journal article·2019-06

AutoEM: a software for automated acquisition and analysis of nanoparticles

Toni Uusimaeki, Thorsten Wagner, Hans-Gerd Lipinski, Ralf Kaegi

Journal of Nanoparticle Research· Volume 21· Issue 6
Publisher page

Abstract

The era of research on (engineered) nanomaterials (NM) has been thriving for more than a decade and has delivered many beneficial applications, but also raised concerns about potential negative impacts on human health and ecosystems. The precautionary principle, hence, calls for a regulation of certain types of NM, which in consequence requires their unambiguous identification. Most of the currently available definitions of NM rely on an evaluation of the size of the constituent particles and therefore methods have to be developed to measure this parameter. Transmission electron microscopy (TEM) is one of the most promising techniques, as its resolving power well covers the nanosize range. However, limited automation of TEM analyses and possible user bias are major drawbacks of the technique and currently put severe constraints on its broader applications in nanometrology. Therefore, the goal of this study was to develop a software code, referred to as AutoEM, to automatically acquire TEM images, measure particle sizes and extract the respective particle size distributions (PSD) of (nano)-materials. The AutoEM software also incorporates methods for elemental analyses of individual particles using electron energy loss and energy dispersive X-ray spectroscopy (EELS/EDX) allowing the extraction of element-specific PSDs. Additionally automated acquisition of energy-filtered images (EFTEM) is implemented in the AutoEM software, which can be used, e.g., to derive thickness maps and, thus, to evaluate the thickness of individual (plate-like) particles.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Nanoparticles: synthesis and applications · Nanoparticle-Based Drug Delivery · Electron and X-Ray Spectroscopy Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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