Aquatic toxicity of manufactured nanomaterials: challenges and recommendations for future toxicity testing
Bibliographic record
Abstract
Environmental context The increased use of nanomaterials in industrial and consumer products requires robust strategies to identify risks when they are released into the environment. Aquatic toxicologists are beginning to possess a clearer understanding of the chemical and physical properties of nanomaterials in solution, and which of the properties potentially affect the health of aquatic organisms. This review highlights the main challenges encountered in aquatic nanotoxicity testing, provides recommendations for overcoming these challenges, and discusses recent studies that have advanced our understanding of the toxicity of three important OECD nanomaterials, titanium dioxide, zinc oxide and silver nanomaterials. Abstract Aquatic nanotoxicologists and ecotoxicologists have begun to identify the unique properties of the nanomaterials (NMs) that potentially affect the health of wildlife. In this review the scientific aims are to discuss the main challenges nanotoxicologists currently face in aquatic toxicity testing, including the transformations of NMs in aquatic test media (dissolution, aggregation and small molecule interactions), and modes of NM interference (optical interference, adsorption to assay components and generation of reactive oxygen species) on common toxicity assays. Three of the major OECD (Organisation for Economic Co-operation and Development) priority materials, titanium dioxide (TiO2), zinc oxide (ZnO) and silver (Ag) NMs, studied recently by the Natural Sciences and Engineering Research Council of Canada (NSERC), National Research Council of Canada (NRC) and the Business Development Bank of Canada (BDC) Nanotechnology Initiative (NNBNI), a Canadian consortium, have been identified to cause both bulk effect, dissolution-based (i.e. free metal), or NM-specific toxicity in aquatic organisms. TiO2 NMs are most toxic to algae, with toxicity being NM size-dependent and principally associated with binding of the materials to the organism. Conversely, dissolution of Zn and Ag NMs and the subsequent release of their ionic metal counterparts appear to represent the primary mode of toxicity to aquatic organisms for these NMs. In recent years, our understanding of the toxicological properties of these specific OECD relevant materials has increased significantly. Specifically, researchers have begun to alter their experimental design to identify the different behaviour of these materials as colloids and, by introducing appropriate controls and NM characterisation, aquatic nanotoxicologists are now beginning to possess a clearer understanding of the chemical and physical properties of these materials in solution, and how these materials may interact with organisms. Arming nanotoxicologists with this understanding, combined with knowledge of the physics, chemistry and biology of these materials is essential for maintaining the accuracy of all future toxicological assessments.
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 | 0.030 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".