Engineered nanomaterials and human health: Part 1. Preparation, functionalization and characterization (IUPAC Technical Report)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 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 it