A method for assessing nanomaterial dispersion quality based on principal component analysis of particle size distribution data Author links open overlay panel--颗粒学报PARTICUOLOGY

Tantra, R., Oksel, C., Robinson, K. N., Sikora, A., Wang, X. Z., & Wilkins, T. A. (2015). A method for assessing nanomaterial dispersion quality based on principal component analysis of particle size distribution data. Particuology, 22, 30-38. https://doi.org/10.1016/j.partic.2014.10.004

A method for assessing nanomaterial dispersion quality based on principal component analysis of particle size distribution data Author links open overlay panel

R. Tantra ^a *, C. Oksel ^b, K.N. Robinson ^a, A. Sikora ^a, X.Z. Wang ^b, T.A. Wilkins ^b

a National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, UK
b Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK

10.1016/j.partic.2014.10.004

Volume 22, October 2015, Pages 30-38

Received 28 April 2014, Revised 20 October 2014, Accepted 22 October 2014, Available online 9 January 2015, Version of Record 4 August 2015.

E-mail: ratna.tantra@npl.co.uk

Highlights

• PCA was used to assess nanomaterial dispersion quality.

• The quality of dispersions from four different protocols was compared.

• The effects of variables within a protocol were investigated.

• Particle concentration was found to have the most influence on variability.

Abstract

Seemingly contradictory findings between studies are a major issue in nanoecotoxicological research and have been explained as a result of the lack of comparability between assay methods, with dispersion of nanomaterials being identified as a key factor. Here we show the use of a multivariate method, principal component analysis (PCA), as a tool in protocol development and categorization of dispersion quality. Results show the significance of particle concentration within a protocol, and its effect on repeatability. Our results suggest that future studies should involve the use of PCA as a powerful data exploration tool to facilitate method development, comparability and integration of data across different laboratories.

Graphical abstract

Keywords

Nanomaterial characterization; Principal component analysis; Nanotoxicology; Dispersion; Particle size distribution

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