TEMAS: A Flexible Non‐AI Algorithm for Metrology of Single‐Core and Core‐Shell Nanoparticles from TEM Images

An essential application of electron microscopy is to provide feedback tune the fabrication nanoparticles (NPs). Real samples tend follow a size distribution commonly linked synthesis process used and in turn their functional properties. This study presents an algorithm for measuring particle distributions images. State-of-the-art methods based on Artificial Intelligence (e.g., Deep Learning) require extensive datasets labeled images similar those expected be analyzed, supervised re-training often required cross-domain application. In contrast, non-AI described this accurate can quickly set up new experimental different domains. The accuracy method validated quantitatively comparing graphical descriptive statistics. Different are measured platinum gold nanocatalysts supported carbon black, amorphous carbon, titanium dioxide crystals. Also, platinum-iron core-shell NPs thin film successfully analyzed. limitation evaluating algorithms metrology lack standards that researchers use as ground truth. order overcome limitation, truth measurements presented here shared open dataset.