Engineered nanomaterials and human health: Part 2. Applications and nanotoxicology (IUPAC Technical Report)
Bibliographic record
Abstract
Abstract Research on engineered nanomaterials (ENM) has progressed rapidly from the very early stages of studying their unique, size-dependent physicochemical properties and commercial exploration to the development of products that influence our everyday lives. We have previously reviewed various methods for synthesis, surface functionalization, and analytical characterization of ENM in a publication titled ‘Engineered Nanomaterials: Preparation, Functionalization and Characterization’. In this second, inter-linked document, we first provide an overview of important applications of ENM in products relevant to human healthcare and consumer goods, such as food, textiles, and cosmetics. We then highlight the challenges for the design and development of new ENM for bio-applications, particularly in the rapidly developing nanomedicine sector. The second part of this document is dedicated to nanotoxicology studies of ENM in consumer products. We describe the various biological targets where toxicity may occur, summarize the four nanotoxicology principles, and discuss the need for careful consideration of the biodistribution, degradation, and elimination routes of nanosized materials before they can be safely used. Finally, we review expert opinions on the risk, regulation, and ethical aspects of using engineered nanomaterials in applications that may have direct or indirect impact on human health or our environment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".