Gaining meaningful statistical data on TEM specimens through automated nanoparticle workflow (APW)

Session

Poster Session 3

Authors

Dr. Anil Yalcin (1)

Affiliations

1. Thermo Fisher Scientific

Keywords

Statistics, Automated Nanoparticle Workflow (APW), Food Additives, Precipitate Analysis, Catalysis

Abstract text

While S/TEM imaging methods and characterization techniques give insights on specimens, acquired images intrinsically represent a limited area (due to S/TEMs being high resolution instruments) with scarce statistical input. When nanoscale statistical data is required, this is achieved through manually acquiring multiple S/TEM (spectrum) images and processing them individually, which is not an ideal workflow. Moreover, sample size is kept small (around 50 particles) due to the extent of manual operation in image acquisition and processing. 

One field requiring nanoscale statistical data is the food industry, where European Food Safety Authority (EFSA) recommended that titanium dioxide nanoparticle additives should include more statistical information (besides the current EU specifications) acquired through electron microscopy techniques [1]. EFSA recommendation has been adopted already by several research institutes. A recent publication proposes a method for quantification and size distribution of titanium dioxide nanoparticles in confectionery by means of S/TEM [2].

To eliminate manual involvement in (spectrum) image acquisition and processing, Thermo Fisher has developed an automated nanoparticle workflow (APW). With clever communication between microscope optics and stage, large area (spectrum) imaging can now be carried out in high resolution in an automated way on S/TEM. Moreover, thanks to the integrated energy dispersive x-ray spectroscopy (EDS) detectors, one can simultaneously conduct elemental analysis and acquire STEM images during APW workflow. Acquired data is processed on-the-fly, enabling immediate access to statistics and considerably reducing time to data. With the whole workflow being automated, large sample sizes can be achieved, ensuring more reliable and meaningful statistics.

As electron microscopy techniques are nowadays generating large datasets, APW will help users retrieve meaningful information on their specimens in an unattended way without any additional time for data processing. We believe that besides the food industry, APW will address numerous research fields/industries where sample statistics are of great significance, such as metals (precipitate analysis) and catalysis (correlation between particle size and surface area).

References

[1] Younes, M. et al. EFSA Journal, 2019, 17, 5760.

[2] Geiss, O. et al. Food Control, 2021, 120, 107550.