Nanoplastic Contamination in Freshwater Biofilms Using Gel Permeation Chromatography and Plasmonic Nanogold Sensor Approaches
Bibliographic record
Abstract
The worldwide contamination of aquatic ecosystems by plastics is raising concern, including their potential impacts on the base of the food chain, which has been poorly documented. This study sought to examine, for the first time, the presence of nanoplastics (NPs) in biofilms from freshwater streams/rivers. They were collected at selected polluted sites, such as the industrial sector for plastic recycling and production, miscellaneous industries, agriculture, municipal wastewaters/effluents and road runoffs. In parallel, the functional properties of sampled biofilms were determined by proteins, lipids, esterase (lipase), viscosity and oxidative stress. The results revealed that biofilms collected at the plastic industries and road runoffs contained the highest NP levels based on size exclusion chromatography, fluorescence detection and a new nanogold sensor visualization method. Examination of the chromatographic elution profiles showed increased abundance and size of NPs in the 10-150 nm size range at the polluted sites. Biofilms from the plastic industry site had elevated levels of aldehydes (oxidative stress) and lipids compared to the other sites. Biofilms collected at the municipal sites had elevated levels of proteins and esterases/lipases, with a decrease in total lipids. Biofilms collected at agriculture sites had the lowest levels of NPs in this campaign, but more samples would be needed to confirm these trends. In conclusion, biofilms represent an important sink for plastics in freshwater environments and display signs of distress upon oxidative stress.
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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) | 0.001 | 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 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".