Fullerenes Influence the Toxicity of Organic Micro-Contaminants to River Biofilms
Bibliographic record
Abstract
Organic micro-contaminants (OMC) enter in freshwaters and interact with other contaminants such as carbon nanoparticles, becoming a problem of unknown consequences for river ecosystems. Carbon nanoparticles (as fullerenes C60) are good adsorbents of organic contaminants and their interaction can potentially affect their toxicity to river biofilms. We tested the C60 interactions with selected OMCs and their effects on river biofilms in different short-term experiments. In these, river biofilms were exposed to C60 and three OMC (triclosan, diuron or venlafaxine) and their respective mixtures with fullerenes (C60 + each OMC). The effects were evaluated on structural, molecular and functional descriptors of river biofilms. Our results showed that C60 did not cause toxic effects in river biofilms whereas diuron and triclosan significantly affected the heterotrophic and phototrophic components of biofilms and venlafaxine affected only the phototrophic component. The joint exposure of C60 with venlafaxine was not producing differences with respect to the former response of the toxicant, but the overall response was antagonistic (i.e., decreased toxicity) with diuron, and synergistic (i.e., increased toxicity) with triclosan. We suggest that differences in the toxic responses could be related to the respective molecular structure of each OMC, to the concentration proportion between OMC and C60, and to the possible competition between C60-pollutants on blocking the receptors of the biological cell membranes. We conclude that the presence of C60 at low concentrations modified the toxicity of OMC to river biofilms. These interactions should therefore be considered when predicting toxicity of OMC in river ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".