Introduction

Characterization procedures for nanoparticles often include, but are not limited to, size, shape, surface structure (or texture), and surface chemistry. These measurements, combined with phase information, such as crystalline phase, constitute the morphology of the material. This document focuses on two attributes of morphology, size and shape distributions, for discrete, agglomerated and aggregated nano-objects (materials with at least one dimension in the nanoscale, 1 nm < a length dimension < 100 nm). Transmission electron microscopy, a standard tool for measurements on the nanoscale, provides two-dimensional images of particle projections. This generic workflow for measuring and evaluating particle size and shape distributions on the nanoscale includes sample preparation, instrument factors, image capture, particle analysis, data analysis, and reporting. Seven case studies have been included to illustrate how the generic protocol can be applied to different particle morphologies and sample types. Three discrete particle test samples are reported: spheroidal (gold nanospheres), a bimodal mixture of particle sizes (colloidal silicas), and a mixture of particle shapes (gold nanorods and gold nanocubes). Two aggregate test samples are reported: amorphous aciniform aggregates (carbon black) and aggregates of primary crystallites (titania). Measurements methods are also presented for low aspect ratio samples and nanoparticles with specific crystal habits. Several of the case studies are supported by interlaboratory collaborations conducted under the guidelines of the Versailles Project on Advanced Materials and Standards (VAMAS) for interlaboratory comparisons (ILCs)^[⁴²^].

Three types of size and shape descriptors are considered. Size descriptors include those determined by linear or areal measurements. Shape descriptors include elongational descriptors, such as ratios of two length descriptors, and ruggedness descriptors, which represent surface irregularities.

The protocol emphasizes qualitative and quantitative analysis of data quality by the user. Qualitative comparisons of datasets include determining the similarity or differences between single descriptor means or multivariate means. Quantitative comparisons of datasets are based on difference or similarities between the parameters of reference models fitted to descriptor distributions. At least two parameters (mean and spread) and their uncertainties are needed to define a descriptor distribution. In some cases, these two quantitative parameters and their uncertainties may not be sufficient for characterization of particle size and shape distributions. Data visualization techniques, such as residual deviation and quantile plots, and data correlations, such as pairs of size and shape descriptors or fractal analysis, can provide additional ways to evaluate and differentiate test samples. Taken together, qualitative and quantitative quality metrics plus visualization and correlation tools permit users to tailor the protocol to their qualitative and quantitative quality targets.

BS EN ISO 21363:2022

Nanotechnologies. Measurements of particle size and shape distributions by transmission electron microscopy

Current

Published:

31 May 2022