Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 449

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Surface area is the biologically most effective dose metric for acute nanoparticle toxicity in the lung

Open access
Schmid, Otmar, Stoeger, Tobias·2016·Journal of Aerosol Science

Abstract

In this study we provide guidance on the biologically most relevant dose metric for pulmonary toxicity of biopersistent, spherical nanoparticles (NPs). A retrospective analysis of nine in vivo studies on particle-induced, acute pulmonary toxicity in animal models (mouse, rat) was performed encompassing five different types of nanomaterials (polystyrene, titanium dioxide, carbonaceous materials, transition metal oxides (Co, Ni, Zn) and hydrothermally synthesized α-quartz) with a wide range of primary particle diameters (9–535 nm) and mass-specific BET surface areas (6–800 m2/g). The acute influx of polymorphonuclear neutrophils (PMNs) into the lungs after intratracheal instillation of NPs was chosen as a toxicological endpoint for acute lung inflammation. The allometrically scaled toxicological data were investigated with respect to various dose metrics, namely (primary) particle number, joint length, BET and geometric surface area, volume and mass. Surface area is identified as the biologically most relevant dose metric for spherical NPs explaining about 80% of the observed variability in acute pulmonary toxicity (R2=0.77). None of the other dose metrics explains more than 50% of the observed variability in pulmonary inflammation. Moreover, using surface area as the dose metric allows identification of material-based toxicity classes independent of particle size. Typical materials without intrinsic toxicity – here referred to as low-solubility, low-toxicity (LSLT) materials – show low surface-specific toxicity with an EC50 dose of 175 m2/g-lung (geometric mean; σg=2.2), where EC50 represents the dose inducing 50% of the maximum effect (here 30% PMN). In contrast, transition metal oxides (here Co, Ni, Zn) – materials known for their intrinsic toxicity – display a 12-fold enhanced surface-specific toxicity compared to LSLT particles (EC50=15 m2/g-lung). This analysis implies that surface-related modes of action are driving acute pulmonary toxicity for the types of NPs investigated here. The relevance of other dose metrics such as number and volume is acknowledged in the context of different modes of action, namely shape-induced toxicity (fiber paradigm) and extremely high particle lung burden (overload conditions), respectively. So which dose metric should be monitored by aerosol scientists involved in health related aerosol exposure measurements? The short answer is – all of them (except length), but there is a strong preference towards surface area.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.jaerosci.2015.12.006

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Cited by

See all 449

Metadata

Authors
Schmid, O., & Stoeger, T.
Venue
Journal of Aerosol Science
Publisher
Elsevier BV
Published
2016-09
Volume
99
Pages
133-143
ISSN
0021-8502 (ISSN-L)
OpenAlex
W2343635104
Cited by
449
References
4
FWCI
15.13
Type
journal article
Language
en
Open access
hybrid

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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Surface area is the biologically most effective dose metric for acute nanoparticle toxicity in the lung. https://app.cochat.ai/papers/10.1016/j.jaerosci.2015.12.006
The paper
Schmid, O., & Stoeger, T. (2016). Surface area is the biologically most effective dose metric for acute nanoparticle toxicity in the lung. Journal of Aerosol Science. https://doi.org/10.1016/j.jaerosci.2015.12.006

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.