Jörg Radnik 1, Vasile-Dan Hodoroaba 1, Harald Jungnickel 2, Jutta Tentschert 2, Andreas Luch 2, Vanessa Sogne 3, Florian Meier 3, Loïc Burr 4, David Schmid 4, Christoph Schlager 5, Tae Hyun Yoon 6,7, Ruud Peters 8, Sophie M. Briffa 9 and Eugenia Valsami-Jones 9
1 Division 6.1, Federal Institute for Material Testing and Research (BAM), Unter den Eichen 44–46, 12203 Berlin, Germany;
2 Department of Chemical & Product Safety, German Federal Institute for Risk Assessment, Max-Dohrn-Strasse 8–10, 10589 Berlin, Germany;
3 Postnova Analytics GmbH, Rankine-Strasse 1, 86899 Landsberg, Germany;
4 Centre Suisse d’Electronique et de Microtechnique (CSEM), Bahnhofstrasse 1, 7302 Landquart, Switzerland;
5 Vitrocell Systems GmbH, Fabrik Sonntag 3, 78193 Waldkirch, Germany; email@example.com
6 Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea;
7 Institute of Next Generation Material Design, Hanyang University, Seoul 04673, Korea
8 Wageningen Food Safety Research, Wageningen University & Research, Akkermaalsbos 2, 6708 Wageningen, The Netherlands;
9 School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
The aim of this publication is to discuss how and when automated preparation can enhance the quality of the measurement results. For modern apparatuses, the measurement conditions are recorded and saved in the metadata automatically. As a result, the main reason for varying results is the different sample preparation. An actual interlaboratory comparison is ongoing to investigate the effect of different preparation methods systematically for ToF-SIMS. An air–liquid interface was developed to show that automation is possible for rather complex samples. Biological samples can be prepared in a reproducible manner, under exactly the same conditions on a TEM grid for the analysis of size and shape. Furthermore, chemical analysis can be performed by means of mass spectrometry.
Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by automation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been developed, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air–liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data.