Effect of Biofouling on the Sorption of Organic Contaminants by Microplastics
Bibliographic record
Abstract
Microplastics in the aquatic environment are susceptible to colonization by surrounding microorganisms, which form biofilms over the microplastic's surface. These biofilm-laden microplastics can then interact with a diverse array of contaminants. In the present study, biofilms were grown on microplastics in a laboratory setting using Pseudomonas aeruginosa as a model biofilm-forming bacterium for periods of 5 to 15 days. The sorption of three organic compounds representing different levels of hydrophobicity, namely methylene blue (MB), phenanthrol, and phenanthrene, was used to evaluate the effect of biofilm biomass on the adsorption of organic contaminants to microplastics. The sorption of MB and phenanthrol was found to increase with biofouling time, indicating affinity between these contaminants and the biofilm biomass on the particle. However, the presence of a biofilm did not influence the sorption of phenanthrene on the microplastics. These results suggest that the hydrophobicity of organic contaminants plays a major role in how biofouling of microplastics will influence contaminant sorption by microplastics. For some contaminants, biofilm can enhance the role of microplastics as contaminant vectors. These findings emphasize the need to understand the biomass load on environmental microplastics and the contaminants that associate with it for an accurate representation of the risk associated with microplastics in the environment. Environ Toxicol Chem 2024;43:1973-1981. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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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) | 0.003 | 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".