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Analytical methods to assess nanoparticle toxicity
Abstract
During the past 20 years, improvements in nanoscale materials synthesis and characterization have given scientists great control over the fabrication of materials with features between 1 and 100 nm, unlocking many unique size-dependent properties and, thus, promising many new and/or improved technologies. Recent years have found the integration of such materials into commercial goods; a current estimate suggests there are over 800 nanoparticle-containing consumer products (The Project on Emerging Nanotechnologies Consumer Products Inventory, , accessed Oct. 2008), accounting for 147 billion USD in products in 2007 (Nanomaterials state of the market Q3 2008: stealth success, broad impact, Lux Research Inc., New York, NY, 2008). Despite this increase in the prevalence of engineered nanomaterials, there is little known about their potential impacts on environmental health and safety. The field of nanotoxicology has formed in response to this lack of information and resulted in a flurry of research studies. Nanotoxicology relies on many analytical methods for the characterization of nanomaterials as well as their impacts on in vitro and in vivo function. This review provides a critical overview of these techniques from the perspective of an analytical chemist, and is intended to be used as a reference for scientists interested in conducting nanotoxicological research as well as those interested in nanotoxicological assay development.
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Metadata
- Authors
- Marquis, B. J., Love, S. A., Braun, K. L., & Haynes, C. L.
- Venue
- The Analyst
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2009
- Volume
- 134
- Issue
- 3
- Pages
- 425
- ISSN
- 0003-2654 (ISSN-L)
- 1364-5528
- OpenAlex
- W2156406953
- Cited by
- 406
- References
- 180
- FWCI
- 17.71
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.