastic particles due to natural weathering effects. We observe that the presence of 20 nm plastic nanoparticles at concentrations below 200 ppm had no impact on planktonic growth of a panel of heterotrophic marine bacteria. However, the presence of plastic nanoparticles significantly impacted the formation of biofilms in a species-specific manner. While carboxylated nanoparticles increased the amount of biofilm formed by several species, amidine-functionalized nanoparticles decreased the amount of biofilm of many but not all bacteria. Further experiments suggested that the aggregation dynamics of bacteria and nanoparticles were strongly impacted by the surface properties of the nanoparticles. The community structure of an artificially constructed community of marine bacteria was significantly altered by exposure to plastic nanoparticles, with differently functionalized nanoparticles selecting for unique and reproducible community abundance patterns. These results suggest that surface properties and concentration of plastic nanoparticles, as well as species interactions, are important factors determining how plastic nanoparticles impact biofilm formation by marine bacteria.
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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) | 0.001 | 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".