Engineered nanomaterials and human health: Part 1. Preparation, functionalization and characterization (IUPAC Technical Report)
Bibliographic record
Abstract
Abstract Nanotechnology is a rapidly evolving field, as evidenced by the large number of publications on the synthesis, characterization, and biological/environmental effects of new nano-sized materials. The unique, size-dependent properties of nanomaterials have been exploited in a diverse range of applications and in many examples of nano-enabled consumer products. In this account we focus on Engineered Nanomaterials (ENM), a class of deliberately designed and constructed nano-sized materials. Due to the large volume of publications, we separated the preparation and characterisation of ENM from applications and toxicity into two interconnected documents. Part 1 summarizes nanomaterial terminology and provides an overview of the best practices for their preparation, surface functionalization, and analytical characterization. Part 2 (this issue, Pure Appl. Chem. 2018; 90(8): 1325–1356) focuses on ENM that are used in products that are expected to come in close contact with consumers. It reviews nanomaterials used in therapeutics, diagnostics, and consumer goods and summarizes current nanotoxicology challenges and the current state of nanomaterial regulation, providing insight on the growing public debate on whether the environmental and social costs of nanotechnology outweigh its potential benefits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".